Part I - Theoretical and Methodological Foundations in Digital Media Research and Adolescent Mental Health

1 Methodological and Conceptual Issues in Digital Media Research

Kaveri Subrahmanyam and Minas Michikyan

Decades of research on adolescence has demonstrated that contexts such as families, peer groups, schools, and neighborhoods play an important role in adolescent development (Petersen, 1993; Steinberg & Morris, 2001). To these well-accepted contextual influences, we should add media – both mass media (e.g., television, films, and music) as well as new digital media, which include the Internet (e.g., websites, online forums and communities, and video- and image- sharing platforms), communication applications/platforms (e.g., social media and messaging apps), and electronic games. Survey data suggest that digital media have become ubiquitous in young people’s lives (Anderson & Jiang, 2018; Rideout & Robb, 2019); of particular note is that a majority of US adolescents now have access to a computer or smartphone, with 95% reporting access to a smartphone and 45% reporting that they are online almost all the time (Anderson & Jiang, 2018).

Research to date suggests that adolescents primarily use digital media for information, communication, and entertainment, with peer interaction and communication becoming especially popular (Anderson & Jiang, 2018; Subrahmanyam & Greenfield, 2008; Valkenburg & Peter, 2011). In prior work, we have suggested that new digital worlds should be considered an important developmental context during adolescence (Subrahmanyam & Šmahel, 2011; Subrahmanyam et al., 2006). Not only are digital media an important social context, but they have also become instrumental in adolescents’ interactions with other key contexts such as friends and families. Relationships with friends and families are predictive of health and well-being during adolescence (Moore et al., 2018) and it is important to understand the impact of youths’ digital media use on their psychological well-being and mental health.¹ This handbook brings together the multidisciplinary scholarship on adolescent social media use and mental health, and critically evaluates the extant research to provide a blueprint for future research. In this introductory chapter, we first provide an overview of the definitions and terminology related to digital media and social media, an overview of adolescents’ digital media use at the time of this writing, and a brief historical account of the study of adolescents’ social media use. In the second part of the chapter, we describe some of the key methodological and conceptual issues pertaining to adolescent digital media use research.

Overview of Definitions and Terminology

When reviewing the literature on new digital media, one sees a confusing array of terminology and labels with little consistency in how the terms are used. Thus, it is important to begin by defining the terminology that will be used in this chapter and more generally in this handbook. At the most general level, the term mass media is used to refer to legacy media forms such as television, films, and music, where the communication is “one-to-many”; in other words, a media producer creates the content, which is then consumed by many people, often using specific hardware (e.g., television set, boombox, record player). In contrast to mass media are new media, also called interactive media, screen media, or digital media, the term used in the title of this book. Although there are many definitions of interactive media, we adopt the one proposed by England and Finney (2002) that states: “interactive media is the integration of digital media including combinations of electronic text, graphics, moving images, and sound, into a structured digital computerised environment that allows people to interact with the data for appropriate purposes” (p. 2). There are two key elements to note in this definition of interactive media – first is that the user interacts with the electronic data to construct and co-construct the content; second, the digital environment includes a variety of hardware components (e.g., computers, mobile devices, smartphones) and software platforms (operating systems, internet browsers, and specialized applications/apps).

The term digital media is a broad umbrella term for a variety of media forms including electronic/video games, online messaging, social media, and other digital communication applications/digital tools. Social media are the primary focus of this book and we adopt the definition put forth by Carr and Hayes (2015): “Social media are Internet-based channels that allow users to opportunistically interact and selectively self-present, either in real-time or asynchronously, with both broad and narrow audiences who derive value from user-generated content and the perception of interaction with others” (p. 51). As clarified by Nesi, Prinstein, and Telzer (see the Introduction to this volume), “We define this to include social networking sites (e.g., Instagram, Snapchat, WeChat, and Facebook), messaging tools (e.g., text messaging and messaging apps), online forums and communities, video- and image-sharing platforms (e.g., YouTube and TikTok), and video games with a social component.” In this chapter, we use the terms social media and digital media interchangeably.

Overview of Adolescents’ Digital Media Use

Adolescent respondents to the 2019 Common Sense Census reported an average of 7 hours and 22 minutes of daily screen use that was not for school or homework; furthermore, they reported spending 39% of their screen time watching TV/videos, 22% of their time on gaming, 16% on social media, and 8% browsing websites (Rideout & Robb, 2019). Among the adolescent respondents in the 2018 Pew report, YouTube (85%), Instagram (72%), and Snapchat (69%) were the most popular online platforms, and only 51% reported that they used Facebook (Anderson & Jiang, 2018). According to a 2021 Pew report, TikTok is gaining popularity among younger social media users – 55% of its users are between the ages of 18 and 24 (Auxier & Anderson, 2021. Overall, the survey data suggest that adolescents spend a considerable portion of their day with screen media.

History of the Study of Adolescents’ Social Media Use

In this section, we present a brief historical account of the study of adolescents’ use of digital media, including social media. The Internet as we know it has only been used widely by youth for about a decade and a half, and so it might seem strange to use a “historical lens” to describe research on it. Nonetheless, the lessons learned from examining the historical context and arc of the extant body of work can help researchers adapt to the changes in social media that are inevitable in the years to come. Research on youth digital media use has been conducted by scholars coming from a range of disciplinary traditions including psychology, communication studies, media studies, education, computer science, and human–computer interaction.

The disciplines of psychology and developmental psychology / developmental science were slow to recognize the growing importance of digital media in the lives of children and adolescents. Researchers who first worked in this area (Subrahmanyam & Manago, 2012) found it challenging to publish in mainstream journals in the field unless the papers were part of a special issue or a special collection (Greenfield et al., 2012; Greenfield & Yan, 2006; Michikyan & Suárez-Orozco, 2016; Subrahmanyam & Greenfield, 2008; Yan & Hardell, 2018). In fact, it was not until 2016 that the Society for Research in Child Development hosted its first special topics conference on the role of technology in child development. The constantly changing and fluid nature of the digital landscape presents unique methodological challenges to digital media researchers, and we address them in a later section. For ease of communication, we divide research on adolescents’ social media use into three phases. The first phase of research focuses on the use of the Internet and on early online communication contexts such as text-based chat rooms, bulletin boards, and blogs. The second phase investigates the first generation of digital media platforms, which were referred to as “social networking sites” in the literature. The third phase encompasses research on the social media platforms/applications that are in vogue at the time of writing this chapter.

The First Phase: Research on Internet Use and Early Online Communication Contexts

The Internet became available to the public in 1991, and we see survey reports and journal articles on youths’ internet use starting around the late 1990s and early 2000s (Finkelhor et al., 2000; Kraut et al., 1998; Roberts et al., 1999; Sanders et al., 2000; Stahl & Fritz, 2002; Subrahmanyam et al., 2001; Turow, 1999). Given the novelty of the Internet and that youth were among the early adopters of it, studies generally focused on two questions – what youth did online (Roberts et al., 1999; Turow, 1999) and how their internet use related to their safety and psychological well-being (Kraut et al., 1998; Sanders et al., 2000; Stahl & Fritz, 2002; Subrahmanyam et al., 2001). The majority of studies used self-report survey designs and showed that from the very beginning, youth who had access to the Internet used it for communication, with popular applications including email and chat rooms (Turow, 1999).

Each new media technology such as radio, film, and television has been greeted by concerns about its negative effects on youth (Wartella & Jennings, 2000; Wartella & Robb, 2009), and the Internet was no different. Early concerns centered on risky behaviors related to visiting problematic content (e.g., pornography), having contact with strangers, inappropriate/unsafe interactions (e.g., sexual solicitation, threatening or harassing contact) (e.g., Finkelhor et al., 2000; Stahl & Fritz, 2002), and psychological well-being (Kraut et al., 1998; Sanders et al., 2000). The latter concern stemmed from two related elements of youths’ internet use at that time – first, computer-mediated interactions were text-based, and users were disembodied, so they did not have access to face-to-face cues such as gaze, gestures, emotional tone, and body language. Thus, online interactions were perceived to be lower in quality. Additionally, internet use was not very diffuse and so youths’ online interactions mostly occurred with strangers and others from outside their offline social networks. Given these elements of youths’ online interactions, the concern was that lower quality online interactions with strangers were displacing/replacing higher quality face-to-face interactions with friends and acquaintances; thus, early scholarship examined the implications of adolescents’ internet use for social isolation, loneliness, and depression. A detailed description of this research is beyond the scope of this chapter, and the interested reader is referred to the papers above and a monograph on digital youth coauthored by the first author (Subrahmanyam & Šmahel, 2011).

This body of work suggested that the relation between internet use and psychological well-being was complex with contradictory results. For instance, the HomeNet study, a longitudinal field study conducted in Pittsburgh between 1995 and 1998 (Kraut et al., 1996, 1998) found that during the first two years of the study, increased time spent online was associated with declines in well-being (social involvement, loneliness, and depression). However, in the third year of the study, internet use was associated with smaller declines and even reversals (i.e., improvements) in well-being (Subrahmanyam et al., 2001). The HomeNet study was unique in that a diverse sample of 93 families (208 adults and 110 children and adolescents) were given computers and internet access in 1995. They were then surveyed on several measures and their online activities were automatically recorded whenever they went online. The seminal study was conducted at a time when people had little exposure to technology, and thus the researchers were able to get a detailed picture of youths’ online activities and well-being from their first exposure to this technology and for a short period of time thereafter. Despite the study’s contradictory findings, the appeal of online settings for youth was clear – a 16-year-old HomeNet participant declared, “I really want to move to Antarctica – I’d want my cat and Internet access and I’d be happy.” The HomeNet study was conducted in 1995 – not much seems to have changed in that regard since that time!

Other studies in the first phase of research were more qualitative and focused on obtaining a rich picture of what adolescents and emerging adults (college students) were doing online (Greenfield & Subrahmanyam, 2003; Turkle, 1995a). Recall that youth were among the early adopters of the most common communication venues of that era, including internet relay chat rooms, multiuser dungeons (MUDs), and the commercially available chat rooms hosted by AOL, Yahoo, and instant messaging (also hosted by AOL). These spaces were significantly different from the social media apps were available when this chapter was written. They were text-based, accessed via computers and low-speed internet, and users were disembodied and largely anonymous (Subrahmanyam & Šmahel, 2011).

Adult researchers were not using these venues, and thus were unfamiliar with the text-based language and code that digital media users were constructing and coconstructing within them; using qualitative methods from a variety of disciplines including ethnography, participant observation, and discourse analysis, these researchers provided a rich picture of the structure, content, modes of online communication, and youth subculture that was emerging within new online venues such as MUDS, chat rooms, blogs, bulletin boards, and webpages (Greenfield & Subrahmanyam, 2003; Huffaker & Calvert, 2005; Šmahel & Subrahmanyam, 2007; Subrahmanyam et al., 2004; Suzuki & Calzo, 2004). These studies provided a window into adolescents’ emerging online lives and showed that youth used these spaces in the service of core developmental issues, including identity exploration, intimacy, health, and sexuality. Whereas youths’ online lives were psychologically connected to their offline counterparts, they were not mirror images of each other. Given the features of new online environments such as anonymity, disembodiedness, and lack of face-to-face cues, youths’ communication within them was often exaggerated and were observed with new intensities (Šmahel & Subrahmanyam, 2007; Subrahmanyam, 2007). For instance, within online chat rooms, there was one sexual comment per minute, and one obscene comment every two minutes (Subrahmanyam et al., 2006).

The Second and Third Phase: Research on Digital Communication Tools from Social Networking Sites to Social Media Apps

The next wave of online communication tools included the now defunct MySpace and Friendster, as well as Facebook. They were the first generation of social networking sites and were introduced in the early to mid-2000s. Social network sites were defined by boyd and Ellison (2007) as “web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system” (p. 211). For a detailed history of social networking sites, we refer the reader to boyd and Ellison’s 2007 article published in the special volume of the Journal of Computer-Mediated Communication, which was the first collection of research on social networking sites. Since then, there have been dramatic advances in hardware (e.g., smartphones and tablets), software, and internet access (e.g., high speed Wi-Fi), and the term “social media” has come to replace the term “social networking sites.” It is difficult to pinpoint who coined the term social media and when it began to be used in popular culture (Bercovici, 2010). As noted earlier, this handbook uses the term social media to refer to digital tools that can be used for social interaction and selective self-presentation.

Compared to the early text-based online communication tools, social networking sites and social media apps are multimodal and allow users to interact and communicate via text, images, audio, and video. They make it possible to have both private and public interactions, and they vary in the extent to which users are disembodied and anonymous (Subrahmanyam & Greenfield, 2008). As access to the Internet and mobile devices became widespread, users were also more likely to interact with people they knew from their offline lives. In contrast to the earlier generation of online venues, where youth primarily interacted with people they met online, research revealed that youth used social networking sites and then social media platforms/applications to interact and make plans with friends from their offline lives and to keep in touch with peers they were not able to meet in person (Pempek et al., 2009; Subrahmanyam et al., 2008). Social media platforms also allowed users to easily engage in self-expression and self-presentation via text and audiovisual content such as status updates, emojis, pictures and videos (Manago et al., 2008; Michikyan & Subrahmanyam, 2012).

As found in the first studies of online communication venues, youths’ offline and online social media lives were psychologically connected; online behaviors were again exaggerated, with youth reporting wider online networks (Manago et al., 2012). Given many adolescents’ 24/7 access to social media and, by extension, their immersion in elevated levels of peer interaction, self-disclosure, and self-presentation, research has examined the implications of social media use for the intrapersonal need for identity and the interpersonal need for intimacy as well as for psychological well-being. The chapters of this handbook will give the reader a detailed picture of the theoretical and empirical scholarship related to adolescents’ use of social media and their mental health. In the remaining part of this chapter, we draw on lessons learned from the extant research on youths’ use of social media to identify methodological challenges and conceptual issues pertaining to adolescent digital media use research.

Methodological Challenges in Adolescent Digital Media Research

New online contexts presented many unique challenges to researchers when they burst on the scene and adolescents flocked to them. Researchers have now become adept with technology and are social media users themselves; in fact, within their ranks are those who are referred to as digital natives – or individuals who have grown up with technology their entire lives (Prensky, 2001). Here we examine some of the key methodological challenges confronting researchers who seek to investigate the implications of youths’ digital media use. As before, we use a historical lens, as it helps to illustrate both the challenges that researchers faced and will continue to face.

Fluid Digital Media Landscape

The fluid nature of technology and rapid pace of change has always been an intrinsic element of the digital media landscape. During the first phase of digital media research, researchers not only had to contend with changes in hardware, but also in internet speed, software, and communication applications, and widening of users to include both strangers as well as friends and acquaintances. Additionally, there were constant shifts in the communication applications that were popular among adolescents at any given time, and changes in the features and elements within applications. MySpace, which was at one time a favored social networking site, was eventually supplanted by Facebook. There were also constant changes in elements of social media apps such as the top 8 list in MySpace or the Like button on Facebook. Rapid change in technology and rates of adoption also meant that there were changes in who was online. As noted earlier, initially youth mostly interacted online with strangers, including adults and peers who they did not know from their offline lives. Subsequently, as technology became more diffuse and widespread, more of their peers were online. Simultaneously there was an explosion in the popularity of more private and closed systems via social networking sites, within which people created profiles and chose who they interacted with and who could see the information they shared on their profiles. They were thus more likely to interact online with peers from their offline lives.

Perhaps the most challenging issue was how frequently changes occurred – there was often a lag between when a digital media platform emerged and gained popularity and when researchers began to investigate its use in earnest. In some cases, researchers found themselves investigating a platform that was no longer in vogue, as youth had moved on to the next new context that had appeared on the digital scene. This fluidity of digital platforms is particularly challenging for longitudinal studies as it complicates comparisons between different waves of data. Logistically, this meant that researchers had to focus broadly on a category of applications (e.g., chat rooms or social networking sites) and not target specific applications (e.g., AOL chat rooms, MySpace, or Facebook). This is also the approach adopted in this handbook. Given the fundamentally transient nature of digital platforms, even focusing on application categories does not ensure continued relevance after a platform’s eventual demise; so, we used a developmental lens for our early studies and focused on developmental tasks including identity, sexuality, and intimacy. Such a developmental approach ensures that study results are relevant long after the shelf life of a particular digital media platform or category of platforms/applications. An additional approach to ensure the continued relevance of research on an application is to focus on elements or features of digital platforms and the activities that they support. This issue also relates to conceptual considerations and is discussed in further detail in the latter part of this chapter.

A related methodological challenge that arose in early digital media studies was that each new platform had different communication features or capabilities. From the earliest studies of online communication, communication scholars interested in computer-mediated communication investigated how communicative cues in online settings shape interaction within them (Culnan & Markus, 1987; Walther, 1992). Subsequently, drawing from this body of work and Gibson’s notion of affordances in the context of object perception (Gibson, 1979), the term media affordances (Hutchby, 2001) has been used to refer to the qualities of different digital platforms, including mobile phones and social media (boyd, 2011; Ellison & Vitak, 2015; Reid & Reid, 2007; Subrahmanyam & Šmahel, 2011; Treem & Leonardi, 2013). Because different platforms have different affordances, it is important for researchers to be flexible and use different approaches when studying youths’ use of these technologies.

In our own work at the Children’s Digital Media Center @ Los Angeles, techniques from discourse analysis and participant observation were adapted to investigate how adolescent digital media users utilize the communication cues available in online chat rooms to construct and co-construct conversational coherence (Greenfield & Subrahmanyam, 2003; Subrahmanyam & Manago, 2012) in the service of key developmental tasks such as identity and sexuality (Subrahmanyam et al., 2004). These studies were qualitative in design and used a single chat transcript to analyze the online culture that adolescent digital media users were co-constructing. Subsequent studies utilized a combination of qualitative and quantitative analysis to examine a larger number of utterances in chat rooms as well posts on online blogs (Subrahmanyam et al., 2006, 2009). When social networking sites and text messaging became popular and youths’ digital communication occurred in private spaces, we shifted to self-report measures and adapted techniques from social network analysis (Reich et al., 2012; Subrahmanyam et al., 2008), mixed-method (Michikyan, 2019; Michikyan et al., 2015), and daily diary (Subrahmanyam et al., 2020) designs to better investigate the developmental implications of youths’ digital media use for their well-being. Thus, it is important for researchers investigating youths’ social media to be flexible and adapt their methodological and analytical approaches based on an analysis of the digital media platform’s affordances, how youth use it, and its potential mental health implications.

Measuring Digital Media Use

Researchers investigating youths’ digital media use must make decisions about how they measure usage and the research designs they adopt. These decisions have methodological as well as conceptual implications, and in this section, we address them from a methodological perspective. Using the “historical lens” that we have adopted heretofore in this chapter, we see that from the earliest studies of youths’ internet use and continuing into extant social media apps, amount of time spent online has been researchers’ favored measure of operationalizing social media usage. This was influenced by prior research on mass media such as television, and research on the first generation of electronic media, including computers and games, when computers were often in common spaces and shared among members of the family. Thus, it was reasonable that digital media users would be able to estimate the time they spent on average during a given period (day or week). With youths’ widespread access to mobile technologies and high-speed internet, the issue of time use has become considerably complicated. As with all retrospective self-report measures, internet time use measures are susceptible to inaccurate/distorted/biased estimates (Parry et al., 2021; Scharkow, 2016). An alternative way of obtaining an estimate is to use software to automatically record internet use, as in the HomeNet study; however, given that multitasking with multiple windows on a screen or with multiple devices is ubiquitous, it is important to distinguish between open/active windows and applications to which a user may or may not actually be paying attention.

Another approach to studying digital media use is by analyzing the actual content of digital communication. In fact, this was the method by which researchers analyzed conversation in the first generation of digital media platforms such as online teen chat rooms, blogs, and bulletin boards (Subrahmanyam et al., 2006, 2009; Suzuki & Calzo, 2004). These applications were publicly available and so accessing the content was relatively easy for researchers. As the digital landscape moved toward closed networks with private (e.g., private messaging on Facebook, direct messaging on Twitter, private messaging on smartphones) and public communication (e.g., Facebook wall, publicly available tweets), researchers deployed automatic means to capture the content of youths’ digital communication (Negriff, 2019; Underwood et al., 2012). Underwood et al. (2012) pioneered this technique by providing adolescent participants in a longitudinal study with BlackBerry devices and automatically recording text messages and other contents of their private communication. While this approach provides an unfiltered window into adolescents’ digital worlds, it is logistically challenging, as it provides a vast amount of data that then has to be analyzed by researchers, machine learning models, or a combination of the two (Dinakar et al., 2014). The biggest concern of this approach, of course, is that the analytical technique – whether human or machine – may impute intentions, emotions, biases, and motives that were not intended by the social media user.

While there is no easy remedy for the measurement challenges outlined above, some possible solutions are briefly described next. This is not intended to be an exhaustive list, but to provide a selective sampling to illustrate how to approach measurement of youths’ social media use. First, researchers should consider a mixed-methods design to capture users’ intentions; in one study on online self-presentation, we asked participants to describe a picture they posted, and their identity-related meaning making was coded and then quantitatively analyzed. Note that codes were based on participants’ own descriptions of the picture and their reasons for posting instead of the researchers trying to deconstruct the image and post (Michikyan et al., 2015). Second, we encourage researchers to consider daily diary designs and ecological momentary assessment techniques to get more accurate estimates of users’ social media use and activities over several days at a time. Daily diary studies have been used extensively in social psychology to study frequent everyday interactions (Bolger et al., 2003); at the end of each day, participants are asked to report on their interactions that day and about other variables such as well-being, conflict, etc. In the experience sampling method, participants are asked to self-report what they are doing, feeling, and thinking at random points during times they are awake (Larson & Csikszentmihalyi, 2014). In both methods, participants report about their social media use when it is fresh in their mind, helping to limit memory distortions that are more likely when asked to estimate or recall use and activities on average. Both designs have the added advantage of yielding multiple data points over time, which are essential to address key questions regarding the longer-term implications of social media use and for the examination of within-person effects (Gonzales, 2014; Jelenchick et al., 2013; Kross et al., 2013; Pouwels et al., 2021; Subrahmanyam et al., 2020).

Finding Equivalent Comparison Groups

A final methodological challenge stems from the widespread use of social media among adolescents. Typically, when studying the influence or impact of a variable, psychologists compare groups of people with varying levels of the variable in question. This is true whether the comparison group is naturally occurring in a correlational or descriptive study (e.g., coffee drinkers and nondrinkers, alcohol drinkers and nondrinkers, and video-game players and nonvideo-game players) or created by the experimenter’s manipulation in an experimental design. Because digital media have become ubiquitous in adolescents’ lives, it is virtually impossible to find a group of youth who do not use social media and are truly equivalent to a group of youth who use them, at least in the Global North. The lack of a naturally occurring control or comparison group is an intractable design challenge facing social media researchers, and correlational designs have dominated the literature to date. A few researchers have conducted clever experiments to test the effects of digital communication (Gross, 2009; Sherman et al., 2013, 2016; Vogel et al., 2015; Weinstein, 2017); given the dearth of such studies, there is an urgent need for more experimental designs to help unearth the mechanisms by which social media use shapes well-being.

Conceptual Considerations for Adolescent Digital Media Research

As the foregoing section demonstrates, the novelty, variability, and fluidity of the digital landscape presents methodological challenges for researchers investigating adolescents’ digital media use. This section discusses some of the conceptual issues that should guide research on the implications of adolescents’ social media use for their well-being.² Specifically, researchers examining youths’ digital media use must make decisions about how they conceptualize and operationalize digital media usage. The following three conceptual considerations can help to guide researchers as they make these decisions:

1. (1) Conceptualizing the role of digital media in adolescent development and well-being: digital media as a developmental context.

2. (2) Conceptualizing youths’ digital media usage: reimagining and operationalizing digital media use.

3. (3) Conceptualizing pathways between digital media usage and well-being: considering mediators and moderators.

Figure 1.1 presents a schematic of the conceptual considerations that researchers should keep in mind when studying adolescent digital media use and psychological well-being.

Figure 1.1 A schematic representation of conceptual considerations for digital media usage and mental health

Note: As indicated in the figure, intrapersonal needs and interpersonal needs drive adolescents’ motives of digital media usage and impact their choice of digital media platforms. The selection of specific digital media platform and its affordances shape adolescents’ use and motives as well as the levels and types of activities; these in turn influence the different mechanisms through which adolescents make meaning of their digital media use, impacting their psychological well-being and mental health. Individual factors as well as contextual factors both within and outside of the digital media context can influence digital media usage; only digital media-specific contextual factors (e.g., digital status seeking and positivity norm) are shown in the schematic.

Consider Digital Media as a Developmental Context

There is wide agreement in the literature that human behavior across developmental time can only be fully understood in context (Bronfenbrenner & Morris, 2006; Vygotsky, 1978). With regard to adolescent development, the role of contexts such as families, peer groups, schools, and neighborhoods has been well documented (Petersen, 1993; Steinberg & Morris, 2001), and as noted at the start of this chapter, digital media should be viewed as an important context in the lives of adolescents. Digital media broadly, and social media more specifically, are incredibly diverse, and a variety of applications are available, each with its own unique communication contexts and affordances. An important question is the extent to which researchers should focus on particular social media platforms in any study. As an example, consider the case of Twitter and Snapchat, which share similar features such as photo sharing, but also differ in their affordances and how they are used (Alhabash & Ma, 2017). From an affordances perspective (Treem & Leonardi, 2013), both social media platforms – Twitter and Snapchat – allow for visibility (i.e., the ability to make information about oneself, once fully or partly invisible, visible to others), editability (i.e., the ability to construct, reconstruct, and coconstruct the information intended to convey to others), and association (i.e., the ability to establish a relationship with others and with a specific content), but they differ in terms of persistence (i.e., the ability to access and review the information in its original form after the user has completed the communication or interaction). Empirical evidence also suggests that the specific digital media platform matters to the user in terms of how and why it is used (Alhabash & Ma, 2017; Madden et al., 2013; Utz et al., 2015); if it matters to the user, then it seems that it should matter to researchers’ conceptualizations of digital media both when investigating and disseminating their results.

At the same time, social media platforms are adopting similar features –Snapchat Stories vs. Instagram Stories vs. Facebook Stories. What was once unique to Snapchat – the limited time feature – is no longer the case, as Instagram and Facebook now afford the ability to automatically vanish videos and images. The most recent trends – similarities across social media platforms and the emergence of newer platforms like TikTok – push us to consider whether and how we need to distinguish between the various social media platforms that youth use when investigating implications for development and well-being. Given how quickly digital media platforms evolve and the variety of affordances and activities possible on each platform, it is challenging for researchers to remain consistent in their conceptualization as the platforms themselves change. Perhaps one way to reconcile this conundrum – a lack of consistency in conceptualizing and operationalizing specific digital media platforms – is to be consistent in the recognition and articulation of digital media platforms as unique developmental contexts with specific affordances through which users influence and are influenced by the context (Subrahmanyam & Greenfield, 2008; Subrahmanyam et al., 2006).

Researchers should also be mindful of both the contextual factors outside of the digital world (e.g., race, socioeconomic status, immigration generation status) as well as within the specific social media context (i.e., implicit and explicit norms and expectations shared among users within a specific digital media context) (De Choudhury et al., 2017; Elsaesser et al., 2021; Michikyan & Suárez-Orozco, 2017; Nesi & Prinstein, 2019; Valkenburg & Peter, 2007). For brevity, we only discuss contextual factors within social media, and provide two examples to illustrate the potential role of social media–specific contextual factors that could shape psychological well-being and mental health. One example is the positivity norm found within many social media platforms, where the unspoken expectation is for users to engage in a set of behaviors in an attempt to put their best “face” forward (Qui et al., 2012). Another example is digital status seeking – wherein users engage in specific behaviors to obtain peer status (Nesi & Prinstein, 2019). These examples illustrate that just as researchers take into account key features of particular developmental contexts such as peer groups and schools, they should take into account the contextual characteristics of the specific digital media platform studied – including its affordances and unique cultural elements (e.g., positivity norm), even when such contextual characteristics may not be analytical variables. Taken together, these examples suggest that consistency in the recognition and articulation of digital media can provide a more complete understanding of digital media usage in the service of development and mental health.

Reimagine Digital Media Usage

Another critical issue concerns the conceptualization and operationalization of digital media usage. Digital media usage has been conceptualized and operationalized in myriad ways (see Kross et al., 2020; Schønning et al., 2020, for a review). Extant research has mostly operationalized digital media usage in terms of the extent to which users engage in different activities via digital platforms, for example through self-presentation and self-disclosure using social media and the frequency and time spent on these activities (e.g., Gil-Or, 2015; Manago et al., 2008; Masur & Scharkow, 2016; Michikyan, 2019; Michikyan, Dennis, & Subrahmanyam, 2014a; Michikyan et al., 2015; Qui et al., 2012; Twomey & O’Reilly, 2017; Wright et al., 2018). In the next subsections, we discuss the different ways that digital media usage has been operationalized and make recommendations for how usage can be conceptualized to study adolescent digital media use more accurately and meaningfully.

Digital Screen Time: As noted earlier in the methodological challenges section, digital screen time is frequently used in investigations of youths’ digital media use, and as a retrospective self-report measure, it is susceptible to both over- and underreporting (Parry et al., 2021; Scharkow, 2016). Here we examine whether total digital screen time, on its own, is a meaningful and accurate measure of digital media usage as related to psychological well-being (see Meier & Gray, 2014; Orben & Przybylski, 2019a, for a similar argument). Scholars (e.g., Orben, 2020) have asked whether self-reported measures of digital screen time should be “retired.” The arguments for retiring self-reported measures of total digital screen time seem valid given that the size of the negative effects of digital media use on mental health is either nonexistent (Coyne et al., 2020) or too small to have a practical significance or to warrant a meaningful scientific debate (Orben & Przybylski, 2019b). In fact, some have argued that adolescent digital media users would need to spend a physically impossible amount of time using digital media – more than 63 hours per day – to experience noticeable decline in their well-being (see Orben & Przybylski, 2019a). While it might be premature to “retire” the concept of self-reported measures of digital screen time without more research, it is important to “reimagine” it. A more meaningful approach to conceptualizing and operationalizing digital screen time would be to combine – the amount of time + activity + specific time frame + motive of use – within a single item or question (e.g., “How much time did you spend today chatting with friends on Instagram to tell them about your problems and troubles?”). Doing so would increase the meaningfulness and accuracy of measuring digital screen time. Another possibility is to combine objective measures of screen time (e.g., via tracking apps) with objective measures of content or activity, and a subjective measure of motivation (see Subrahmanyam et al., 2020; Underwood et al., 2012). Future research should aim to tease apart the role of these different components – for instance, examining whether chatting with friends on Instagram about problems improves mental health when it happens for short durations of time, but undermines mental health when it happens for longer periods of time. Taken together, it appears that it might not the amount of digital screen time in and of itself that matters for psychological well-being, but rather, how adolescents use their time engaging with digital media.

Digital Activities: Social media platforms afford users a range of possible digital activities, and there is empirical evidence that different activities may differentially impact psychological well-being (Kross et al., 2020). For instance, digital activities that involve interacting with existing friends via text messaging can enhance well-being (Valkenburg & Peter, 2007), because certain features of text-based content may serve as “digital affiliative cues” that can facilitate emotional bonding (Sherman et al., 2013). Similarly, digital activities that involve visual cues or image-based content (e.g., photos) can also enhance well-being by decreasing loneliness and by increasing happiness and life satisfaction (Pittman & Reich, 2016), likely because photos can foster increased social connectedness (Bakhshi et al., 2014; Goh et al., 2009); it is worth noting that image-based content can also negatively impact well-being by increasing social comparisons, particularly those focused on appearance and body image (e.g., Lewallen & Behm-Morawitz, 2016).

Other aspects of digital media activities that are relevant in adolescent social media use research are the type of activity and level of interactivity – or exchanges between users and between users and specific digital media features (see Stromer-Galley, 2004, for a detailed argument). Research examining youths’ digital media usage and psychological well-being has focused on the distinction between two types of social activities: “active” use vs. “passive” use (Kross et al., 2020; Schønning et al., 2020); “active” use (e.g., commenting on or responding to someone else’s content) has been found to enhance well-being (Escobar-Viera et al., 2018; Liu et al., 2019), whereas “passive” use (e.g., reading comments or newsfeeds of hundreds of friends and followers without any participation or lurking) has been found to undermine psychological well-being (Escobar-Viera et al., 2018; Tandoc et al., 2015; Underwood & Ehrenreich, 2017).

Although the terms “active” and “passive” capture some aspects of social media use that are relevant to mental health and well-being, other aspects of use are not captured by these terms. Consider one example of self-presentation via posting photos, which is considered an “active” use of digital media (Wang et al., 2017). Missing from the current conceptualization of “active” digital media usage, however, is whether and how interactive “active” usage is. For instance, when a user posts a picture, does the self-presentation occur during an interaction with other users, or does the user engage in self-presentation outside of an interaction, as a result of an exchange between the user and specific digital media features, or some combination of both? The current conceptualization of “passive” digital media usage places significant emphasis on content consumption with minimum interactivity (e.g., lurking) (Underwood & Ehrenreich, 2017). Even with content consumption, “passive” activities such as viewing humorous or inspiring social media content might improve mood and well-being. An important question is how passive “passive” digital media usage is. Is reading someone else’s comments really passive?

This begs the question of what “active” use and “passive” use in the current paradigm really capture (Valkenburg et al., 2021). It appears that the active- vs. passive-use paradigm emphasizes the behavioral component of digital media usage, while mostly ignoring its cognitive and affective components. A focus on the behavioral component of digital media usage assumes that “passive” use (e.g., lurking) is psychologically passive. Drawing from Bandura that “a theory that denies that thoughts can regulate actions does not lend itself readily to the explanation of complex human behavior” (Bandura, 1986, p. 15), we reject the premise that digital media usage is passive. Even “passive” digital media usage wherein the user chooses to observe other users’ activities online without any participation (e.g., reading someone else’s comments) involves some level of psychological activity (e.g., encoding, interpretation, and processing) (see Sherman et al., 2018; see also Bandura, 1997). Instead, we propose that digital media usage be viewed on an interactivity continuum with exchanges between users and between users and specific digital media features (Stromer-Galley, 2004). In this conceptualization of digital media usage, digital activities are assumed to be inherently active, although the level of interactivity might vary. Drawing from the developmental literature on peer interactions (Rubin et al., 2006), the terms reciprocal activities and parallel activities may better capture the distinction between different kinds of social media activities that are relevant to mental health and well-being.

Reciprocal activities involve exchanges via digital media platforms wherein users can take turns to respond and react to one another (e.g., texting, chatting, commenting on another user’s post, liking another user’s photo, etc.). Parallel activities involve actions via digital media platforms that do not engage other users in a particular exchange, or actions that do not involve reciprocity or response from other users (posting a status, a comment, or a photo to express one’s thoughts and feelings, watching a video/film, etc.). Reciprocal and parallel activities vary across the interactivity continuum, with reciprocal activities at the high end of the interactivity continuum and parallel activities at the low end. Given the complexity of digital media usage, reciprocal activities and parallel activities may also be dynamic – influencing one another and at varying levels of intensity. For instance, a user can engage in both high or low levels of reciprocal and parallel activities simultaneously (e.g., texting while reading someone else’s comment, or posting more than one photo and chatting with one or more persons at the same time). This distinction is one possibility and not intended to comprehensively capture all aspects of digital/social media activities. Another distinction proposed in the research is between content creation, production, and consumption (see Schønning et al., 2020); content curation and/or content distribution are increasingly important aspects of social media use. The point here is that when considering social media usage, researchers must capture the nuances when conceptualizing digital activities. The continuum of parallel to reciprocal activities can help as it can capture both the level and the characteristic of digital media usage – whether and how actively the user is interacting with others online and/or whether and how actively the user is “interacting” with a digital media platform and with themselves.

Consider a Variety of Mechanisms and User Variables as Mediators and Moderators

As noted elsewhere in the literature (Beyens et al., 2020; Subrahmanyam et al., 2020) and in this handbook, extant research on the relation between adolescent digital media use and psychological well-being has revealed no clear or consistent patterns, suggesting that the relation is complex. Thus, it is important for researchers to also consider underlying mechanisms as well as individual factors that may shape the pathways between digital media usage and mental health.

Possible Mechanisms: Digital media usage has been linked with different mechanisms that can either enhance or undermine mental health and well-being (Escobar-Viera et al., 2018; Yoon et al., 2019). For instance, researchers have demonstrated that specific digital activities (e.g., reading someone else’s comments without any participation), on the one hand, can undermine well-being through upward social comparison (i.e., comparing oneself with someone better off than oneself), negative self-evaluation (Wang et al., 2017), and rumination (Feinstein et al., 2013), as well as through feelings of envy (Appel et al., 2015) and fear of missing out (Oberst et al., 2017; Przybylski et al., 2013). On the other hand, digital activities wherein users peruse their own photos on social media might also improve psychological well-being through self-affirmation (see Toma & Hancock, 2013) and perhaps through downward comparison (i.e., comparing oneself with someone worse off than oneself).

Depending on the type of self-comparison, social media users may experience decreases or increases in their mental health and well-being. Not only do people compare themselves with others, but they also compare their “current self” with their “past self” and generally view themselves as improving over the years, despite how illusory this view may be (M. Ross & Wilson, 2003). Applying this to digital media usage, it is to be expected that users who engage in downward comparison (i.e., viewing the current self as better than the past self when comparing recent online photos with the earlier photos) may experience increases in their mental health. Although it remains to be seen, the effects of downward comparison may be of even greater significance for adolescent digital media users who are undergoing the task of developing a personal fable (Elkind, 1967; Erikson, 1959; Granic et al., 2020a, 2020b).

Possible User Variables: Examining user variables that may moderate the pathway between social media usage and well-being can also yield more nuanced insights about the ways that digital media use can enhance or undermine mental health. As an example, we focus on one variable, personality, to illustrate why researchers should consider individual factors as moderators and mediators when studying the relation between digital media usage and psychological well-being and mental health (Ehrenberg et al., 2008; Kircaburun et al., 2020; Michikyan et al., 2015; C. Ross et al., 2009). Other potential moderators identified in prior research include age and gender (Booker et al., 2018; Correa et al., 2010; Simoncic et al., 2014), offline support (Hatchel et al., 2019), and social anxiety (Hatchel et al., 2018; Subrahmanyam et al., 2020).

Personality can be defined as a collection of generally stable characteristics that define the self across time and context (Zuckerman, 1991) – including traits such as extroversion, introversion, neuroticism, and openness to new experiences (Costa & McCrae, 2008). Social media users who are extroverted (e.g., outgoing, talkative) and who are open to new experiences (reflecting curiosity and novelty-seeking) appear to engage in self-enhancing digital activities (e.g., via posting selfies) (Sorokowska et al., 2016, Zywica & Danowski, 2008), which can further enhance their interpersonal skills and psychological well-being. However, even among extroverts, users who are also experiencing psychological well-being concerns (e.g., lower life satisfaction) may be prone to problematic social media use such as addiction (Nikbin et al., 2020).

Like their extroverted peers, introverted users who are shy or less outgoing and users who are moody (indicative of high neuroticism) also benefit from using social media (Simoncic et al., 2014); however, these groups of users appear to utilize social media to compensate for a lack of offline social networks and a lack of confidence in their interpersonal skills (Ehrenberg et al., 2008; C. Ross et al., 2009). Since neuroticism can be manifested as loneliness and anxiety (Cattell & Mead, 2008), it is also not uncommon for social media users with high trait neuroticism to engage in frequent parallel activities involving self-presentation (e.g., posting comments and photos) (C. Ross et al., 2009), which tend to be more elaborate (Bai et al., 2012), more negative (Kern et al., 2014), and more socially desirable and less truthful (Michikyan et al., 2014). It appears that the reluctance to engage other users via digital media might reflect social anxiety or the fear of being negatively evaluated by other users that is typically experienced by social media users with high trait neuroticism (Bowden-Green et al., 2021). A major complication in the search for user variables is that different individual factors may interact both with one another as well as with contextual factors, often in a nonlinear way. Thus, the various ways in which different individual factors and contextual factors as well as different mechanisms interact with one another should be considered when conceptualizing the multiple ways digital media usage impacts adolescents’ psychological well-being and mental health.

Conclusions

As digital media are now entrenched in the lives of adolescents, they have become an important contextual influence along the lines of families, peer groups, and schools. Considerable research demonstrates the importance of friends and families in adolescent health and well-being, and it is similarly important to investigate the impact of digital media on adolescent well-being and mental health. This introductory chapter presented an overview of the terms and history of research on this topic and described some of the pressing methodological and conceptual issues confronting researchers investigating this topic. Our discussion highlighted two main themes: (1) Changes in technology are inevitable, and thus researchers will need to be flexible in the methodological approaches they adopt to investigate the short- and long-term implications of youths’ social media use; (2) Researchers must clearly articulate how they conceptualize and operationalize digital media, its role, usage, and pathways of influence. We present a few ways that researchers can adapt to the methodological challenges and clarify how they should innovate when conceptualizing and measuring adolescents’ digital media use. These are but a few suggestions, and we encourage researchers to build and expand on them as they investigate the growing presence of social media in adolescents’ lives.

2 Theoretical Foundations of Social Media Uses and Effects

Patti M. Valkenburg

Empirical work into the cognitive, affective, and behavioral effects of media use started in the 1920s under the umbrella concept of mass communication. The term mass communication arose as a response to the new opportunities of reaching audiences via the mass media (e.g., film, radio; McQuail, 2010). In early mass communication theories, the mass did not only refer to the “massness” of the audience that media could reach, but also to homogenous media use and powerful media effects, notions that apply increasingly less to the contemporary media landscape (Valkenburg et al., 2016). In the past two decades, media use has undergone a rapid evolution. It has become increasingly individualized, and, with the introduction of social media, undeniably more dynamic and ubiquitous. It is no surprise, therefore, that communication and media effects theories have undergone important adjustments. And it is also no surprise that the mass has turned increasingly obsolete in contemporary media effects theories (Valkenburg & Oliver, 2019).

The aim of this chapter is to discuss the communication and media effects theories that may serve as the foundations for research into the effects of social media use on adolescents. To define social media, I follow the definition of Bayer et al. (2020, p. 472): Social media are “computer-mediated communication channels that allow users to engage in social interaction with broad and narrow audiences in real time or asynchronously.” Social media use thus entails the active (e.g., posting) or passive (e.g., browsing), private (one-to-one) or public (e.g., one-to-many), and synchronous or asynchronous usage of social media platforms, such as Instagram, Facebook, Snapchat, TikTok, WeChat, and WhatsApp.

The first section of this chapter focuses on three important paradigms of general media effects theories that may help us understand the effects of social media, namely the selectivity, transactionality, and conditionality paradigms. The second section reviews computer-mediated communication theories, which originated in the 1970s, and are still relevant to understand the effects of social media. The third section introduces a transactional affordance theory of social media uses, which is inspired by transactional theories of development (Bronfenbrenner, 2005; Sameroff, 2009), Self-effects theory (Valkenburg, 2017), and affordance theories of social media use (e.g., boyd, 2011; McFarland & Ployhart, 2015). A fourth and final section presents some avenues for future research into the effects of social media on adolescents.

Media Effects Theories

In this chapter, I define media effects as the deliberate and nondeliberate short- and long-term within-person changes in cognitions, emotions, attitudes, and behavior that result from media use (Valkenburg et al., 2016). And I define a (social) media effects theory as a theory that attempts to explain the uses and effects of (social) media use on individuals, groups, or societies as a whole (Valkenburg & Oliver, 2019). To be labeled a (social) media effects theory, a theory at least needs to conceptualize media use, and the potential changes that this use can bring about within individuals, groups, or societies (i.e., the media effect).

Over the past decades, dozens of media effects theories have been developed. These theories differ substantially in how they conceptualize the media effects process. Some theories, particularly the early ones, focus primarily on unidirectional linear relationships between media use and certain outcomes. Other, more comprehensive theories pay more attention to the interactive effects of media use and nonmedia factors (e.g., dispositions, social contexts) on certain outcomes. Valkenburg et al. (2016) argued that media effects theories can be organized along five paradigms that specify the conditions under which media effects can (or cannot) occur. This chapter discusses the three paradigms that are most relevant to our understanding of the effects of social media use, the selectivity, transactionality, and conditionality paradigm. The term “message” in this chapter refers to all textual, auditory, visual, and audiovisual content that is shared on social media.

The Selectivity Paradigm

The selectivity paradigm of media effects theories states that: (a) individuals can only attend to a limited number of media messages out of the wealth of media messages that can potentially attract their attention, (b) they select these media messages in response to dispositions, needs, and desires that differ from person to person, and (c) only those media messages they select have the potential to influence them. The selectivity paradigm is represented by two different communication theories: uses and gratifications theory (Katz et al., 1973) and selective exposure theory (Zillmann & Bryant, 1985). Both theories argue that a variety of cognitive and psychosocial factors guide and filter one’s selective media use. An important difference between the theories is that uses and gratifications theory conceives of media users as rational and conscious of their selective media use, whereas selective exposure theory argues that media users are often not aware, or at least not fully aware, of their selection motives.

The Transactionality Paradigm

The transactionality paradigm is an extension of the selectivity paradigm. Early studies into the selectivity paradigm have predominantly focused on the extent to which the dispositions of media users (e.g., needs, moods, attitudes) predict their tendency to select media. In other words, these studies conceptualized selective media use as the outcome, whereas the effects of this media use received less attention. In more recent transactional media effects theories (e.g., Slater, 2007; Valkenburg & Peter, 2013a), the selectivity paradigm has become an integrated part of the media effects process. Transactional media effect theories argue that (a) the media user, rather than the media, is the starting point of a process that leads to selective media use, (b) this selective media use may bring about a transaction (i.e., change) in the media user, which is the media effect, and (c) this media effect may, in turn, reciprocally influence media use and the antecedents of media use. For example, it has been shown that adolescents high in trait aggressiveness are more likely to selectively expose themselves to violent websites, which may further enhance their trait aggressiveness (Slater, 2003).

The propositions in transactional media effects theories have important implications for theories and research on the effects of social media. First, in comparison with mass media, social media have more filters and algorithms to cater to the preferences of adolescent users, which may stimulate their selective exposure to messages that match these preferences. Second, social media platforms typically allow adolescents to make their posts more personal, vivid, and emotional, which may enhance the likelihood of effects. Third, since 2017, adolescents can not only search for messages related to a specific hashtag but can also follow one or more hashtags, after which posts under these hashtags start to show up more prominently in the users’ timelines or feeds (Scherr et al., 2020). In comparison with mass media content, such posts may be more effective both in attracting the selective attention of recipients of these posts, and in influencing their cognitions, attitudes, and behavior (e.g., Parmelee & Roman, 2020).

Following transactional theories, social media use may thus result in selective exposure to messages that match with individuals’ preexisting dispositions (e.g., needs, moods, attitudes), more so than mass media use. These theories thus imply that social media users may also more than mass media users be able to shape their own media effects via this targeted selective social media use. Hence, if we want to understand the effects of social media use on adolescents, we may need to study the antecedents that shape their selective social media use. Selective exposure theories have mostly focused on dispositional antecedents, such as mood and preexisting attitudes. But according to Valkenburg & Peter’s (2013a) differential susceptibility to media effects model (DSMM), three types of antecedents may predict adolescents’ selective (social) media use and, thus, the effects of this use: dispositional, developmental, and social-context factors.

Dispositional Factors

Dispositions that may lead to selective social media use range from more stable factors (e.g., temperament, personality) to more transient and situational ones (e.g., needs, desires, moods). Both types of antecedents have received some support. For example, fear of missing out (FOMO, a more stable anxiety of missing out on rewarding experiences that others are having) has been linked to adolescents’ (problematic) social media use (Franchina et al., 2018). Furthermore, some (but not all) adolescents experiencing low mood turn to social media to look for funny clips or supportive feedback (Rideout & Fox, 2018).

Developmental Factors

As for development, research has shown that children and adolescents typically prefer media messages that are only moderately discrepant from their age-related comprehension schemata and level of psychosocial development (Valkenburg & Cantor, 2000). If they encounter media content that is too discrepant, they will allocate less attention to it or avoid it. This moderate-discrepancy hypothesis explains, for example: (a) why toddlers are typically attracted to audiovisual material with a slow pace, simple characters, and familiar contexts, and why they can be mesmerized by buttons on tablets; (b) why preschoolers typically like to attend to faster-paced, more adventurous contexts, and more sophisticated fantasy characters; (c) why children in middle childhood typically enjoy computer games and virtual worlds that allow collecting and saving, and identify with real-life idols; and (d) why adolescents are the most avid users of social media for interacting with their friends, and seek online entertainment that presents irreverent humor or risky behavior (for a more elaborate review of developmentally related media preferences, see Valkenburg and Piotrowski (2017).

Social Context Factors

Social context refers to the surroundings within which individuals or groups act or interact, and whose norms and affordances may influence the cognitions, emotions, attitudes, and behaviors that occur within it. On the macro level, structural aspects of the media system (e.g., platform availability) can affect media choices (e.g., Webster, 2009), whereas on the micro level, parents and schools can forbid adolescents from spending time on social media during dinner or in the classroom (Valkenburg & Piotrowski, 2017). In addition, especially in adolescence, peer groups can exert a strong influence on certain preferences and behaviors (Brechwald & Prinstein, 2011), including media preferences (Valkenburg & Cantor, 2000). Members of a peer group share norms that they have created themselves. Adolescents typically form strong social antennas for these norms, including those pertaining to social media use. Environmental influences on social media use can thus occur overtly (e.g., by parental restriction or monitoring) or more covertly, for example through adolescents’ sensitivity to the prevailing norms in their peer group.

The Conditionality Paradigm

The conditionality paradigm is closely linked with the selectivity and transactionality paradigms. After all, in both paradigms it is argued that only the messages that individuals select in response to person-specific antecedents have the potential to influence them. Theories that propose conditional media effects share the notion that media effects (a) do not equally hold for all media users, and (b) can be enhanced or reduced by dispositional, developmental, and social-context factors (Valkenburg & Peter, 2013a). In line with earlier media effects theories (e.g., Bandura, 2009), Valkenburg and Peter’s DSMM postulates that dispositional, developmental, and social-context factors may have a double role in the media effects process: They not only predict media use, but they also influence the way in which media messages are processed and subsequent distal media outcomes. This twofold influence results in three types of differential susceptibility to media effects: dispositional, developmental, and social-context susceptibility.

Dispositional Susceptibility

Dispositional susceptibility refers to the degree to which certain dispositions influence media processing and media outcomes. It has been shown, for example, that trait aggressiveness can increase the effects of media violence on cognitive and emotional processing of violent media content (Schultz et al., 2004), which may, in turn, result in enhanced aggression (Krcmar, 2009). As for social media, it has been shown that Facebook users who scored high on FOMO, experience more hurtful comments, and more stalking and harassment (Buglass et al., 2017). In addition, sensation seeking is an important predictor of risky behavior on social media, whereas a lack of inhibitory control can result in more negative feedback on these media (Koutamanis et al., 2015). Finally, specific affordances of social media may particularly stimulate online disinhibition among self-conscious and socially anxious adolescents (e.g., Schouten et al., 2007). This online disinhibition has been shown to result in positive (e.g., friendship closeness; Valkenburg & Peter, 2009) or negative effects of social media use (e.g., cyberbullying; Nesi et al., 2018b).

Developmental Susceptibility

Developmental susceptibility refers to the degree to which developmental level influences media processing and media outcomes. Evidence for developmental susceptibility is relatively scarce. It has been shown that younger children react with stronger physiological arousal to violent and frightening audiovisual content than adolescents, even if this content is unrealistic, which may enhance the effects of such content (Cantor, 2009). In addition, online sexual risk behavior seems to reach a peak in middle adolescence, after which it levels off again (Baumgartner et al., 2012). This developmentally induced inverted U-shaped trajectory is often explained by dual-system theories of brain development (e.g., Steinberg, 2010), which argue that the parts of the adolescent brain that are responsible for reward sensitivity to social stimuli may develop more quickly than the parts that are responsible for regulation of this reward sensitivity.

Social-Context Susceptibility

Social-context susceptibility refers to the degree to which social context factors influence media processing and media outcomes. Evidence for social-context susceptibility comes from studies showing that when physical violence is normative in families, children may learn to interpret media violence differently (Schultz et al., 2004), making them more susceptible to media effects on aggression (Fikkers et al., 2013). Social-context susceptibility can be explained by the context-convergence hypothesis (Valkenburg & Peter, 2013a), which posits that individuals are more susceptible to media messages if these messages converge with the values and norms in their social context. In cultivation theory (Gerbner et al., 1980, p. 15), an early media effects theory, this phenomenon has been named resonance: When something experienced in the media is similar to the norms that prevail in one’s social environment, it creates a “double dose” of the message, which enhances the likelihood of media effects.

Social Media as a Social Context in Its Own Right

As discussed earlier on in the chapter, social context refers to the environment within which individuals or groups act or interact, and whose norms and affordances may influence the cognitions, emotions, attitudes, and behaviors that occur within it. An important theoretical question is whether we need to conceptualize social media as a social context in its own right that may shape both social media uses and their effects. Authors differ in their conceptions of whether social media should be seen as a social context in itself. Some scholars adhere to a “Mirroring Framework” (Nesi et al., 2018a, p. 268), that is, the notion that adolescents’ experiences on social media simply mirror their offline experiences.

Several other scholars, including the author of this chapter, believe that social media is not merely a technology, but a social context, whose norms and affordances may influence social media use, as well as the changes among users that result from this use. These scholars do acknowledge that the social media context overlaps with other contexts, such as the family, peer, and school context. But such overlap also applies to other social contexts (e.g., family with school; peer group with school). Coconstruction theory (Subrahmanyam et al., 2006) and the transformation framework (Nesi et al., 2018a, 2018b) both discuss how the social media context differs from equivalent offline interaction contexts. Coconstruction theory proposes that even though adolescents construct the same developmental issues online as they do offline, they use specific affordances of social media that do not exist in offline situations (e.g., cue manageability and scalability) to construct and coconstruct their identity, intimacy, and sexuality. Finally, following affordance theories of social media (e.g., boyd, 2011; McFarland & Ployhart, 2015; Peter & Valkenburg, 2013), the transformation framework considers social media as a context that differs in important ways from face-to-face and earlier digital interactions (e.g., email). As a result, this context may affect social media uses and their effects in different ways than face-to-face and earlier digital interactions (Nesi et al., 2018a, 2018b).

A telling example of a defining norm of the social media context is its positivity bias, which refers to the observation that public social media interactions (e.g., Instagram, Facebook) are typically more positive than equivalent offline interactions (e.g., Reinecke & Trepte, 2014; Waterloo et al., 2017). This positivity bias may influence both message recipients and message senders positively or negatively. Message recipients can be exposed to positively biased messages of happy, successful, and popular peers. Among some recipients this exposure may result in envy and negative psychosocial effects (e.g., Vogel et al., 2014). And among other recipients it may lead to inspiration, and positive psychosocial effects (e.g., Meier et al., 2020).

The positivity bias may also influence message senders in opposite ways. Firstly, their positively biased self-presentations may increase their own psychological well-being (Burnell et al., 2020), a phenomenon that has been named a self-effect (Valkenburg, 2017). But when these self-presentations are exaggerated (e.g., too emotional) they may create embarrassment and guilt, and decrease psychological well-being (Stern, 2015). Apparently, the perceptions and consequences of the positivity bias on social media differ from adolescent to adolescent, an idea that will be elaborated upon when discussing affordance theories of social media.

Computer-Mediated Communication Theories

Studies into the cognitive, affective, and behavioral effects of social media have often been inspired by theories of computer-mediated communication (CMC). CMC theories and research emerged in the 1970s, long before the Internet became widespread. Unlike media effects research, which evolved from the study of mass communication, CMC research originated from a mixture of interpersonal communication, teleconferencing, and organizational behavior. In addition, whereas media effects research is more survey-oriented, the approach of CMC research is mostly experimental. CMC research has typically focused on comparing the cognitive, affective, and behavioral effects of face-to-face communication to those of CMC. It has often centered on questions such as whether and how certain CMC properties, such as anonymity or the lack of audiovisual cues, influence the quality of social interaction among dyads or group members, and the impressions these dyads or group members form of one another.

In the 1970s, some early, rather pessimistic CMC theories compared the “lean” text-only CMC with the “rich” communication in face-to-face settings. In doing so, they tried to explain, for example, why CMC leads to less intimacy and more disinhibited behavior (Walther, 2011). In the early 1990s, a new cluster of theories emerged, with a more optimistic view on CMC. That was the time that individuals started emailing, and the Internet became available for personal use. During this time, Walther’s social information processing theory became influential. This theory explains how CMC partners can gradually overcome the presumed limitations of CMC by creatively employing strategies to exchange and understand social and emotional messages in CMC. In this way, with sufficient time and message exchanges, CMC partners could develop intimacy levels comparable to those in face-to-face communication (Walther, 1992).

In the second half of the 1990s, Walther extended his theory with an even more optimistic perspective, which predicted that CMC messages could lead to greater intimacy than face-to-face communication. According to his hyperpersonal communication model (Walther, 1996), the relative anonymity and reduced audiovisual cues in CMC encourage individuals to optimally present themselves, for instance, by pretending to be kinder and more beautiful than they actually are. Meanwhile, the recipients of these optimized self-presentations are free to fill in the blanks in their impressions of their partners, which may encourage them to idealize these partners. In doing so, CMC relationships could become “hyperpersonal,” that is, more intimate than offline relationships (Walther, 1996). In the same period, another influential CMC theory emerged, the social identity model of deindividuation effects, whose major focus was to explain how the anonymity in CMC groups affects normative and anti-normative behavior among their members (Postmes et al., 2000).

The focus of early CMC theories on anonymity and limited audiovisual cues fitted well in the 1990s and the first half of the 2000s, when CMC was predominantly text-based and typically took place in anonymous chatrooms or newsgroups (Valkenburg et al., 2016). However, most current CMC technologies popular among adolescents, such as Instagram and Snapchat, are much less anonymous than their predecessors, and rely heavily on a range of audiovisual cues. Therefore, it has become less relevant to experimentally compare their specific CMC properties with face-to-face communication (Scott & Fullwood, 2020). Moreover, the “computer” part of CMC applications has become more portable and ubiquitous, and has diluted into a multitude of mobile devices and apps (Xu & Liao, 2020, p. 32). Indeed, the devices with which we communicate have gotten closer and closer to our bodies. They moved from our desks (desktop), to our bags (laptop), to our pockets (smartphone), and to our wrists (Valkenburg & Piotrowski, 2017). It is no surprise that these rapid developments provide contemporary CMC theorists with many new conceptual, theoretical, and empirical challenges (Carr, 2020).

An important strength of CMC theories and research, certainly when compared with media effects theories, has been their strong focus on the dynamic give-and-take interactions between message senders and recipients. CMC theories are, by definition, transactional theories that acknowledge that message exchanges are shaped by both message senders and receivers (Valkenburg, 2017). However, possibly due to its experimental orientation, CMC research has often focused on the unidirectional, across-the-board effects of CMC properties (i.e., anonymity, reduced audiovisual cues) on the recipients of these properties. Although both media effects and CMC theories like to describe recipients as active in the sense that they have autonomy over the way they interpret media or CMC characteristics, the empirically investigated influence in both research traditions is still all too often unidirectional: from the media or technology to the recipients.

However, if we accept that the current generation of social media are not merely technologies, but a social context whose norms and affordances differ from offline social contexts, such as the peer group or the neighborhood (Sameroff, 2009), we may need an updated theorization on the uses and effects of social media. Such an update needs to address the transactional relationships between social media users and the social media context, as well as the interactions between the social media context and other, offline, contexts. In the next section, I will make a preliminary start on such an update, by introducing a transactional affordance theory of social media uses. I deliberately use the term “uses” to refer to the many possible uses of social media.

Three types of theories might offer inspiration to such an updated theorization: transactional theories of development (e.g., Bronfenbrenner, 2005; Sameroff, 2009), Gibson’s (1979) affordance theory, which later evolved into affordance theories of social media (e.g., boyd, 2011; Treem & Leonardi, 2013), and self-effects theory (Valkenburg, 2017). Transactional theories of development propose that change within an adolescent is a product of their continuous dynamic interactions with their experienced social contexts (Bronfenbrenner, 2005; Sameroff, 2009). Gibson’s affordance theory is a learning theory that explains how different perceptions of an object or environment can result in different actions toward or uses of this object or environment. Finally, self-effects are the effects of messages on message senders themselves. As will be clear, social media use cannot only result in transactions (i.e., changes) within message recipients, but also within the senders of these messages.

A Transactional Affordance Theory of Social Media Uses

A transactional affordance theory of social media uses elaborates on three related propositions raised in transactional theories and/or affordance theories and/or self-effects theory: These propositions are: (1) social media users (co)create their own social media context, and this (co)created context shapes their experienced effects; (2) just like the family, school, and peer context, the social media context is a micro-level social context, in which transactional effects are more likely than in the mass media context; (3) the experiences with the social media context differ from adolescent to adolescent; thus, the unique way in which an adolescent experiences the norms, affordances, and messages in this context is the driving force of social media effects on this adolescent.

Social Media Users Shape Their Own Effects

The first proposition is that (1) social media users can individually (or collectively) shape their social media context, and (2) their experiences within this social media context can shape the effects of this context. The first part of this proposition is in line with transactional theories of development and Gibson’s (1979) affordance theory. Transactional theories of development agree that children can shape and be shaped by their experienced social contexts (Bronfenbrenner, 2005; Sameroff, 2009). Likewise, Gibson argued that individuals tend to alter their environment by adjusting its affordances to better suit their needs and desires. In other words, an individual’s perceptions of the affordances of a context may lead to specific uses of this context, which in turn shape the experienced effects of this context. A similar proposition has been raised in self-effects theory (Valkenburg, 2017), which proposes that social media users carefully craft their messages (e.g., social media posts), which may influence the recipients of these messages (i.e., the social environment) but also the message senders themselves, directly via internalization of overt behavior (Bem, 1972), or indirectly, via the feedback that their messages elicit.

The first part of this proposition, that social media users can individually (or collectively) shape their social media context, has received support. Adolescents can (co)create both the affordances and norms of the social media contexts in which they participate. It has been found, for example, that the sharing of intimate, self-related information is more accepted in the social media context than in equivalent offline contexts (Christofides et al., 2009). Another (co)created norm is that the sharing of negative emotions is more accepted in private (e.g., WhatsApp) than public social media contexts (e.g., Instagram; Waterloo et al., 2017). And if adolescents do want to share intimate, self-related information on a public social medium like Instagram, they sometimes turn to a Finsta (a Fake Instagram account where they can be honest and show their true self) in addition to a Rinsta (a Real Instagram account used to post their positive experiences). Finally, overly emotional expressions on in public social media are considered norm violations (Waterloo et al., 2017).

The second part of this proposition, that adolescents’ unique experiences within their (co) created social media context can shape the effects of this context, has also received support. For example, message recipients can selectively and autonomously expose themselves to uplifting or depressing social media messages, which may subsequently affect their well-being in unique ways. In a qualitative study of Rideout and Fox (2018), one adolescent reported: “If I’m feeling depressed, getting on Twitter and seeing funny tweets or watching funny videos on YouTube can really brighten my mood” (p. 20). In this example, a transient dispositional variable (low mood) shaped this adolescent’s selective exposure, which in turn positively shaped their experienced effect (i.e., a brightened mood). In the same study, another adolescent’s preexisting low mood resulted in an opposite effect of social media browsing (i.e., a worsened low mood): “Social media makes me feel worse when I’m scrolling through feeds and seeing news headlines and posts about how terrible something is” (Rideout & Fox, 2018, p. 19). And yet another adolescent with a preexisting low mood reacted with selective avoidance: “Usually friends post happy things – getting together with others, accomplishments, bragging. I don’t always want to see it when I’m feeling down about myself so I stay off social media” (p. 20).

These qualitative finding illustrate the complex nature of the associations between preexisting disposition (i.e., low mood), selective exposure to social media messages, and postexposure mood. Mood-induced selective exposure to social media messages can enhance mood (adolescent 1), worsen mood (adolescent 2), and it can lead to selective avoidance (adolescent 3). Such unique differences have also been reported in two recent experience sampling studies by Beyens et al. (2020, 2021), who found considerable differences in experienced effects of social media use. In one study, they found that 46% of the participating adolescents felt better after social media browsing in the past hour, while 44% did not feel better or worse, and 10% felt worse after such use (Beyens et al., 2020).

Such uniquely experienced social media effects also seem to hold for message senders. Several studies have shown that message sending (e.g., posting) can improve the well-being of message senders (Verduyn et al., 2017), a result that has often been explained by the positive feedback that message senders receive (Verduyn et al., 2017). However, social media–induced improvements in well-being can also occur without any involvement of fellow users (Pingree, 2007; Valkenburg, 2017). Self-expressions on social media, especially when their intended audience is sizeable, may lead to internalization of these self-expressions, for example, via self-perception. Self-perception theory (Bem, 1972) argues that individuals infer their internal self-concept from retrospectively observing their own overt behavior. If these individuals share positive self-expressions induced by the positivity norm in public social media, these individuals may, due to a desire for a consistency between their overt behavior and their self-concept, adjust their self-concept to match their behavior. For a discussion of self-effects in social media, and the mechanisms that may explain such effects, such as cognitive reframing, biased scanning, and public commitment, see Valkenburg (2017).

Social Media as a Micro- and Mesosystem

A second proposition of a transactional affordance theory of social media uses is that the social media context is a micro-level context, in which effects on participants are more likely than in the mass media context. Bronfenbrenner was one of the first to conceptualize the relationship between individuals and their social contexts. He distinguished between four types of contexts: the micro-, meso-, macro-, and exosystem (Bronfenbrenner, 1979, 2005). The microsystem involves direct interactions of the child with their most proximal circle, such as the family, peer group, or neighborhood. The mesosystem represents the possible interactions among these microsystems (e.g., between the family and peer group), whereas the macrosystem refers to the overarching culture or subculture of children. Bronfenbrenner’s fourth context, the exosystem, refers to social contexts that do not allow the child as an active participant but that have the potential to affect the child. An example of an exosystem is the work context of one of the parents of the child. A child cannot actively participate in this context but can in many ways be influenced by it.

At the time of the development of his theory, Bronfenbrenner identified the mass media as an exosystem because it did not allow for active involvement of adolescents, even though it could shape their experiences. Although valid at the time, Bronfenbrenner (1917–2005) could not have foreseen the rapid developments within the media landscape. If he could have, he would probably have categorized the social media context as a microsystem rather than an exosystem. After all, unlike before, the media landscape now does allow for, and even stimulates, direct interactions among participants. For example, idols, an important source of identity formation in adolescence, have been transferred from the exosystem to the microsystem: Whereas movie stars or pop singers used to be celebrities that adolescents could admire from an unsurmountable distance, social media now provide them with ample opportunity for direct communication with their idols. In fact, many of their contemporary idols are YouTubers or Instagram influencers with whom they can directly interact.

If Bronfenbrenner could, he may now also have identified the social media context as part of the mesosystem because it allows for, or even stimulates, interactions with other microsystems (e.g., the family or the peer contexts). Although every traditional microsystem is in part “permeable” to the influences from other microsystems (e.g., family to peers and vice versa; family to school and vice versa), the social media context might be much more permeable to such influences. Conversely, the social media context seems to have penetrated all other microsystems in which adolescents participate, ranging from the family and peer context to the school.

However, if we accept the social media context as a microsystem, we must acknowledge that this context may, due to its proximity, dynamic, and ubiquitous nature, enhance the likelihood of effects on its participants, certainly when compared to the traditional mass media context. And if we accept the social media context as a part of the mesosystem (interactions among microsystems), we need to acknowledge that it may interact with the norms and affordances of other microsystems, such as parents or the school. And such interactions do occur. For example, preventing or counteracting possible negative consequences of social media interactions, and explaining to adolescents that the social media context may not be as perfect as it often appears, are important ingredients of today’s media-specific parenting and school-based prevention and intervention programs (Valkenburg & Piotrowski, 2017).

It Is the Subjective Experience That Counts

A third and final proposition of a transactional affordance theory of social media uses is that the unique way in which individuals experience the norms and affordances of the social media context is the driving force of transactional effects between individuals and this context. This proposition is consistent with both transactional theories of development (Bronfenbrenner, 2005; Sameroff, 2009) and Gibson’s affordance theory (Gibson, 1979). Affordances, according to Gibson, are the unique ways in which individuals experience the utility of objects. For example, distinct individuals may all perceive another utility of a bottle (e.g., as a water container, a vase, a candle holder, or a weapon). However, to understand such individual differences in experiences of the affordances of social media, I first specify some of these affordances and argue how and why these affordances differ from other micro-level social contexts, such as the family or peer contexts.

A growing number of social media scholars have ventured to identify specific affordances of social media (boyd, 2011; McFarland & Ployhart, 2015; Nesi et al., 2018a, 2018b; Sundar et al., 2015; Treem & Leonardi, 2013; Valkenburg & Peter, 2011; Valkenburg & Piotrowski, 2017). Some of these scholars have identified four affordances (Treem & Leonardi, 2013), others have focused on seven (Nesi et al., 2018a; Valkenburg & Piotrowski, 2017) or even eight affordances (McFarland & Ployhart, 2015). Many comparable affordances appear in different studies but sometimes under different names (e.g., identifiability vs. cue absence; scalability vs. publicness). In this chapter, the focus is on three affordances that have been mostly identified in earlier literature. For each affordance, I discuss the scarce evidence of individual differences in the perceptions of its utility, as well as its potential consequences for both senders and recipients of social media messages. A more elaborate discussion of these consequences can be found in Nesi et al. (2018a, 2018b)

Asynchronicity

Most social media are asynchronous, that is, they afford their users the possibility to edit and reflect on their messages and pictures before uploading them. Even in more synchronous apps, such as WhatsApp, users must press the send button before they can transmit their message or photo to partners or group members. Asynchronous communication allows message senders to carefully craft, refine, and optimize their self-presentations. Adolescents differ significantly in the importance they attach to this affordance. In one of our survey studies, we asked (pre)adolescents (10–17-year-olds) how much importance they attached to the idea that they have more time to think about what they share on social media than in face-to-face encounters (this part of data not published). Thirty-seven percent of them attached importance or high importance to this affordance, 25% did not attach any importance to this affordance, and a remaining 38% reported that they did not care. The asynchronicity affordance seemed particularly valuable for early and middle adolescents (12–15-years-olds), socially anxious, and lonely adolescents, who apparently benefit most from the extra time to optimize their self-presentations (Peter & Valkenburg, 2006).

The asynchronicity affordance may influence both senders and recipients of social media messages. The optimized self-presentations of senders could lead to self-effects through internalization of these self-presentations (Valkenburg, 2017). Such optimized self-presentations can also influence message recipients in both positive and negative ways. They can evoke empathy, laughter, or a positive mood, but in case they are optimized to hurt recipients, they can also lead to painful experiences among recipients (Rideout & Fox, 2018; Valkenburg & Peter, 2013a).

Cue Manageability

Most social media offer their users the possibility to show or hide visual or auditory cues about the self. Social media users can decide whether they present themselves only through textual descriptions or whether they add more cues, such as pictures or video clips. Moreover, by means of specific software, they can edit, manipulate, and optimize these cues. Adolescents differ greatly in the importance they attach to the cue-manageability affordance. For example, in one of our studies, 8% of adolescents deemed it important or very important that others cannot see them while communicating on social media, whereas 55% deemed it as unimportant, and 37% reported that they did not care (this part of the data not published). The cue-manageability affordance seems particularly valuable for female adolescents, socially anxious adolescents, and adolescents high in private self-consciousness (e.g., I am generally attentive to my inner feelings), and public self-consciousness (e.g., I usually worry about making a good impression; Schouten et al., 2007).

Like the asynchronicity affordance, cue management affords adolescents possibilities to optimize their online self-presentations, which can lead to positive self-effects, for example via self-perception (Bem, 1972) or to cognitive reframing (an intra-individual change in how previous experiences are viewed). However, when the self-presentations are exaggerated (e.g., too intimate or childish), they can violate the norms of the social media context, and they may trap adolescents in uncomfortable situations, in which they may become ridiculed or socially rejected (Peter & Valkenburg, 2013).

Scalability

Scalability offers social media participants the ability to articulate self-related messages and photos to any size and nature of audiences. It thus provides message senders with ample forums to commit themselves to realistic or imagined social media audiences. This may be preeminently attractive to adolescents, whose egocentrism (i.e., their inability to distinguish between their perception of what others think and what others actually think of them) may result in their perception of an imaginary audience that is constantly observing their actions (Elkind, 1967).

To my knowledge, no research has demonstrated individual differences in the value attached to the scalability affordance, and this may, therefore, be an interesting question for future research. The scalability affordance may enhance self-effects through public commitment. When individuals believe that their self-presentations are public, the likelihood of internalization enhances (Kelly & Rodriguez, 2006), not only because other people can see their presentations, but also because individuals do not like to appear inconsistent in their public self-presentations (Tice, 1992).

The three affordances of social media are all important in their own right but they have an important overarching affordance in common: They offer social media users greater controllability of their self-presentations than face-to-face interactions or older technologies do (Valkenburg & Peter, 2011). This controllability means that social media users can choose not only what, but also how, when, and to whom in the global village they can present themselves. This controllability may offer social media users a sense (or an illusion) of security, which makes some of them feel freer in their interpersonal interactions than they can experience in other micro-level social contexts. This sense (or illusion) of security and freedom is particularly important for adolescents, who typically experience enhanced uncertainty about their identity (i.e., how to define who they are and will become), intimacy (i.e., how to form and maintain meaningful relationships), and sexuality (e.g., how to cope with sexual desire and define their sexual orientation; Steinberg, 2011). This enhanced controllability of self-presentations may, therefore, be a major explanation of adolescents’ attraction to social media (Valkenburg & Peter, 2011).

Conclusions and Avenues for Future Research

In this chapter, I conceptualized social media as a social context in its own right, and borrowing from Bronfenbrenner’s (1979) typology, as a social context that frequently interacts with other micro-level contexts, such as the family, peer group, and school. I also explained how the social media context differs from the traditional mass media context and why it can lead to stronger effects on both message senders and recipients. The social media context is not only more proximal and ubiquitous than the mass media context, but it is also more dynamic in the sense that everyone can actively participate in and contribute to it. Whereas the “effects” of mass media have mostly been conceptualized as recipient effects in earlier research, social media inherently point our attention to self-effects: the messages produced by the sender on themself. The emphasis on self-effects is important for future social media research because it implies a focus on theories accounting for intra-individual transactions as a result of one’s own affordance-induced behavior, next to theories explaining intra-individual transactions among recipients that occur as a result of selective attention and perception of messages sent by others.

Consistent with Gibson’s (1979) affordance theory, this chapter revealed that adolescents differ greatly in their perceptions of some of the affordances of social media. Preliminary work also suggest that they also differ greatly in the effects they experience in the social media context (Pouwels et al., 2021; Valkenburg et al., 2021). Unfortunately, social media effects research still all too often focuses on universal effects. This may in part be due to the experimental focus of the CMC research tradition, in which individual differences are typically disregarded, because they are assumed to be canceled out by random assignment (Bolger et al., 2019). If such individual differences are measured at all, they are often included as covariates rather than as factors that may interact with the experimental condition (Valkenburg & Peter, 2013b).

There is a need for future research focusing on transactional and person-specific effects of social media use. Qualitative studies have repeatedly demonstrated that adolescents can differ substantially in their media use, their experiences on social media, and the effects of social media use (e.g., Rideout & Fox, 2018). However, most quantitative studies into the psychosocial effects of social media still adopt a group-differential approach, in which potential differences in susceptibility are conceptualized by group-level moderators, such as gender or age (Beyens et al., 2020; Howard & Hoffman, 2017). However, due to technological advancements, it has become feasible to collect masses of intensive longitudinal data from masses of individuals on the uses and effects of social media (e.g., through experience sampling or tracking). Moreover, rapid developments in data mining and statistical methods now also enable researchers to analyze highly complex N = 1 time series data, and by doing so, to develop and investigate media effects and other communication theories bottom up (i.e., from the individual adolescent to the population or subpopulation) rather than top down (i.e., from the population to the adolescent; Lerner et al., 2019).

In our recent and current experience sampling studies, we have adopted such a person-specific, N = 1 time series approach (McNeish & Hamaker, 2020). Up to now, our results show striking differences in adolescents’ susceptibility to the momentary effects of social media on well-being (Beyens et al., 2020), self-esteem (Valkenburg et al., 2021), and friendship closeness (Pouwels et al., 2021). In all these studies, the effect sizes of social media use on outcomes ranged from moderately or strongly negative to moderately or strongly positive. For example, the within-person effect sizes of social media browsing on well-being ranged from β = −0.24 to β = +0.68 across adolescents. Likewise, the effects of Instagram use on friendship closeness ranged from β = −0.57 to β = +0.45. And the effects of social media use on self-esteem led to lagged effect sizes ranging from β = −0.21 to β = +0.17.

Unfortunately, we still do not know how these short-term effects of social media use accumulate into longer-term effects, and this is an important avenue for future research. Moreover, up to now we do not know whether the person-specific effects that we found can be attributed to (stable or transient) dispositional, developmental, and/or (situational or structural) social-context factors. An important avenue for future research is to explain why social media use can lead to “positive susceptibles” (i.e., adolescents who mainly experience positive effects of social media use), “negative susceptibles” (adolescents who mainly experience negative effects of social media use, and “nonsusceptibles” (adolescent who are predominantly unaffected by social media use). After all, only if we know which, when, how, and why adolescents may be influenced by certain types of social media use will we be able to adequately target prevention and intervention strategies to these adolescents.