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9 - Big Data

Published online by Cambridge University Press:  11 June 2021

Adrian Furnham
Adrian Furnham
Affiliation:
University of London
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Summary

One of the biggest, newest and most exciting assessment and research opportunity to occur since the millennium has been the exploitation of Big Data, which is the ‘electronic footprint’ that we all leave when using credit and other cards as well as the web, through a variety of social networks. Assessment, selection and recruitment experts have not been slow in seeking Big Data as a way of collecting a wide variety of pieces of information about targeted individuals. There have also been some high-profile scandals using Big data. This chapter looks at the five Vs of Big data: Volume (how much data on individuals is potentially available), Variety (the wide range of data on behaviours available), Velocity (the sheer speed of data accumulation and possibilities of analysis), Veracity (the all-important point of the accuracy and truthfulness of the data) and Value (whether it is uniquely valuable or not). Studies on Facebook profiles are discussed in detail. It is perhaps the most exciting prospect for person assessment, but the promises, perils and problems are also discussed. Finally, half a dozen experts report on how they see Big Data as offering opportunities for person assessment.

Keywords

AIData collectionData miningFacebookImpression managementLinguistic analysisMachine learningNetworksPlatformSelf-presentationSocial contactsWikipedia.
Type
Chapter
Information
Twenty Ways to Assess Personnel
Different Techniques and their Respective Advantages
 , pp. 474 - 505
Publisher: Cambridge University Press
Print publication year: 2021

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References

Adjerid, I., & Kelley, K. (2018). Big data in psychology: a framework for research advancement. American Psychologist, 73(7), 899–917.Google Scholar
Amichai-Hamburger, Y., & Vinitzky, G. (2010). Social network use and personality. Computers in Human Behavior, 26(6), 1289–1295.Google Scholar
Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., & Gosling, S. D. (2010). Facebook profiles reflect actual personality, not self-idealization. Psychological Science, 21(3), 372–374.Google Scholar
Bleidorn, W., & Hopwood, C. J. (2019). Using machine learning to advance personality assessment and theory. Personality and Social Psychology Review, 23(2), 190–203.Google Scholar
Buffardi, L. E., & Campbell, W. K. (2008). Narcissism and social networking web sites. Personality and Social Psychology Bulletin, 34, 1303–1314.Google Scholar
Celli, F., Bruni, E., & Lepri, B. (2014). Automatic personality and interaction style recognition from Facebook profile pictures. In Proceedings of the 22nd international conference on multimedia (pp. 1101–1104). Glasgow, UK: ACM. http://dx.doi.org/10.1145/ 2647868.2654977.Google Scholar
Correa, T., Hinsley, A., & De Zuniga, H. (2010). Who interacts on the web? Computers in Human Behavior, 26, 247–253.Google Scholar
Doward, J., & Gibbs, A. (2017, March 4). Did Cambridge Analytica influence the Brexit vote and the US election? The Guardian. www.theguardian.com/politics/2017/mar/04/nigel-oakes-cambridge-analytica-what-role-brexit-trumpGoogle Scholar
Evans, D. C., Gosling, S. D., & Carroll, A. (2008).What elements of an online social networking profile predict target-rater agreement in personality impressions? In Proceedings of the international conference on weblogs and social media (pp. 1–6). Seattle, WA.Google Scholar
Favaretto, M., De Clercq, E., & Elger, B. S. (2019). Big Data and discrimination: perils, promises and solutions. A systematic review. Journal of Big Data, 6(12), 1–27.CrossRefGoogle Scholar
Ferwerda, B., Schedl, M., & Tkalcic, M. (2015, September). Predicting personality traits with instagram pictures. In Proceedings of the 3rd workshop on emotions and personality in personalized systems 2015 (pp. 7–10). Vienna, Austria: ACM.Google Scholar
Furnham, A., & Swami, V. (2015). An investigation of attitudes toward surveillance at work and its correlates. Psychology, 6(13), 1668–1674.Google Scholar
George, G., Haas, M. R., & Pentland, A. (2014). From the editors: big data and management. The Academy of Management Journal, 57(2), 321–326.Google Scholar
Gil-Lopez, T., Shen, C., Benefield, G. A., Palomares, N. A., Kosinski, M., & Stillwell, D. (2018). One size fits all: context collapse, self-presentation strategies and language styles on Facebook. Journal of Computer-Mediated Communication, 23(3), 127–145.Google Scholar
Golbeck, J., Robles, C., & Turner, K. (2011, May 7–12). Predicting personality with social media. [Paper presented at the 2011 annual conference on human factors in computing systems], Vancouver, Canada.Google Scholar
Gonzalez, R. J. (2017). Hacking the citizenry? Personality profiling, ‘big data’ and the election of Donald Trump. Anthropology Today, 33(3), 9–12.Google Scholar
Gosling, S. D., Augustine, A. A., Vazire, S., Holtzman, N., & Gaddis, S. (2011). Manifestations of personality in online social networks: self-reported Facebook-related behaviors and observable profile information. Cyberpsychology, Behavior, and Social Networking, 14(9), 483–488.Google Scholar
Gosling, S. D., & Mason, W. (2015). Internet research in psychology. Annual Review of Psychology, 66, 877–902.Google Scholar
Green, K. (2013). The social media effect: are you really who you portray online? HuffPost. Retrieved from www.huffingtonpost.com/r-kay-green/the-social-media-effect-a_b_3721029.htmlGoogle Scholar
Hall, J. A., Pennington, N., & Lueders, A. (2014). Impression management and formation on Facebook: a lens model approach. New Media & Society, 16(6), 958–982.Google Scholar
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(44), 1–16.CrossRefGoogle Scholar
Harlow, L. L., & Oswald, F. L. (2016). Big data in psychology: introduction to the special issue. Psychological Methods, 21(4), 447–457.Google Scholar
He, Q., Glas, C. A., Kosinski, M., Stillwell, D. J., & Veldkamp, B. P. (2014). Predicting self-monitoring skills using textual posts on Facebook. Computers in Human Behavior, 33, 69–78.Google Scholar
Ihsan, Z., & Furnham, A. (2018). The new technologies in personality assessment: a review. Consulting Psychology Journal: Practice and Research, 70(2), 147–166.CrossRefGoogle Scholar
Kosinski, M., Bachrach, Y., Kohli, P., Stillwell, D., & Graepel, T. (2014). Manifestations of user personality in website choice and behaviour on online social networks. Machine Learning, 95(3), 357–380.Google Scholar
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805.Google Scholar
Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032–2033.Google Scholar
Lane, J. (2016). Big data and anthropology: concerns for data collection in a new research context. Journal of the Anthropological Society of Oxford, 3(1), 74–88.Google Scholar
Mahmoodi, J., Leckelt, M., van Zalk, M. W., Geukes, K., & Back, M. D. (2017). Big Data approaches in social and behavioral science: four key trade-offs and a call for integration. Current Opinion in Behavioral Sciences, 18, 57–62.Google Scholar
Marcus, B., Machilek, F., & Schütz, A. (2006). Personality in cyberspace: personal Web sites as media for personality expressions and impressions. Journal of Personality and Social Psychology, 90(6), 1014–1031.Google Scholar
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt Publishing Company.Google Scholar
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, D. J., Seligman, M. E., & Martin, E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934–952.Google Scholar
Qiu, L., Lin, H., Leung, A. K., & Tov, W. (2012a). Putting their best foot forward: emotional disclosure on Facebook. Cyberpsychology, Behavior, and Social Networking, 15(10), 569–572.Google Scholar
Qiu, L., Lin, H., Ramay, J., & Yang, (2012b). You are what you tweet: personality expression and perception on Twitter. Journal of Research in Personality, 46(6), 710–718.Google Scholar
Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., & Crowcroft, J. (2012, February). The personality of popular facebook users. In Proceedings of the ACM 2012 conference on computer supported cooperative work (pp. 955–964). Seattle, DC.Google Scholar
Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009). Personality and motivations associated with Facebook use. Computers in Human Behavior, 25(2), 578–586.Google Scholar
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E., & Ungar, L. H. (2013). Personality, gender, and age in the language of social media: the open-vocabulary approach. PLOS ONE, 8(9), 1–16.CrossRefGoogle ScholarPubMed
Skowron, M., Tkalčič, M., Ferwerda, B., & Schedl, M. (2016). Fusing social media cues: personality prediction from Twitter and Instagram. In Proceedings of the 25th international conference companion on world wide web (pp. 107–108). Geneva, Switzerland: International World Wide Web Conferences Steering Committee.Google Scholar
Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B., & Bühner, M. (2019, June 12). Behavioral patterns in smartphone uage predict Big Five personality traits. Retrieved from https://doi.org/10.31234/osf.io/ks4vd.Google Scholar
Thompson, A. (2016, December 8). Journalists and Trump voters live in eparate online bubbles, MIT analysis shows. Vice News. www.vice.com/en_us/article/d3xamx/journalists-and-trump-voters-live-in-separate-online-bubbles-mit-analysis-showsGoogle Scholar
Vazire, S., & Gosling, S. D. (2004). e-Perceptions: personality impressions based on personal websites. Journal of Personality and Social Psychology, 87(1), 123–132.CrossRefGoogle ScholarPubMed
Xu, R., Frey, R. M., Vuckovac, D., & Ilic, A. (2015). Towards understanding the impact of personality traits on mobile app adoption – a scalable approach. In Proceedings of the 23rd European conference on information systems (pp. 1–12), Münster, Germany.Google Scholar

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  • Big Data
  • Adrian Furnham, University of London
  • Book: Twenty Ways to Assess Personnel
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108953276.010
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Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Big Data
  • Adrian Furnham, University of London
  • Book: Twenty Ways to Assess Personnel
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108953276.010
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Big Data
  • Adrian Furnham, University of London
  • Book: Twenty Ways to Assess Personnel
  • Online publication: 11 June 2021
  • Chapter DOI: https://doi.org/10.1017/9781108953276.010
Available formats
×