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3 - Socioeconomic Indicators

Published online by Cambridge University Press:  05 May 2015

By
Huina Mao
Edited by
Yelena Mejova ,
Ingmar Weber  and
Michael W. Macy
Huina Mao
Affiliation:
Indiana University
Yelena Mejova
Affiliation:
Qatar Computing Research Institute, Doha
Ingmar Weber
Affiliation:
Qatar Computing Research Institute, Doha
Michael W. Macy
Affiliation:
Cornell University, New York
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Summary

Twitter has been widely used for human behavior research with its applications in public opinion mining (cf. Chapter 3), studying well-being (cf. Chapter 4), disease monitoring (cf. Chapter 5), and disaster mapping (cf. Chapter 6). In this chapter, we review existing work on using Twitter data to measure socio- economic indicators, including unemployment rate, consumer confidence, social mood, investor sentiment, and financial markets. Moreover, to complement research with Twitter data, we use several examples to illustrate the use of other large-scale data sources (e.g., web search queries, mobile phone calls) for socioeconomic measurement and prediction. At the end, we discuss challenges with existing research and identify several directions for future work.

Introduction

There has been considerable success in leveraging large-scale social media data at the intersection of social sciences and computational sciences with myriad applications in socioeconomic measurement and prediction.

An early study (Antweiler & Frank, 2004) finds that the message volume of stock message boards on Yahoo! Finance and Raging Bull can predict market volatility, and disagreement among posted messages is related to high trading volume. Public mood indicators extracted from social networks such as Facebook (Karabulut, 2011), LiveJournal (Gilbert & Karahalios, 2010), and Twitter (Bollen, Mao, & Zeng, 2011) can predict stock market fluctuations. Zhang, Fuehres, and Gloor (2010) study the correlation between emotional tweets and financial market indicators. They find that the percentage of emotional tweets is negatively correlated with Dow Jones, NASDAQ, and Standard and Poor's (S&P) 500 values, but positively correlated with Volatility Index (VIX). Bollen, Mao, and Zeng (2011) develop a multidimensional mood analysis model that can track Twitter mood in six dimensions (i.e., calm, alert, sure, vital, kind, and happy) and find that Twitter calmness has significant predictive power on daily Dow Jones Industrial Average (DJIA) price changes.

Type
Chapter
Information
Twitter: A Digital Socioscope , pp. 75 - 95
Publisher: Cambridge University Press
Print publication year: 2015

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