Center for Communication and Democracy Senior Associate Dhavan V. Shah, Maier-Bascom Professor at the University of Wisconsin and Director of the Mass Communication Research Center, has co-edited the May 2015 special issue of The ANNALS of the American Academy of Political and Social Science, “Toward Computational Social Science: Big Data in Digital Environments.”
The special issue includes articles by leading scholars in the field of big data, including:
Hargittai, E. (2015). Is bigger always better? Potential biases of big data derived from social network sites. The ANNALS of the American Academy of Political and Social Science, 659(1), 63-76. doi: 10.1177/0002716215570866
ABSTRACT: This article discusses methodological challenges of using big data that rely on specific sites and services as their sampling frames, focusing on social network sites in particular. It draws on survey data to show that people do not select into the use of such sites randomly. Instead, use is biased in certain ways yielding samples that limit the generalizability of findings. Results show that age, gender, race/ethnicity, socioeconomic status, online experiences, and Internet skills all influence the social network sites people use and thus where traces of their behavior show up. This has implications for the types of conclusions one can draw from data derived from users of specific sites. The article ends by noting how big data studies can address the shortcomings that result from biased sampling frames.
Bode, L., Hanna, A., Yang, J., & Shah D. V. (2015). Candidate networks, citizen clusters, and political expression: Strategic hashtag use in the 2010 midterms. The ANNALS of the American Academy of Political and Social Science, 659(1), 149-165. doi: 10.1177/0002716214563923
ABSTRACT: Twitter provides a direct method for political actors to connect with citizens, and for those citizens to organize into online clusters through their use of hashtags (i.e., a word or phrase marked with # to identify an idea or topic and facilitate a search for it). We examine the political alignments and networking of Twitter users, analyzing 9 million tweets produced by more than 23,000 randomly selected followers of candidates for the U.S. House and Senate and governorships in 2010. We find that Twitter users in that election cycle did not align in a simple Right-Left division; rather, five unique clusters emerged within Twitter networks, three of them representing different conservative groupings. Going beyond discourses of fragmentation and polarization, certain clusters engaged in strategic expression such as “retweeting” (i.e., sharing someone else’s tweet with one’s followers) and “hashjacking” (i.e., co-opting the hashtags preferred by political adversaries). We find the Twitter alignments in the political Right were more nuanced than those on the political Left and discuss implications of this behavior in relation to the rise of the Tea Party during the 2010 elections.
Freelon, D., Lynch, M., & Aday, S. (2015). Online fragmentation in wartime: A longitudinal analysis of tweets about Syria, 2011–2013. The ANNALS of the American Academy of Political and Social Science, 659(1), 166-179. doi: 10.1177/0002716214563921
ABSTRACT: Theorists have long predicted that like-minded individuals will tend to use social media to self-segregate into enclaves and that this tendency toward homophily will increase over time. Many studies have found moment-in-time evidence of network homophily, but very few have been able to directly measure longitudinal changes in the diversity of social media users’ habits. This is due in part to a lack of appropriate tools and methods for such investigations. This study takes a step toward developing those methods. Drawing on the complete historical record of public retweets posted between January 2011 and August 2013, we propose and justify a partial method of measuring increases or decreases in network homophily. We demonstrate that Twitter network communities that focused on Syria are in general highly fragmented and homophilous; however, only one of the nine detected network communities that persisted over time exhibited a clear increase in homophily.