Candidate Networks, Citizen Clusters, and Political Expression

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.

Campanha eleitoral no Twitter: as estratégias dos candidatos na disputa para a Prefeitura de Curitiba em 2016

“…divulgação de suas agendas e de suas campanhas eleitorais, bem como para a exposição de sua imagem através de fotos e vídeos (PARMELEE; BICHARD, 2012 BODE et al, 2015). O terceiro grupo procura identificar mensagens postadas durante debates televisionados e entrevistas -ou seja, de que forma o público procura intervir durante estes eventos (ELMER, 2012;RECUERO, 2016).…”

Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter

“…Having analyzed the micro-level dynamics of word usage within #BlackLivesMatter and #AllLivesMatter, we turn to focus on the broader topics of these movements and how these topics coincide with the word-level analysis. Previous work on political polarization has used hashtags as a proxy for topics [30,32,34,51,52] and here we use the same interpretation. However, not all hashtags assist in understanding the broad topics.…”

Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges

“…Surveying the literature in the big data era, it is not uncommon to see the studies that analyzed multi-million pieces of data. For instance, 46 billion words posted by 63 million unique Twitter users were considered as the social sensors of human happiness (Dodds, Harris, Kloumann, Bliss, & Danforth, 2011); a corpus that contained 509 million tweets posted by 2.4 million Twitter users from 84 countries was examined to detect people's seasonal mood changes (Golder & Macy, 2011); 9 million tweets posted by the Twitter followers of candidates for the U.S. House and Senate and governorship in 2010 midterm elections were used to study the digital public's political expression (Bode et al, 2015). However, the "N ≈ all" view has been challenged by many skeptics who have called to caution the biased sampling, particularly in those studies relying on specific social networking sites (boyd & Crawford, 2012;Hargittai, 2015;Sandvig, 2015).…”

“…The progress in acquiring, processing, and analyzing big data has enlightened many fields in the social sciences, such as political science (Bode, Hanna, Yang, & Shah, 2015;Schwartz & Ungar, 2015), public health (Achrekar, Gandhe, Lazarus, Yu, & Liu, 2011;Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014), economics (Einav & Levin, 2014), and criminology (Chen, Cho, & Jang, 2015), to name a few. In the field of education, despite the fast growing body of literature on learning analyticscollecting and analyzing big data to optimize student learning, particularly in online learning environment (Baker & Yacef, 2009), there has been limited scholarship on capitalizing on big data in education policy.…”