A Biased Review of Biases in Twitter Studies on Political Collective Action

Abstract

In recent years researchers have gravitated to social media platforms, especially Twitter, as fertile ground for empirical analysis of social phenomena. Social media provides researchers access to trace data of interactions and discourse that once went unrecorded in the offline world. Researchers have sought to use these data to explain social phenomena both particular to social media and applicable to the broader social world. This paper offers a minireview of Twitter-based research on political crowd behavior. This literature offers insight into particular social phenomena on Twitter, but often fails to use standardized methods that permit interpretation beyond individual studies. Moreover, the literature fails to ground methodologies and results in social or political theory, divorcing empirical research from the theory needed to interpret it. Rather, papers focus primarily on methodological innovations for social media analyses, but these too often fail to sufficiently demonstrate the validity of such methodologies. This minireview considers a small number of selected papers; we analyze their (often lack of) theoretical approaches, review their methodological innovations, and offer suggestions as to the relevance of their results for political scientists and sociologists.

Mini-review paper, 10 pages. Draft under review

References in corpus (9)

• Finding community structure in networks using the eigenvectors of matrices
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Modern temporal network theory: A colloquium
• Validation of Dunbar's number in Twitter conversations
• P-values: misunderstood and misused
• Generalization of Blonder-Tinkham-Klapwijk theory to particle-hole mixing boundary conditions: π-shift and conductance dips
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Cited by in corpus (5)

• Directions in Abusive Language Training Data: Garbage In, Garbage Out
• Towards a Collective Agenda on AI for Earth Science Data Analysis
• Integrating sentiment and social structure to determine preference alignments: The Irish Marriage Referendum
• Tweeting for the Cause: Network analysis of UK petition sharing
• Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models