Measuring Alignment of Online Grassroots Political Communities with Political Campaigns

Authors

  • Cameron Raymond Department of Computer Science, University of Toronto Oxford Internet Institute, University of Oxford
  • Isaac Waller Department of Computer Science, University of Toronto
  • Ashton Anderson Department of Computer Science, University of Toronto

Keywords:

Organizational and group behavior mediated by social media; interpersonal communication mediated by social media, Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health

Abstract

Social media reduces barriers for the formation of large, self-organizing grassroots communities. For political campaigns this poses significant opportunities to address declining party membership, but also reputational risks and potential loss of campaign coherence. While balancing these factors is often done informally, we adopt a behavioural approach by using neural community embeddings to evaluate online communities along cultural, political, and demographic dimensions. We apply this technique to the 2020 U.S. Democratic presidential primaries and the website Reddit, providing novel insights into the important tension between campaigns and third-party actors. Using two benchmark comparison classes, we demonstrate that our embedding dimensions mirror their offline analogues, but more so the views of a candidate's supporters than the candidate's themselves. Finally, we introduce temporal aspects to our community embedding to evaluate the stability of political communities and their interrelations. These analyses serve as an exploration and application of our novel embedding methodology, and give insight into the relationship between online communities and the movements they support.

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Published

2022-05-31

How to Cite

Raymond, C., Waller, I., & Anderson, A. (2022). Measuring Alignment of Online Grassroots Political Communities with Political Campaigns. Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 806-816. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/19336