Too Small to Fail: Information Sharing Behavior in a US Municipal Election

Ziqian Song, Liuqing Li, Edward Fox

Abstract

Proximal communities in democratic societies comprise citizens, organizations, and governmental agencies, working together to identify and evaluate problems and opportunities in the public interest, build consensus around alternative approaches and solutions, and implement agreed upon policies. Communication and information sharing is critical to performing this collective work that involves not only face-to-face communication, but diverse media and technology whether offline (i.e., broadcast and print media) or online. We used topic modeling, social graphing and sentiment analysis to analyze information sharing behavior among individuals and organizations related to a geographic community and environs during municipal and state assembly elections in 2015. We investigate tweets, and Facebook posts and comments related to these elections as evidence for information sharing at the local level and/or of content relevant to the local community. Our findings suggest that the abundance of elections-relevant topics indicates that Twitter and FB were actively used for information sharing. The greater trust in local as opposed to non-local content and sources established in prior studies is consistent with our community level data in which sentiment expressed in our data is predominantly neutral. We argue that the greater trust in local as opposed to national sources of news, and in social media based on local social networks makes community-level groups and information sharing self-correcting and resilient, and thus, too small to fail.

Andrea L. Kavanaugh, Ziqian Song, Liuqing Li, Edward A. Fox, Bethany Hsiao: Too Small to Fail: Information Sharing Behavior in a US Municipal Election. DG.O 2020: 41-51