Social Media Public Opinion as Flocks in a Murmuration: Conceptualizing and Measuring Opinion Expression on Social Media. Issue 1 (21st December 2021)

• Record Type: Journal Article
• Authors: Zhang, Yini; Chen, Fan; Rohe, Karl
• Is Part Of: Journal of computer-mediated communication. Volume 27:Issue 1(2022)
• Publication Date: 2021-12-21
• Subjects: Public Opinion -- Social Media -- Social Network Structure -- Social Network Analysis -- Social Media Data Mining; Telematics -- Periodicals; Computer networks -- Social aspects -- Periodicals; Communication -- Periodicals; 302.20285
• DOI: 10.1093/jcmc/zmab021
• Languages: English
• ISSNs: 1083-6101

Abstract

Abstract: We propose a new way of imagining and measuring opinions emerging from social media. As people tend to connect with like-minded others and express opinions in response to current events on social media, social media public opinion is naturally occurring, temporally sensitive, and inherently social. Our framework for measuring social media public opinion first samples targeted nodes from a large social graph and identifies homogeneous, interactive, and stable networks of actors, which we call "flocks, " based on social network structure, and then measures and presents opinions of flocks. We apply this framework to Twitter and provide empirical evidence for flocks being meaningful units of analysis and flock membership predicting opinion expression. Through contextualizing social media public opinion by foregrounding the various homogeneous networks it is embedded in, we highlight the need to go beyond the aggregate-level measurement of social media public opinion and study the social dynamics of opinion expression using social media. Lay Summary: As people from different backgrounds actively and publicly express opinions on social media, such organic, dynamic, and conversational expression differs from individual preferences gathered by survey-based public opinion polls. The unfolding of public opinion on social media in response to real-world events resembles a murmuration of starlings, whose formation changes fluidly. To capture the unique characteristics of social media public opinion, we propose a measurement framework called "murmuration" that detects meaningful "flocks" (i.e., networks) of accounts based on social network structure and examines the opinion expression of those flocks. We demonstrate the effectiveness of this framework in identifying homogeneous, interactive, and stable flocks of social media users and revealing distinct temporal and content patterns of opinion expression by different flocks. This work shows how opinion expression is tied to one's social network on social media and can shed light on the dynamics of interaction between different groups of social actors. The results also inform social media opinion measurement: to measure social media public opinion, texts should be combined with social network structure so that opinion expression can be disaggregated and situated in its online social context.