[Paper Review] How Twitter affects the perception of public opinion: Two case studies.

Felix Gaisbauer, Armin Pournaki+2 more|arXiv (Cornell University)|Sep 3, 2020

Social Media and Politics4 citations

TL;DR

This paper proposes a network-based analysis of Twitter retweets and replies to study how visibility and intergroup dynamics shape public opinion perception. It finds that right-to-far-right users are more active in retweets and replies, disproportionately amplifying their views while engaging confrontationally with other groups, especially during events like the 2019 Leipzig riots and Saxony state elections.

ABSTRACT

This article analyses public debate on Twitter via network representations of retweets and replies. We argue that tweets observable on Twitter have both a direct and mediated effect on the perception of public opinion. On this basis, we show that through the interplay of the two network representations, it is possible to investigate which opinion groups on the platform primarily shape public opinion, and which ones remain silent to a disproportionate degree. The method is employed to observe public debate about two events: The Saxon state elections and violent riots on New Year's Eve of 2019 in the city of Leipzig. We show that in both cases, (i) different opinion groups exhibit different propensities to get involved in debate, and therefore have unequal impact on public opinion. Users retweeting right to far-right parties and politicians are significantly more active, hence their positions are disproportionately visible. (ii) Said users direct their replies primarily to other opinion groups, hence act more confrontational, while the contrary is not the case.

Motivation & Objective

• To investigate how Twitter's network structures—retweets and replies—affect the perception of public opinion.
• To identify which opinion groups are over- or under-represented in public debate due to network visibility and engagement patterns.
• To examine the role of intergroup dynamics in shaping visibility and tone of discourse on social media.

Proposed method

• Constructs network representations from Twitter data using retweets and replies as edges between users.
• Analyzes user activity levels and engagement patterns across distinct opinion groups.
• Measures the visibility of opinion groups by quantifying retweet frequency and network centrality.
• Compares reply behavior to identify which groups initiate or respond to intergroup communication.
• Uses case studies of the 2019 Leipzig riots and Saxony state elections to validate findings in real-world contexts.
• Applies network analysis techniques to detect imbalances in visibility and confrontation patterns among opinion groups.

Experimental results

Research questions

• RQ1 How do retweet and reply networks on Twitter collectively influence the perception of public opinion?
• RQ2 Which opinion groups are most visible in public debate, and why?
• RQ3 How do engagement patterns differ between opinion groups in terms of activity and intergroup communication?
• RQ4 To what extent do right-to-far-right users shape public opinion perception through network visibility?
• RQ5 Why do some opinion groups remain underrepresented despite participating in discourse?

Key findings

• Right-to-far-right users exhibit significantly higher retweet activity, leading to disproportionate visibility in public opinion perception.
• These users are more likely to reply to other opinion groups, indicating a confrontational communication style.
• Users from other opinion groups show lower reply activity and are less likely to initiate intergroup exchanges.
• The retweet network amplifies the voices of a few active groups, particularly right-to-far-right users, skewing perceived public opinion.
• The reply network reveals asymmetrical engagement, with right-to-far-right users targeting other groups more frequently.
• During both the Leipzig riots and Saxony state elections, the same visibility and engagement imbalances were observed, confirming consistent network effects.