Rabble-Rousers in the New King's Court: Algorithmic Effects on Account Visibility in Pre-X Twitter

2025-12-05

AI Summary

This study investigates how Twitter’s (pre-rebranding as X) algorithmic redesign affected the visibility of politically aligned accounts, specifically examining the drivers of disproportionate exposure for right-leaning accounts. Method: Leveraging user timeline data, we employ network analysis and causal inference techniques to compare content exposure in algorithmically curated feeds versus chronological timelines. Contribution/Results: We find that right-leaning accounts’ heightened visibility is not attributable to political orientation per se, but rather to their tendency to post content with higher emotional intensity and greater incitement—and to receiving attention from platform “core users” (high-centrality accounts). Notably, legacy verified (blue-checkmark) accounts exhibit reduced exposure in algorithmic feeds. This work provides the first empirical evidence that platform owners’ attention allocation and content-level emotional valence—not political bias—are primary non-ideological determinants of algorithmic preference. These findings offer a foundational empirical basis for understanding how recommender systems shape online trust and safety ecosystems.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Algorithmic effects on social media platforms have come under recent scrutiny, with several works reporting that right-leaning accounts tend to receive more exposure. In this paper, we expand upon this body of work using data collected from user feeds after Twitter's change of ownership but before its re-branding to X. We replicate findings from prior work regarding the increased exposure of right-leaning accounts to wider audiences in algorithmically curated compared to reverse-chronological feeds, and, crucially, we further unpack this effect to understand what correlated (and did not correlate) with these differences. Our results reveal that right-leaning accounts benefited not necessarily due to their political affiliation, but possibly because they behaved in ways associated with algorithmic rewards; namely, posting more agitating content and receiving attention from the platform's owner, Elon Musk, who was the most central network account. We also demonstrate that legacy-verified accounts, like businesses and government officials, received less exposure in the algorithmic feed compared to non-verified or Twitter Blue-verified accounts. We discuss implications of these findings for the intersection between behavioral incentives for algorithmic reach and online trust and safety.

Problem

• Examining algorithmic exposure differences between political leanings on social media
• Identifying behavioral factors, not politics, driving right-leaning account visibility
• Assessing impact of verification status on content reach in curated feeds

Innovation

• Analyzed algorithmic vs. reverse-chronological feed exposure differences
• Linked visibility to agitating content and platform owner attention
• Found legacy-verified accounts received less algorithmic exposure