X-posing Free Speech: Examining the Impact of Moderation Relaxation on Online Social Networks (2404.11465v2)

Abstract: We investigate the impact of free speech and the relaxation of moderation on online social media platforms using Elon Musk's takeover of Twitter as a case study. By curating a dataset of over 10 million tweets, our study employs a novel framework combining content and network analysis. Our findings reveal a significant increase in the distribution of certain forms of hate content, particularly targeting the LGBTQ+ community and liberals. Network analysis reveals the formation of cohesive hate communities facilitated by influential bridge users, with substantial growth in interactions hinting at increased hate production and diffusion. By tracking the temporal evolution of PageRank, we identify key influencers, primarily self-identified far-right supporters disseminating hate against liberals and woke culture. Ironically, embracing free speech principles appears to have enabled hate speech against the very concept of freedom of expression and free speech itself. Our findings underscore the delicate balance platforms must strike between open expression and robust moderation to curb the proliferation of hate online.

• The paper reveals that the relaxation of moderation policies led to a 50.5% increase in hate speech, demonstrating harmful content proliferation.
• The paper employs content and network analyses on over 10 million tweets to show how hate communities merge and become more interconnected.
• The paper identifies influential users using a novel Moving PageRank approach, highlighting key nodes that amplify hate speech spread.

Examining the Impact of Moderation Relaxation on Online Social Networks

Introduction

The research explores the consequences of moderation relaxation on social media platforms, using Elon Musk's acquisition of Twitter as a case study. By conducting content and network analyses on a dataset of over 10 million tweets, the study investigates the proliferation of hate speech and the network dynamics that emerge when moderation is relaxed. The findings highlight significant increases in hate speech, particularly targeting marginalized groups, and uncover the formation of cohesive hate communities. These observations underscore the need for social media platforms to balance open discourse with effective moderation to mitigate the spread of harmful content.

Dataset and Methodology

The dataset comprises tweets collected using Twitter's Academic API, focusing on interactions pre- and post-takeover. Data collection was exhaustive, encompassing tweets containing ethnic slurs from a curated list, as well as user timelines of those deemed to post significant hateful content. The timeline spans a month before to a month after the acquisition, providing a comprehensive view of user interactions and content dissemination. The analysis employs several models for hate speech classification and network dynamic evaluations.

Changes in Hate Speech Landscape

The study observes a considerable increase in hate speech, with categories such as racism showing a 50.5% increase in composition post-takeover. The analysis identifies prominent shifts in representative language, with terms like 'n****r' and 'com*ie' increasing in prevalence, highlighting a significant rise in certain forms of hate speech.

Figure 1: 2 weeks before the takeover.

The semantic analysis also reveals a notable association between terms such as 'Free Speech' and rhetoric tied to political agendas, illustrating how certain political ideologies gain traction under relaxed moderation rules (Table \ref{tab:context-table}).

Network Dynamics and Community Formation

Following the takeover, the study shows that hate interaction networks became denser and more interconnected, due in part to influential bridge users. The average growth rate of network nodes doubled post-relaxation, while the number of separate communities decreased by 17%, suggesting a merging of previously isolated communities into larger clusters.

Figure 2: Rate of growth of the average degree centrality of nodes increases by 144.44\% post-takeover.

Figure 3: Rate of growth of the number of connected components decreases by 17.3\% post-takeover.

This enhanced connectivity points to a troubling pattern wherein relaxed moderation catalyzes not just more hate content but also its amplified dissemination through extensive networks.

Identification of Influential Users

The research identifies pivotal nodes in the hate speech network using a novel Moving PageRank (MPR) approach. These users significantly affect information diffusion, acting as network bridges and amplifying connectivity. Analysis of user profiles suggests a predominant presence of far-right and extremist ideologies among influential users, though the spectrum includes various political inclinations.

Figure 4: Number of followers ($\rho = -0.429$).

Interestingly, early detection based on user metrics like follower count proved inadequate, underscoring the necessity of dynamic network analysis for accurate influencer identification. Regression models corroborated this, indicating that content and static metrics alone poorly predict influential user ranks.

Discussion

The relaxation of moderation policies has facilitated an increase in hate speech, spurred integration among hate communities, and ironically, intensified rhetoric opposing open discourse principles. The observable augmentation in offensive language usage highlights the regression to explicit hate speech under the lax governance afforded by moderation relaxations.

The increased politicization and targeting of liberals reflect significant shifts in dialogue dynamics. The merging of disparate hateful communities facilitated by key influencer nodes further demonstrates the necessity of balancing open discourse with robust moderation frameworks.

Conclusion

This examination reveals the critical need for platforms to meticulously balance freedom of expression with proactive moderation strategies. While uninhibited speech promotes diverse viewpoints, it can also lead to unchecked proliferation of harmful rhetoric. Platforms should consider mitigating these risks through counter-speech, community-driven moderation, and strategic influencer management. The insights from this study could guide platforms in striking a nuanced balance between fostering free expression and curbing online hate propagation.