Deplatforming Norm-Violating Influencers on Social Media Reduces Overall Online Attention Toward Them (2401.01253v1)

Abstract: From politicians to podcast hosts, online platforms have systematically banned (``deplatformed'') influential users for breaking platform guidelines. Previous inquiries on the effectiveness of this intervention are inconclusive because 1) they consider only few deplatforming events; 2) they consider only overt engagement traces (e.g., likes and posts) but not passive engagement (e.g., views); 3) they do not consider all the potential places users impacted by the deplatforming event might migrate to. We address these limitations in a longitudinal, quasi-experimental study of 165 deplatforming events targeted at 101 influencers. We collect deplatforming events from Reddit posts and then manually curate the data, ensuring the correctness of a large dataset of deplatforming events. Then, we link these events to Google Trends and Wikipedia page views, platform-agnostic measures of online attention that capture the general public's interest in specific influencers. Through a difference-in-differences approach, we find that deplatforming reduces online attention toward influencers. After 12 months, we estimate that online attention toward deplatformed influencers is reduced by -63% (95% CI [-75%,-46%]) on Google and by -43% (95% CI [-57%,-24%]) on Wikipedia. Further, as we study over a hundred deplatforming events, we can analyze in which cases deplatforming is more or less impactful, revealing nuances about the intervention. Notably, we find that both permanent and temporary deplatforming reduce online attention toward influencers; Overall, this work contributes to the ongoing effort to map the effectiveness of content moderation interventions, driving platform governance away from speculation.

• The paper employs a stacked difference-in-differences design to show that deplatforming results in a 63% reduction on Google Trends and a 43% drop on Wikipedia after 12 months.
• The paper uses a longitudinal, quasi-experimental approach analyzing 165 deplatforming events of 101 influencers to capture causal effects.
• The paper reveals that both temporary and permanent bans achieve similar declines in attention, with misinformation spreaders experiencing even greater reductions.

Effects of Deplatforming on Online Attention Toward Norm-Violating Influencers

The paper at hand provides a detailed analysis of the effects of deplatforming influencers on social media and the resultant reduction in online attention toward these individuals. Through a longitudinal, quasi-experimental paper, the authors address the limitations present in previous research, specifically the narrow focus on a few deplatforming events and the exclusive consideration of overt engagement metrics. This paper encompasses a comprehensive examination of 165 deplatforming events involving 101 influencers, using both Google Trends and Wikipedia pageviews as proxies for online attention.

The methodology utilized is a stacked difference-in-differences (DiD) approach, which enables the disentanglement of causal effects from the general social interest. The authors find that, on average, deplatforming results in a $-$63\% reduction in online attention when measured through Google and a $-$43\% reduction on Wikipedia after 12 months. These findings lend robust support to the hypothesis that deplatforming is an effective mitigation strategy to reduce the exposure and influence of norm-violating figures across the digital ecosystem.

The paper also contributes significant insights into the nuanced effectiveness of deplatforming. For instance, both temporary and permanent deplatforms yield similar reductions in attention, suggesting that even time-limited sanctions can effectively curb the influence of controversial figures. Furthermore, influencers associated with misinformation dissemination experience a comparatively larger decrease in attention following deplatforming than those associated with other types of norm violations.

An intriguing aspect of the paper is how it leverages platform-agnostic engagement metrics. Unlike previous studies focused purely on social media metrics such as posts and likes, this research taps into broader digital behavior. Metrics from Google Trends and Wikipedia offer a more holistic measure of public attention, paving the way for future research that extends beyond direct engagement metrics.

The theoretical implications of this research point towards a more strategic approach in content moderation policies. Platforms might consider employing temporary bans more frequently as they appear to suffice in reducing unwanted attention while mitigating potential backlash associated with permanent exclusions. Moreover, the effective targeting of misinformation actors suggests that strategies might need to be tailored according to the type of norm violation to maximize the effectiveness of interventions.

Future research could further expand on these findings by exploring the causal pathways through which deplatformed influencers manage to migrate and rebuild their audience on alternative platforms. Another area for exploration could be the broader societal impact of deplatforming, potentially examining whether it contributes to the fragmentation of discourse across mainstream and alternative digital spaces.

Overall, this paper makes a valuable contribution to the literature on content moderation strategies by empirically demonstrating the efficacy of deplatforming in a comprehensive and data-driven manner. It provides a framework that can be further built upon to adaptively govern online spaces in an era where digital activity is intricately tied to societal discourse.