Deplatforming Norm-Violating Influencers on Social Media Reduces Overall Online Attention Toward Them

TL;DR

This study investigates whether deplatforming influential users reduces their online attention. It analyzes 165 deplatforming events across 101 influencers by building a dataset from Reddit and linking each entity to Google Trends and Wikipedia pageviews via Google Knowledge Graph IDs, enabling cross-platform, passive attention analysis. Employing a stacked difference-in-differences design, the authors find substantial declines in attention 12 months post-deplatforming: about -63% on Google Trends and -43% on Wikipedia, with larger effects for high-attention and misinformation-based bans and no significant difference between temporary and permanent bans. The results contribute to platform governance by demonstrating that sanctions on influencers can meaningfully reduce broader online attention, while also underscoring the importance of considering external events and cross-platform dynamics in moderation research.

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.

Figures (13)

• Figure 1: Our approach to studying deplatforming. We obtain deplatforming events and entities from news shared on Reddit, e.g., Twitter bans Alex Jones and InfoWars; cites abusive behaviorschneider2018twitter. Then, we extract the Google Knowledge Graph identifier of these entities (e.g., Alex Jones corresponds to /m/01_6j_) and obtain digital attention traces from Wikipedia (page views) and Google Trends (search interest). Last, we analyze these time series with descriptive and quasi-experimental methods.
• Figure 2: Overview of our data collection and curation pipeline. Starting from Reddit, we obtain 440 $\langle$entity, platform$\rangle$ pairs, each corresponding to a deplatforming event (Step 1). Then, we link entities to Google Knowledge Graph identifiers, which are subsequently linked to GKG-ids (Step 2) and manually filter, annotate, and expand the data (Step 3). Last, we obtain online traces corresponding to each entity from Wikipedia, Google Trends, and Media Cloud (a source of internet news), performing additional filtering to ascertain the quality of the online traces (Step 4).
• Figure 3: Number of ban groups per merge threshold
• Figure 4: Number of bans per month
• Figure 5: Event-level labels