Social media users and inauthentic accounts, such as bots, may coordinate in promoting their topics. Such topics may give the impression that they are organically popular among the public, even though they are astroturfing campaigns that are centrally managed. It is challenging to predict if a topic is organic or a coordinated campaign due to the lack of reliable ground truth. In this paper, we create such a ground truth by detecting the campaigns promoted by ephemeral astroturfing attacks. These attacks push any topic to Twitter’s (X) trends list by employing bots that tweet in a coordinated manner within a short period and then immediately delete their tweets. We also manually curate a dataset of organic Twitter trends. We then create engagement networks out of these datasets which can serve as a challenging testbed for graph classification task to distinguish between campaigns and organic trends. Engagement networks consist of users as nodes and edges indicate engagements (retweets, replies, and quotes) between users. We release the engagement networks for 179 campaigns and 135 non-campaigns, and also provide finer-grain labels to characterize the type of the campaigns and non-campaigns. Our dataset, LEN (Large Engagement Networks), in the URL below. In comparison to traditional graph classification datasets, which are small with tens of nodes and hundreds of edges at most, graphs in LEN are larger. The average size of a graph in LEN has ∼11K nodes and ∼23K edges. We show that state-of-the-art GNN methods give only mediocre results for campaign vs. non-campaign and campaign type classification on LEN. LEN offers a unique and challenging playfield for the graph classification problem. We believe that LEN will help advance the frontiers of graph classification techniques on large networks and also provide an interesting use case in terms of distinguishing coordinated campaigns and organic trends.

The incel (involuntary celibate) community is a radicalized online subculture. Understanding its dynamics is crucial for mitigating youth radicalization and preventing online polarization. In this study, we examine Reddit communities to identify users at risk of deep engagement in incel-related subreddits. We analyze activity patterns and comments of 14,000 users and employ a two-step approach to carefully prepare a custom set of features to identify at-risk users. We first consider 7,000 incel-engaged users and use them to select 7,000 control users who are similar to the incel-engaged users in terms of non-incel activities. Recent subreddit activity patterns of those users are used to create features. We then use word2vec on the comment texts to create text-based features. We find that utilizing only the subreddit activity patterns of users achieves an accuracy of 79% while using word2vec modeling alone yields a classification accuracy of 76%. Remarkably, the two approaches have complementary strengths and integrating both approaches achieves a near-perfect classification accuracy of 99.8%. By employing a two-pronged approach, our results achieve a significant increase over previous work. By illuminating social media’s role in online radicalization processes, we hope that the insights from our work can guide policymakers and platform moderators in creating safer online spaces.