Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts.

Social media platforms offer unprecedented opportunities for connectivity and exchange of ideas; however, they also serve as fertile grounds for the dissemination of disinformation. Over the years, there has been a rise in state-sponsored campaigns aiming to spread disinformation and sway public opinion on sensitive topics through designated accounts, known as troll accounts. Past works on detecting accounts belonging to state-backed operations focus on a single campaign. While campaign-specific detection techniques are easier to build, there is no work done on developing systems that are campaign-agnostic and offer generalized detection of troll accounts unaffected by the biases of the specific campaign they belong to.

Social media platforms have been establishing content moderation guidelines and employing various moderation policies to counter hate speech and misinformation. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications, to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community. To this end, we study and analyze fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over two hundred interdisciplinary research papers about moderation strategies. We identify the differences between the content moderation employed in mainstream and fringe social media platforms. Finally, we have in-depth applied discussions on both research and practical challenges and solutions.

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.

Twitter bots have evolved from easily-detectable, simple content spammers with bogus identities to sophisticated players embedded in deep levels of social networks, silently promoting affiliate campaigns, marketing various products and services, and orchestrating or coordinating political activities. Much research has been reported on building accurate machine learning classifiers to identifying bots in social networks; recent works on social bots have started the new line of research on the existence, placement, and functions of the bots in a collective manner. In this paper, we study two families of Twitter bots which have been studied previously with respect to spamming activities through advertisement and political campaigns, and perform an evolutionary comparison with the new waves of bots currently found in Twitter. We uncover various evolved tendencies of the new social bots under social, communication, and behavioral patterns. Our findings show that these bots demonstrate evolved core-periphery structure; are deeply embedded in their networks of communication; exhibit complex information diffusion and heterogeneous content authoring patterns; perform mobilization of leaders across communication roles; and reside in niche topic communities. These characteristics make them highly deceptive as well as more effective in achieving operational goals than their traditional counterparts. We conclude by discussing some possible applications of the discovered behavioral and social traits of the evolved bots, and ways to build effective bot detection systems.