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We present a dataset of video descriptions, comments, and user statistics, from the social media platform TikTok, centred around the invasion of Ukraine in 2022, an event that launched TikTok into the geopolitical arena. User activity on the platform around the invasion exposed myriad political behaviours and dynamics previously unexplored on this platform. To this end, we provide a mass-scale language and interaction dataset for further research into these processes. In this paper we conduct an initial investigation of language and social interaction dynamics, alongside an evaluation of bot detection on the platform. We have open-sourced the dataset and the library used to collect it to the public.

Uses of pejorative expressions can be benign or actively empowering. When models for abuse detection misclassify these expressions as derogatory, they inadvertently censor productive conversations held by marginalized groups. One way to engage with non-dominant perspectives is to add context around conversations. Previous research has leveraged user- and thread-level features, but it often neglects the spaces within which productive conversations take place. Our paper highlights how community context can improve classification outcomes in abusive language detection. We make two main contributions to this end. First, we demonstrate that online communities cluster by the nature of their support towards victims of abuse. Second, we establish how community context improves accuracy and reduces the false positive rates of state-of-the-art abusive language classifiers. These findings suggest a promising direction for context-aware models in abusive language research.

In this paper we introduce Reddit legends, a collection of venerated old posts that have become famous on Reddit. To establish the utility of Reddit legends for both computational science/HCI and folkloristics, we investigate two main questions: (1) whether they can be considered folklore, i.e. if they have consistent form, cultural significance, and undergo spontaneous transmission, and (2) whether they can be studied in a systematic manner. Through several subtasks, including the creation of a typology, an analysis of references to Reddit legends, and an examination of some of the textual characteristics of referencing behaviour, we show that Reddit legends can indeed be considered as folklore and that they are amendable to systematic text-based approaches. We discuss how these results will enable future analyses of folklore on Reddit, including tracking subreddit-wide and individual-user behaviour, and the relationship of this behaviour to other cultural markers.

The use of online user traces for studies of human mobility has received significant attention in recent years. This growing body of work, and the more general importance of human migration patterns to government and industry, motivates the need for a formalized approach to the computational modeling of human mobility - in particular how and when individuals change their place of residence - from online traces. Prior work on this topic has skirted the underlying computational modeling of residence inference, focusing on migration patterns themselves. As a result, to our knowledge, all prior work has employed heuristics to compute something like residence histories. Here, we formalize the residence assignment problem, which seeks, under constraints associated with the minimum length-of-stay at a residence, the most parsimonious sequence of residence periods and places that explains the movement history of an individual. Here we provide an exact solution for this problem and establish its algorithmic complexity. Because the calculation of optimal residence histories (under the assumptions of the model) is tractable, we believe that this method will be a valuable tool for future work on this topic.

On the topic of journalistic integrity, the current state of accurate, impartial news reporting has garnered much debate in context to the 2016 US Presidential Election. In pursuit of computational evaluation of news text, the statements (attributions) ascribed by media outlets to sources provide a common category of evidence on which to operate. In this paper, we develop an approach to compare partisan traits of news text attributions and apply it to characterize differences in statements ascribed to candidate, Hilary Clinton, and incumbent President, Donald Trump. In doing so, we present a model trained on over 600 in-house annotated attributions to identify each candidate with accuracy > 88%. Finally, we discuss insights from its performance for future research., Comment: Honours Thesis completed for McGill University B.Sc. Software Engineering Supervised under Professor Derek Ruths Network Dynamics Lab

Harassing and hateful speech in online spaces has become a common problem for platform maintainers and their users. The toxicity created by such content can discourage user participation and engagement. Therefore, it is crucial for and a common goal of platform managers to diminish hateful and harmful content. Over the last year, Reddit, a major online platform, enacted a policy of banning sub-communities (subreddits) that they deem harassing, with the goal of diminishing such activities. We studied the effects of banning the largest hateful subreddit (r/fatpeoplehate or FPH) on the users and other subreddits that were associated with it. We found that, while a number of outcomes were possible --- in this case the subreddit ban led to a sustained reduced interaction of its members (FPH users) with the Reddit platform. We also found that the many counter-actions taken by FPH users were short-lived and promptly neutralized by both Reddit administrators and the admins of individual subreddits. Our findings show that forum-banning can be an effective means by which to diminish objectionable content. Moreover, our detailed analysis of the post-banning behavior of FPH users highlights a number of the behavioral patterns that banning can create., Comment: 10 pages

Online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches., Comment: 9 pages, presented at the first workshop on Text Analytics for Cybersecurity and Online Safety (TA-COS), collocated with LREC 2016