community managers

Abstract: Did people stop asking other people for advice online once generative AI could answer their questions? Prior work on ChatGPT's effect on online help-seeking disagrees in both size and sign, in part because no study has compared affected communities against similar communities that AI cannot easily substitute for, over the same months. In this paper, we track monthly post counts in 26 Reddit informational communities against 90 size-comparable hobby communities over the same six calendar months before and after the launch of ChatGPT. We also repeat the entire analysis at 66 earlier dates, before ChatGPT existed, to see what our method reports when no ChatGPT-effect exists. We find that informational help-seeking did not decline. Our results rule out any decline in posting larger than 3.4%, far smaller than the 8% to 25% declines documented in prior work. Steady post counts could still be misleading if AI-written posts had replaced human ones. We test this possibility by scoring 274,411 posts and 223,775 comments with AI-text detectors, compared in a way that cancels out detector false-positives on human-written text. AI-written posts rose only 2-3 percentage points more in informational communities than in hobby communities, short of the 5.1 points that would be needed to hide even the smallest decline previously reported for Reddit. In addition, the comments people receive show no such rise at all. Why, then, do published studies disagree? Reddit community types were already drifting apart before ChatGPT existed, at rates comparable to every published estimate, and without same-time controls, that drift can look like an effect of generative AI. Our own largest estimate, an 18% fall in posts to low-stakes curiosity communities, matches its pre-existing trend. Humans still ask humans for help, and, as far as detection can tell, humans still answer them.

Abstract: With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals' vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as a driver of the spread of false claims, they often fail to explicitly integrate personal ideological biases into belief formation. In this work, we explore how misinformation spreads through the interaction between individuals' ideological biases and social influence. Our model accounts for both the strength of individuals' ideological biases and the extent to which a false claim aligns with their ideology. Social influence modifies the effects of ideological intensity and false claim alignment through network interactions. Notably, the influence of neighbours' ideological intensity on belief is strongly affected by how well those neighbours are connected to one another. These results highlight the importance of considering both network structure and personal ideological biases when modelling misinformation propagation.

Abstract: Otome games, a romance simulation genre primarily targeting female, have emerged as a major force in the global gaming market, attracting hundreds of millions of players and billions in revenue. Despite their popularity, otome game communities face pervasive online toxicity, which has been largely unexplored. In this work, we present the first large-scale measurement of toxicity in otome game communities across social platforms. We introduce OtomeSCAN, a framework for collecting, evaluating, and analyzing 620,045 posts from Weibo and Reddit spanning 18 months. To support robust analysis, we manually annotated a ground-truth dataset of 4,308 posts, identifying eight target groups such as players and game developers. We evaluate seven toxicity detectors on the dataset, including general-purpose models and our proposed LLM-based detectors, with our best model achieving F1-scores of 0.82 (Weibo) and 0.78 (Reddit). Our analysis reveals significant platform-based differences in toxicity: 22.20% of otome-related posts on Weibo are toxic, compared to 3.71% on Reddit. Besides, real-world events like in-community conflicts can rapidly escalate toxicity, with toxicity ratios increasing to 37.09% in just 72 hours during an external attack on Weibo. We also flag 191 potential-coordination clusters in otome game communities, 64.40% of which target game developers, with several accounts participating repeatedly across multiple clusters. We hope our work inspires further research on community-specific toxicity and contributes to building healthier online spaces for marginalized gaming communities.