People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection

NLP models are used in a variety of critical social computing tasks, such as detecting sexist, racist, or otherwise hateful content. Therefore, it is imperative that these models are robust to spurious features. Past work has attempted to tackle such spurious features using training data augmentation, including Counterfactually Augmented Data (CADs). CADs introduce minimal changes to existing training data points and flip their labels; training on them may reduce model dependency on spurious features. However, manually generating CADs can be time-consuming and expensive. Hence in this work, we assess if this task can be automated using generative NLP models. We automatically generate CADs using Polyjuice, ChatGPT, and Flan-T5, and evaluate their usefulness in improving model robustness compared to manually-generated CADs. By testing both model performance on multiple out-of-domain test sets and individual data point efficacy, our results show that while manual CADs are still the most effective, CADs generated by ChatGPT come a close second. One key reason for the lower performance of automated methods is that the changes they introduce are often insufficient to flip the original label.

Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection

Counterfactually Augmented Data (CAD) aims to improve out-of-domain generalizability, an indicator of model robustness. The improvement is credited with promoting core features of the construct over spurious artifacts that happen to correlate with it. Yet, over-relying on core features may lead to unintended model bias. Especially, construct-driven CAD -- perturbations of core features -- may induce models to ignore the context in which core features are used. Here, we test models for sexism and hate speech detection on challenging data: non-hateful and non-sexist usage of identity and gendered terms. In these hard cases, models trained on CAD, especially construct-driven CAD, show higher false-positive rates than models trained on the original, unperturbed data. Using a diverse set of CAD -- construct-driven and construct-agnostic -- reduces such unintended bias.

Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions

The disruptive offline mobilization of participants in online conspiracy theory (CT) discussions has highlighted the importance of understanding how online users may form radicalized conspiracy beliefs. While prior work researched the factors leading up to joining online CT discussions and provided theories of how conspiracy beliefs form, we have little understanding of how conspiracy radicalization evolves after users join CT discussion communities. In this paper, we provide the empirical modeling of various radicalization phases in online CT discussion participants. To unpack how conspiracy engagement is related to radicalization, we first characterize the users' journey through CT discussions via conspiracy engagement pathways. Specifically, by studying 36K Reddit users through their 169M contributions, we uncover four distinct pathways of conspiracy engagement: steady high, increasing, decreasing, and steady low. We further model three successive stages of radicalization guided by prior theoretical works. Specific sub-populations of users, namely those on steady high and increasing conspiracy engagement pathways, progress successively through various radicalization stages. In contrast, users on the decreasing engagement pathway show distinct behavior: they limit their CT discussions to specialized topics, participate in diverse discussion groups, and show reduced conformity with conspiracy subreddits. By examining users who disengage from online CT discussions, this paper provides promising insights about conspiracy recovery process.

Characterizing Social Imaginaries and Self-Disclosures of Dissonance in Online Conspiracy Discussion Communities

Online discussion platforms offer a forum to strengthen and propagate belief in misinformed conspiracy theories. Yet, they also offer avenues for conspiracy theorists to express their doubts and experiences of cognitive dissonance. Such expressions of dissonance may shed light on who abandons misguided beliefs and under which circumstances. This paper characterizes self-disclosures of dissonance about QAnon, a conspiracy theory initiated by a mysterious leader Q and popularized by their followers, anons in conspiracy theory subreddits. To understand what dissonance and disbelief mean within conspiracy communities, we first characterize their social imaginaries, a broad understanding of how people collectively imagine their social existence. Focusing on 2K posts from two image boards, 4chan and 8chan, and 1.2 M comments and posts from 12 subreddits dedicated to QAnon, we adopt a mixed methods approach to uncover the symbolic language representing the movement, expectations, practices, heroes and foes of the QAnon community. We use these social imaginaries to create a computational framework for distinguishing belief and dissonance from general discussion about QAnon. Further, analyzing user engagement with QAnon conspiracy subreddits, we find that self-disclosures of dissonance correlate with a significant decrease in user contributions and ultimately with their departure from the community. We contribute a computational framework for identifying dissonance self-disclosures and measuring the changes in user engagement surrounding dissonance. Our work can provide insights into designing dissonance-based interventions that can potentially dissuade conspiracists from online conspiracy discussion communities.

"Unsex me here": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples

To effectively tackle sexism online, research has focused on automated methods for detecting sexism. In this paper, we use items from psychological scales and adversarial sample generation to 1) provide a codebook for different types of sexism in theory-driven scales and in social media text; 2) test the performance of different sexism detection methods across multiple data sets; 3) provide an overview of strategies employed by humans to remove sexism through minimal changes. Results highlight that current methods seem inadequate in detecting all but the most blatant forms of sexism and do not generalize well to out-of-domain examples. By providing a scale-based codebook for sexism and insights into what makes a statement sexist, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.