It is found that LLMs outperform expectations at replicating comment structure and formality, but struggle to accurately capture nuanced emotions, e.g. understating joy and overstating anger.
Abstract
Online communities face a constant battle against toxicity and misinformation. While human moderators struggle to keep pace with the volume of content, LLMs offer a promising solution for automatically generating constructive responses and shaping online interactions. This paper preliminarily investigates if LLMs can mimic the communication styles of Reddit users using their comment history as context. We evaluate two prompting approaches: predicting a target comment and filling in masked comments. We find that LLMs outperform expectations at replicating comment structure and formality, but struggle to accurately capture nuanced emotions, e.g. understating joy and overstating anger. These findings highlight a promising direction for LLMs in guiding online conversations towards prosociality influencing emergent communication patterns and norms within the community. The results of our study inspire future work with more rigorous methods of evaluation to explore the LLMs'effectiveness across diverse online communities to better understand their broader societal impact.
Fringe online message boards are often studied in the context of the extreme ideology that they produce. So far, however, not much of this research has focused on direct real-world harm in the all-too-common form of collective harassment. We directly analyze the complex dynamics of KiwiFarms, an online message board de...
Understanding the dynamics of stance change on social media is crucial for addressing polarization and information integrity, yet observational studies face challenges including limited experimental control, restricted data access, and algorithmic confounds. We leverage Generative Agent-Based Modeling (GABM)—a novel si...
Valerio La Gatta, Gian Marco Orlando, Marco Perillo et al.· Proceedings of the 37th ACM...· 0 citations
A framework that disentangles two distinct triggers of political sycophancy: opinion (aligning with explicit narratives) and identity (stereotyping based on demographic labels) is introduced, highlighting how personalization may amplify identity- or opinion-conditioned shifts in the model's behaviors.
Li-Ni Fu, Chang-Chih Meng, Chien-Hua Chen et al.· 0 citations
A dual-level evaluation framework to assess LLM-based agents at both the individual and collective levels is proposed, finding that while agents capture broad partisan orientations, they underestimate within-group variability and reproduce stereotypical ideological biases.
M. Al Ali, Filip Mihai Muntean, Lucia Donatelli et al.· International Conference on...· 1 citation
This work manually analyzed a forum discussion, synthesized an exploratory analysis framework from relevant literature, built a design probe, and interviewed 21 researchers to uncover perceived opportunities and barriers with LLM representations of collective discussions.
Tony W. Li, Zhi-Qing Wang, T. Tran et al.· Proceedings of the 14th ACM...· 0 citations
Public support for climate action hinges on the integrity of information environments. In an online experiment, U.S. adults used ChatGPT to evaluate climate-related claims. We first conducted computational text analysis of GPT conversation logs, assessing valence, formality, bias, recency, authority cues, and semantic...
S. Tsang, Dan-Dan Wang· PLOS Climate· 0 citations
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