arXiv:2608.29198v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment...
arXiv:2609.14699v1 Announce Type: new Abstract: Visual content shapes audience perception and opinion on social media, and computational social science increasingly relies on automated tools to analy...
MemeLens is a unified multilingual, multitask Vision‑Language Model designed to improve meme understanding across a wide range of tasks such as hate, misogyny, propaganda, sentiment, and humour. The authors consolidated 38 public meme datasets, mapping their labels into a shared taxonomy of 20 tasks covering harm, targets, figurative intent, and affect, and conducted extensive experiments to show that multimodal training and a unified approach outperform fine‑tuning on individual datasets. All experimental resources, the model, and the datasets are released publicly for community use.
arXiv:2605.27388v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as proxies for computational social analysis, yet faithfully representing the "thick description...
arXiv:2505. 24539v4 Announce Type: replace-cross Abstract: We present a study on how and where personas -- defined by distinct sets of human characteristics, values, and beliefs -- are encoded in the representation space of large language models (LLMs).
arXiv:2611.06848v1 Announce Type: new Abstract: Common methods for monitoring features in large language models (LLMs) such as linear probes and sparse autoencoders (SAEs) assume that features mainta...
Common methods for monitoring features in large language models (LLMs) such as linear probes and sparse autoencoders (SAEs) assume that features maintain a consistent representation across contexts. L...
The study examines how political narratives formed and spread on X, Facebook, and Reddit during the 2024 Moldovan presidential election. Using multilingual stance detection, network analysis, and causal inference, it finds distinct cross‑platform differences in positions on European integration, Russia, and the candidates, and shows that amplification and peer exposure significantly reinforce these narratives. This is the first comprehensive analysis of social media discourse for this election, highlighting the role of language, network dynamics, and causal effects in shaping political influence.
arXiv:2610.16498v1 Announce Type: cross Abstract: Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emot...
arXiv:2609.38256v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's...
arXiv:2609.00491v1 Announce Type: new Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect larg...
The study examines how intergroup identity—specifically shared gender or age—affects commenters’ social judgments and empathy in the r/AmITheAsshole subreddit. Analyzing 126,716 comments on 36,163 posts, the authors find that ingroup commenters are more likely to give a ‘Not the Asshole’ verdict and display greater text-based understanding, as measured by large language models. The research also notes age differences among commenters and shows that the ingroup effect is moderated by the relationship between the poster and another party mentioned in the conflict.