arXiv:2606. 06715v1 Announce Type: cross Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 14888v1 Announce Type: cross Abstract: Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains.
arXiv:2604. 21334v2 Announce Type: replace Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects?
arXiv:2607. 21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation.
arXiv:2605. 22641v3 Announce Type: replace-cross Abstract: Detecting Schwartz values in political text is difficult because implicit cues often depend on surrounding arguments and fine-grained distinctions between neighboring values.