LLMs Can Infer Political Alignment from Online Conversations

• URL: http://arxiv.org/abs/2603.11253v2
• Date: Fri, 13 Mar 2026 16:15:23 GMT
• Title: LLMs Can Infer Political Alignment from Online Conversations
• Abstract summary: We show that large language models (LLMs) can reliably infer hidden political alignment.<n>LLMs leverage words that are highly predictive of political alignment while not being explicitly political.
• Score: 8.422083744745324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the correlational structure in our traits such as identities, cultures, and political attitudes, seemingly innocuous preferences like following a band or using a specific slang can reveal private traits. This possibility, especially when combined with massive, public social data and advanced computational methods, poses a fundamental privacy risk. As our data exposure online and the rapid advancement of AI are increasing the risk of misuse, it is critical to understand the capacity of large language models (LLMs) to exploit such potential. Here, using online discussions on DebateOrg and Reddit, we show that LLMs can reliably infer hidden political alignment, significantly outperforming traditional machine learning models. Prediction accuracy further improves as we aggregate multiple text-level inferences into a user-level prediction, and as we use more politics-adjacent domains. We demonstrate that LLMs leverage words that are highly predictive of political alignment while not being explicitly political. Our findings underscore the capacity and risks of LLMs for exploiting socio-cultural correlates.

Related papers

• How Far Will They Go? Red-Teaming Online Influence with Large Language Models [12.2074171577139]
  Large language model (LLM)-based agents increasingly participate in online discourse.<n>We focus on locally deployed open-source LLMs, given their superior alignment with the operational constraints of privacy-conscious malicious actors.<n>We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics.
  arXiv  Detail & Related papers  (2026-05-20T19:25:26Z)
• Are Dilemmas and Conflicts in LLM Alignment Solvable? A View from Priority Graph [50.65575078859993]
  Large Language Models (LLMs) increasingly face conflicts and dilemmas in many scenarios.<n>We model the LLM's preferences to make different choices as a priority graph, where instructions and values are nodes, and the edges represent context-specific priorities determined by the model's output distribution.<n>We propose a runtime verification mechanism, enabling LLMs to query external sources to ground their context and resist manipulation.
  arXiv  Detail & Related papers  (2026-03-16T16:53:08Z)
• Beyond the Link: Assessing LLMs' ability to Classify Political Content across Global Media [37.149543017244234]
  This article evaluates whether large language models (LLMs) can accurately distinguish PC from non-PC using both the text and the URLs of news articles across five countries.<n>Using cutting-edge models, we benchmark their performance against human-coded data to assess whether URL-level analysis can approximate full-text analysis.<n>Our findings show that URLs embed relevant information and can serve as a scalable, cost-effective alternative to discern PC.
  arXiv  Detail & Related papers  (2025-06-20T18:57:43Z)
• Passing the Turing Test in Political Discourse: Fine-Tuning LLMs to Mimic Polarized Social Media Comments [0.0]
  This study explores the extent to which fine-tuned large language models (LLMs) can replicate and amplify polarizing discourse.<n>Using a curated dataset of politically charged discussions extracted from Reddit, we fine-tune an open-source LLM to produce context-aware and ideologically aligned responses.<n>The results indicate that, when trained on partisan data, LLMs are capable of producing highly plausible and provocative comments, often indistinguishable from those written by humans.
  arXiv  Detail & Related papers  (2025-06-17T15:41:26Z)
• Political-LLM: Large Language Models in Political Science [159.95299889946637]
  Large language models (LLMs) have been widely adopted in political science tasks.<n>Political-LLM aims to advance the comprehensive understanding of integrating LLMs into computational political science.
  arXiv  Detail & Related papers  (2024-12-09T08:47:50Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• Large Language Models Reflect the Ideology of their Creators [71.65505524599888]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language.<n>This paper shows that the ideological stance of an LLM appears to reflect the worldview of its creators.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Assessing Political Bias in Large Language Models [0.624709220163167]
  We evaluate the political bias of open-source Large Language Models (LLMs) concerning political issues within the European Union (EU) from a German voter's perspective. We show that larger models, such as Llama3-70B, tend to align more closely with left-leaning political parties, while smaller models often remain neutral.
  arXiv  Detail & Related papers  (2024-05-17T15:30:18Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory [82.7042006247124]
  We show that even the most capable AI models reveal private information in contexts that humans would not, 39% and 57% of the time, respectively. Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.
  arXiv  Detail & Related papers  (2023-10-27T04:15:30Z)
• Privacy in Large Language Models: Attacks, Defenses and Future Directions [84.73301039987128]
  We analyze the current privacy attacks targeting large language models (LLMs) and categorize them according to the adversary's assumed capabilities. We present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks.
  arXiv  Detail & Related papers  (2023-10-16T13:23:54Z)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback [11.895749982167375]
  Large language models (LLMs) are used to generate content for a wide range of tasks, and are set to reach a growing audience in coming years. This intensifies the need to ensure that models are aligned with human preferences and do not produce unsafe, inaccurate or toxic outputs. Personalising LLMs through micro-level preference learning processes may result in models that are better aligned with each user.
  arXiv  Detail & Related papers  (2023-03-09T17:52:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.