arXiv cs.AI· Andrea Piras, Osman Rahmoun, Elena Zheleva·· 12 小时前AI 评分24
2024 摩尔多瓦总统选举中的政治叙事：立场、放大与影响
Political Narratives in the 2024 Moldovan Presidential Election: Stance, Amplification, and Influence
AI 导读
研究分析 2024 年 8 月至 10 月 X、Facebook 和 Reddit 上的公开帖子，用大语言模型对欧洲一体化、俄罗斯及两位主要总统候选人的立场做多语言立场检测，并在 X 上构建转发网络与语义对齐网络识别叙事的放大与同向框架。基于 proximal 因果推断框架的估计显示，各主要立场类别的平均处理效应均为正且统计显著，表明放大与同伴接触会强化线上政治叙事。
正文 · 原文
Abstract:Social media platforms play a central role in shaping political discourse during elections, particularly in geopolitically contested regions. We study how political narratives emerged, aligned, and propagated during the 2024 Moldovan presidential election by analyzing publicly available posts collected from X, Facebook, and Reddit between August and October 2024. Combining multilingual stance detection, coordination analysis, and causal inference, we examine both descriptive and causal mechanisms underlying online political discourse. First, we apply large language models to classify stance toward European integration, Russia, and the two main presidential candidates, revealing clear cross-platform and cross-linguistic differences in how political positions are expressed. Second, we construct retweet-based and semantic alignment networks on X to identify amplification and aligned framing of dominating narratives. Finally, we use a proximal causal inference framework to estimate peer influence effects in stance formation. Our results indicate consistently positive and statistically significant average treatment effects across major stance categories. These findings provide evidence that amplification and peer exposure contribute to the reinforcement of political narratives online. This work offers the first analysis of social media discourse surrounding the 2024 Moldovan presidential election and demonstrates the value of integrating language-based annotation, network analysis, and causal inference for studying political influence in multilingual and geopolitically sensitive contexts.
| Comments: | 12 pages, 10 figures. To be published in the proceedings of the 13th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2026) |
| Subjects: | Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.22464 [cs.SI] |
| (or arXiv:2610.22464v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.22464 arXiv-issued DOI via DataCite |
Submission history
From: Andrea Piras [view email]
[v1]
Thu, 15 Oct 2026 17:28:47 UTC (2,255 KB)
来源：arXiv cs.AI · arxiv.org