检测社交媒体上的说服：从模型开发到说服策略的洞察
Towards Detecting Persuasion on Social Media: From Model Development to Insights on Persuasion Strategies
AI总结：
本文通过开发轻量级说服文本检测模型（在SemEval 2023任务3子任务3中达到最优性能）并应用于澳大利亚联邦选举2022 Facebook广告数据集，揭示了政治竞选在不同资金策略、词汇选择、人口统计定位和选举临近时说服强度时间变化中的模式。
摘要：
政治广告通过嵌入更广泛宣传策略中的微妙说服技巧，在塑造公众舆论和影响选举结果方面发挥着关键作用。检测这些说服元素对于提高选民意识和确保民主进程的透明度至关重要。本文通过两项相互关联的研究，提出了一种连接模型开发与实际应用的综合方法。首先，我们引入了一个轻量级说服文本检测模型，该模型在SemEval 2023任务3子任务3中达到了最先进性能，同时所需的计算资源和训练数据远少于现有方法。其次，我们通过收集澳大利亚联邦选举2022 Facebook广告（APA22）数据集，对其中一部分进行说服标注，并对模型进行微调以使其从主流新闻适应社交媒体内容，从而展示了该模型的实际效用。然后，我们应用微调后的模型对APA22数据集的其余部分进行标注，揭示了政治竞选如何通过不同的资金策略、词汇选择、人口统计定位以及选举日临近时说服强度的时间变化来利用说服的独特模式。我们的发现不仅强调了分析社交媒体说服时领域特定建模的必要性，还展示了揭示这些策略如何能够增强透明度、告知选民并促进数字竞选中的问责制。
英文摘要：
Political advertising plays a pivotal role in shaping public opinion and influencing electoral outcomes, often through subtle persuasive techniques embedded in broader propaganda strategies. Detecting these persuasive elements is crucial for enhancing voter awareness and ensuring transparency in democratic processes. This paper presents an integrated approach that bridges model development and real-world application through two interconnected studies. First, we introduce a lightweight model for persuasive text detection that achieves state-of-the-art performance in Subtask 3 of SemEval 2023 Task 3 while requiring significantly fewer computational resources and training data than existing methods. Second, we demonstrate the model's practical utility by collecting the Australian Federal Election 2022 Facebook Ads (APA22) dataset, partially annotating a subset for persuasion, and fine-tuning the model to adapt from mainstream news to social media content. We then apply the fine-tuned model to label the remainder of the APA22 dataset, revealing distinct patterns in how political campaigns leverage persuasion through different funding strategies, word choices, demographic targeting, and temporal shifts in persuasion intensity as election day approaches. Our findings not only underscore the necessity of domain-specific modeling for analyzing persuasion on social media but also show how uncovering these strategies can enhance transparency, inform voters, and promote accountability in digital campaigns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
