用时间序列双情感分析提升谣言检测准确率
Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments
- 通过多任务后缀学习捕捉谣言演化中的情感变化
- 在四个基准上显著优于现有情感相关方法
- 仅需少量参数微调，适配大模型快速部署
社交媒体上谣言的广泛传播对人们的生活产生重大影响，可能引发公众恐慌。谣言常引发特定情感共鸣，促使用户转发。为有效检测与追踪谣言，必须观察源消息与回应消息对随时间演变的细粒度情感特征。然而，现有方法未充分考虑此方面。本文提出MSuf，首个基于时间序列双（耦合）情感的多任务后缀学习框架，用于谣言检测与追踪。MSuf包含三个模块：(1) 使用LLM提取情感强度特征并按时间排序；(2) 将排序后的情感特征与源文本词嵌入融合，获得对齐嵌入；(3) 将两个硬提示与对齐向量结合，通过一个冻结的LLM同时完成谣言检测与情感分析。实验表明，该方法仅需极小参数微调即可显著提升LLM在谣言检测上的表现。在四个谣言检测基准上评估，相比其他情感相关方法有明显提升。
原文摘要 · Abstract (English)
The widespread dissemination of rumors on social media has a significant impact on people's lives, potentially leading to public panic and fear. Rumors often evoke specific sentiments, resonating with readers and prompting sharing. To effectively detect and track rumors, it is essential to observe the fine-grained sentiments of both source and response message pairs as the rumor evolves over time. However, current rumor detection methods fail to account for this aspect. In this paper, we propose MSuf, the first multi-task suffix learning framework for rumor detection and tracking using time series dual (coupled) sentiments. MSuf includes three modules: (1) an LLM to extract sentiment intensity features and sort them chronologically; (2) a module that fuses the sorted sentiment features with their source text word embeddings to obtain an aligned embedding; (3) two hard prompts are combined with the aligned vector to perform rumor detection and sentiment analysis using one frozen LLM. MSuf effectively enhances the performance of LLMs for rumor detection with only minimal parameter fine-tuning. Evaluating MSuf on four rumor detection benchmarks, we find significant improvements compared to other emotion-based methods.
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