论文标题
情绪引导的跨域假新闻检测使用对抗领域适应
Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
论文作者
论文摘要
关于假新闻检测的最新作品表明，将情感用作功能或基于情感的功能以提高性能的功效。但是，这些情绪引导特征在跨域设置中对假新闻检测的影响（我们面临域转移的问题）仍然在很大程度上尚未探索。在这项工作中，我们评估了情感引导特征对跨域假新闻检测的影响，并进一步提出了一种使用对抗性学习的情感引导，域自适应方法。我们证明了情感引导模型在跨域设置中的功效，从fakenewsamt，Celeb，Politifact和Gossipcop数据集的各种源和目标数据集组合。
Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.
