VRoC：变分自编码器辅助的基于文本的多任务谣言分类器
VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text
摘要：
社交媒体变得流行起来，几乎影响了我们日常生活的方方面面。虽然网上发帖对个人用户来说非常方便，但它也助长了各种谣言的快速传播。谣言的迅速和广泛传播会造成持续的不利或有害影响。因此，研究者们在减少谣言的负面影响方面投入了大量的精力。为此，谣言分类系统旨在检测，跟踪和验证社交媒体中的谣言。这样的系统通常包括四个组件：（i）谣言检测器，（ii）谣言跟踪器，（iii）立场分类器，以及（iv）真实性分类器。为了提高谣言检测、跟踪和验证的最新水平，我们提出了一种基于推特级变分自编码器的谣言分类系统VRoC。VRoC由一个协同训练引擎组成，该引擎训练变分自编码器（VAE）和谣言分类组件。协同训练引擎帮助VAE调整它们的潜在表示，使其对分类器友好。我们还表明，VRoC能够以高精度对看不见的谣言进行分类。对于PHEME数据集，VRoC在宏观F1评分方面，在观察到的和未观察到的谣言上，始终优于几种最先进的技术，最高可达26.9%。
英文摘要：
Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause persistent adverse or detrimental impacts. Therefore, researchers invest great efforts on reducing the negative impacts of rumors. Towards this end, the rumor classification system aims to detect, track, and verify rumors in social media. Such systems typically include four components: (i) a rumor detector, (ii) a rumor tracker, (iii) a stance classifier, and (iv) a veracity classifier. In order to improve the state-of-the-art in rumor detection, tracking, and verification, we propose VRoC, a tweet-level variational autoencoder-based rumor classification system. VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components. The co-train engine helps the VAEs to tune their latent representations to be classifier-friendly. We also show that VRoC is able to classify unseen rumors with high levels of accuracy. For the PHEME dataset, VRoC consistently outperforms several state-of-the-art techniques, on both observed and unobserved rumors, by up to 26.9%, in terms of macro-F1 scores.
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。
