With the growing popularity and ease of access to the internet, the problem of online rumors is escalating. People are relying on social media to gain information readily but fall prey to false information. There is a lack of credibility assessment techniques for online posts to identify rumors as soon as they arrive. Existing studies have formulated several mechanisms to combat online rumors by developing machine learning and deep learning algorithms. The literature so far provides supervised frameworks for rumor classification that rely on huge training datasets. However, in the online scenario where supervised learning is exigent, dynamic rumor identification becomes difficult. Early detection of online rumors is a challenging task, and studies relating to them are relatively few. It is the need of the hour to identify rumors as soon as they appear online. This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, simple, and robust, offers the suitability of being adopted as a tool for online rumor identification.

翻译：随着互联网越来越受欢迎和容易进入互联网,网上流言问题正在加剧。人们依靠社交媒体随时获得信息,但却成为虚假信息的牺牲品。在网上文章一到就发现流言方面缺乏可信度评估技术。现有研究已经制定了若干机制,通过开发机器学习和深层学习算法来打击网上流言。到目前为止,文献为依赖大量培训数据集的流言分类提供了监督框架。然而,在监督学习非常迅速、动态流言识别困难的在线情景中,早期发现网上流言是一项艰巨的任务,而与之相关的研究相对较少。当流言在网上出现时,即需要立即识别这些流言。这项工作提议了一个新的框架,用于利用最新集群技术进行不受监督的流言探测,依靠在线日志的内容和社会特征。拟议架构超越了现有的几个基线,并比若干受监督的技术做得更好。拟议的方法是轻巧、简单、稳健,提供了被采用作为在线流言识别工具的适宜性。