Islam Md Rafiqul, Liu Shaowu, Wang Xianzhi, Xu Guandong
Advanced Analytics Institute (AAi), University of Technology Sydney (UTS), Sydney, Australia.
School of Computer Science, University of Technology Sydney (UTS), Sydney, Australia.
Soc Netw Anal Min. 2020;10(1):82. doi: 10.1007/s13278-020-00696-x. Epub 2020 Sep 29.
Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.
最近，诸如脸书、推特和新浪微博等社交网络的使用已成为我们日常生活中不可或缺的一部分。它被视为用户分享个人信息、图片和视频的便捷平台。然而，尽管人们享受社交网络带来的便利，但许多欺骗性活动，如假新闻或谣言，可能会误导用户相信错误信息。此外，在社交网络中传播大量错误信息已成为一个全球性风险。因此，社交网络中的错误信息检测（MID）受到了广泛关注，并被视为一个新兴的研究领域。我们发现，与MID相关的几项研究已经针对新的研究问题和技术展开。然而，虽然很重要，但错误信息的自动检测却很难实现，因为它需要先进的模型来理解与真实信息相比，所报道的信息有多相关或多不相关。现有研究主要集中在三大类错误信息：虚假信息、假新闻和谣言检测。因此，针对上述问题，我们对自动错误信息检测进行了全面综述，内容包括（i）虚假信息、（ii）谣言、（iii）垃圾信息、（iv）假新闻和（v）虚假信息。我们对使用深度学习（DL）自动处理数据并创建模式以做出决策的MID进行了前沿综述，这不仅是为了提取全局特征，也是为了取得更好的结果。我们进一步表明，DL是用于前沿MID的一种有效且可扩展的技术。最后，我们提出了几个目前限制实际应用的开放问题，并指出了这方面未来的发展方向。
