The massive spread of false information on social media has become a global risk, implicitly influencing public opinion and threatening social/political development. False information detection (FID) has thus become a surging research topic in recent years. As a promising and rapidly developing research field, we find that much effort has been paid to new research problems and approaches of FID. Therefore, it is necessary to give a comprehensive review of the new research trends of FID. We first give a brief review of the literature history of FID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in FID, which paves a promising way to tackle FID challenges. Finally, we give our views on the open issues and future research directions of FID, such as model adaptivity/generality to new events, embracing of novel machine learning models, aggregation of crowd wisdom, adversarial attack and defense in detection models, and so on.

中文翻译：

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社交媒体上虚假信息检测的未来

虚假信息在社交媒体上的大量传播已成为全球风险，隐性影响公众舆论并威胁社会/政治发展。虚假信息检测（FID）因此成为近年来蓬勃发展的研究课题。作为一个有前途且发展迅速的研究领域，我们发现人们已经为 FID 的新研究问题和方法付出了很多努力。因此，有必要对 FID 的研究新动向进行全面回顾。我们首先简要回顾了 FID 的文献历史，在此基础上，我们提出了它的几个新的研究挑战和技术，包括早期检测、多模态数据融合检测和解释性检测。我们进一步研究了 FID 中各种人群智能的提取和使用，这为解决 FID 挑战铺平了道路。最后，我们对 FID 的开放性问题和未来研究方向发表了看法，例如模型对新事件的适应性/通用性、新机器学习模型的拥抱、群体智慧的聚合、检测模型中的对抗性攻击和防御等.