The move of propaganda and disinformation to the online environment is possible thanks to the fact that within the last decade, digital information channels radically increased in popularity as a news source. The main advantage of such media lies in the speed of information creation and dissemination. This, on the other hand, inevitably adds pressure, accelerating editorial work, fact-checking, and the scrutiny of source credibility. In this chapter, an overview of computer-supported approaches to detecting disinformation and manipulative techniques based on several criteria is presented. We concentrate on the technical aspects of automatic methods which support fact-checking, topic identification, text style analysis, or message filtering on social media channels. Most of the techniques employ artificial intelligence and machine learning with feature extraction combining available information resources. The following text firstly specifies the tasks related to computer detection of manipulation and disinformation spreading. The second section presents concrete methods of solving the tasks of the analysis, and the third sections enlists current verification and benchmarking datasets published and used in this area for evaluation and comparison.
翻译:在过去十年中,数字信息渠道作为新闻来源的受欢迎程度急剧增加,从而有可能将宣传和假信息转移到网上环境,因为数字信息渠道作为新闻来源的受欢迎程度急剧提高,这种媒体的主要优势在于信息创造和传播的速度,另一方面,这不可避免地增加了压力,加快编辑工作、进行事实检查和对来源可信度的检查。本章概述了计算机支持的根据若干标准发现假信息和操纵技术的方法。我们集中研究自动方法的技术方面,这些自动方法支持事实核对、专题识别、文本样式分析或信息在社交媒体渠道的过滤。大多数技术都采用人工智能和机器学习,并结合现有信息资源进行特征提取。下面的文字首先具体说明了计算机探测操纵和错误信息传播的任务。第二节介绍了解决分析任务的具体方法,第三节介绍了目前为评价和比较而公布的和使用的核查和基准数据集。