# Mimicking Human Verification Behavior for News Media Credibility Evaluation
## 1 Introduction  
With the widespread use of social networks by the public, the digital dissemination of information has subtly changed the overall structure of the public media space. Users can easily obtain a large amount of information via WeChat, Sina Weibo, personal media, and other network platforms. Meanwhile, with the continuous improvement of modern technologies such as the Internet of Things and its related services [1], the channels for information acquisition and dissemination will become more diverse and convenient. However, while enjoying this convenience, the public must also face a series of problems brought on by purposeful dissemination of information [2]. Nowadays, the spread of false, biased, and promotional content online has made it impossible for fact-checkers to verify every suspicious article. Therefore, many researchers are shifting their attention to analyzing entire news organizations, assessing the probability of credibility of the content at the time of news release by examining the reliability of the source of the news. There is currently insufficient in-depth research in this field, which is mainly related to false information, false news detection, and media bias. Relevant researchers in the field of natural language processing have made many attempts to solve this problem, such as stance detection [3], rumor detection [4], and the use of other fact-checking websites such as Snopes, FactCheck, and Politifact [5]. However, the fact verification speed of these two types of methods is far from adequate in addressing the current quantity and dissemination speed of information, and they are not competent in credibility recognition in the early stage of information dissemination and cannot effectively block the impact of the early spread of false information. The majority of audiences base the evaluations of the credibility of media on their own cognitive processes and on information retrieval processing. Additionally, some readers may rely on the results of third-party software evaluation, such as those provided Ad Fontes Media or Media Bias/Fact Check [6]. Although these websites and the human behind them often provide effective credibility labels, they also require subjective explanations from media producers and may not be suitable to other media ecosystems (for instance, the methods used for evaluation in the United States may not be applicable to other countries, such as China), and they can only be expanded at the speed of the human user to evaluate media websites. Therefore, people are increasingly interested in finding more effective computational methods for evaluating media credibility. Inspired by [7], we propose the MiBeMC model to better mimic human behavior in evaluating media credibility on the internet. In creating MiBeMC, we acted from the belief that information modules typically have high affinity and mutual support, which is consistent with previous research on modal similarity or consistency [8; 9]. Figure 1 shows human evaluation media credibility stages on the internet, consisting of multiple components with a news example with two components, i.e., title and body content. The evaluation behavior can be divided into three stages: (1) reading the title and content on a page on a website; (2) after the readers pay attention to the news outlet, they will associate the credibility of the same outlet's news with what they have seen before; and (3) the human user will search for other news on relevant information based on the title. In stage (1), readers generally conduct a preliminary evaluation of the credibility of the information published by the media based on the title and content. In stage (2), readers generally further evaluate the credibility of the media outlet based on historical articles. In stage (3), readers generally search for relevant topic information and complete their final evaluation of media information based on relevant titles and content.