Research on Knowledge-Fusion-Based Fake News Data Mitigation Strategy

In recent years, with the innovation of Internet technology and the deep popularization of mobile Internet, it has become extremely convenient for people to obtain, publish and disseminate information. At the same time, the number of users of social networking platforms has shown a rapid growth, covering the world. People’s dependence on social platforms has deepened, and many people even use it as the preferred channel for obtaining information. However, the drawbacks of the Internet and social platforms being flooded with a large amount of false news are gradually becoming prominent, which not only damages the network ecology, but also causes a series of social problems. In view of the fact that it is very difficult for ordinary people to accurately distinguish the massive information on the Internet and are easily misled and deceived, people in related industries and research circles have called for the establishment of an automatic detection framework based on new artificial intelligence technologies to enable the governance of online false news. This study will be carried out by integrating knowledge into the study of false news mitigation. Knowledge fusion refers to the knowledge that integrates more information content within a unit time and density. Knowledge fusion includes but is not limited to language knowledge, world knowledge, domain knowledge, etc. False news mitigation refers to further improving the ability to mitigate false news through factual error correction, two - way factual error correction, and nonfactual content confrontation. Therefore, the focus of this study is to introduce knowledge fusion, improve the ability to mitigate false news through false news detection, factual error correction, and non-factual content confrontation.