Design and Implementation of Rumor Refuting and Accountability System Based on Deep Learning and Graphic Database
- 10.2991/978-94-6463-024-4_34How to use a DOI?
- Deep learning; Graphic databases; Refuting rumors; Accountability system
With the popularity of social networks, online rumors are increasing day by day. Internet rumors are harmful, spread fast, and will cause great loss to personal interests and social interests. In order to suppress the spread of online rumors and hold those who spread rumors accountable, we designed and implemented a system to refute and hold those responsible for online rumors accountable. The system has two major functions. First, with the help of deep learning model, it can automatically detect whether the news reported by users is rumor or not, and push the correct news to all users who have browsed the wrong news, so as to suppress the rumor spread. Second, with the help of graphic database, the whole process of rumor generation and dissemination can be displayed in detail, thus providing strong evidence for relevant departments to investigate the responsibility of those who spread rumors.
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Cite this article
TY - CONF AU - Jingrong Wang AU - Haori Lu AU - Yutong Li AU - Jiazhen Song AU - Peng Nie PY - 2022 DA - 2022/12/12 TI - Design and Implementation of Rumor Refuting and Accountability System Based on Deep Learning and Graphic Database BT - Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022) PB - Atlantis Press SP - 322 EP - 330 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-024-4_34 DO - 10.2991/978-94-6463-024-4_34 ID - Wang2022 ER -