Open AccessRumor detection based on topic classification and multi-scale feature fusion
Author(s) -
Li Tan,
Zihao Ma,
Juan Cao,
Xinyue Lv
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/3/032032
Subject(s) - rumor , computer science , field (mathematics) , feature (linguistics) , data mining , artificial intelligence , set (abstract data type) , scale (ratio) , the internet , machine learning , pattern recognition (psychology) , data science , mathematics , world wide web , geography , philosophy , public relations , cartography , political science , pure mathematics , programming language , linguistics
In recent years, with the rapid development of Internet technology, the spread of network rumors has become one of the important obstacles to maintain the stable development of social networks and ensure the public security. Most of the existing researches focus on the detection of rumors in general fields, ignoring the differences among different fields. According to the characteristics of rumor in the health field, this paper proposes a rumor detection method based on topic classification and multi-scale fusion. Different methods are used to extract features from different sub datasets of different scales, taking into account the overall, inter topic, and intra subject correlation and differences, and then judge after feature fusion. The experimental results show that this method is better than the general detection method in the data set of health field, and has some improvement compared with the algorithm in the same field.