Abstract：Aiming at the problem of detecting dangerous speech leaders in weak relational networks, this paper proposes a method for detecting dangerous speech leaders in weakly connected social networks. The proposed method is based on multi-representation fusion. It determines the sentiment and sensitivity of users' speech to evaluate their professionalism representation. Users' speech is used as nodes to construct a social environment network, and a graph neural network is employed to calculate text co-occurrence relationships and the importance index of users within this network. The user activity representation in social network is then integrated to detect dangerous speech leaders. The results show that the proposed method has better performance compared to baselines.
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