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A Guided Topic Detection Model Based on Topic Evolution and Group Stance
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AI
DOI：10.1109/TCSS.2025.3642625.png)
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Abstract
En 中文
Guided topic detection plays a crucial role in public opinion management and crisis response. To address the adversarial nature of group stances and the dynamic evolution of topics, this study proposes a guided topic detection model based on topic evolution and group stances. First, considering the hidden nature of groups in guided topics, the TA-Louvain hidden community mining method is proposed to quickly and accurately discover the community structure and track the structural changes of the same group. Then, for the complexity of group features in guided topics, an attention mechanism-based group feature representation method AG2vec was designed, which can improve the accuracy and robustness of group feature representation. Finally, to address the confrontational nature of group stances, stance-related features are extracted by combining game theory with a multiple linear regression algorithm. Based on this, group change GRU (GC-GRU), a GRU-based guided topic detection model, is proposed to capture the dynamic evolution of group features over time. Experimental validation shows that the method can flexibly respond to the rapid evolution of topics. It also fully accounts for the confrontational and dynamic types of user group stances, thereby enabling the effective detection of guided topics.
Keywords:
Adversarial nature
AG2vec
GC-GRU
group stances
guided topics
TA-Louvain
Journal
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4.9
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577
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6.8K