Fusing Parallel Social Contexts within Flexible-Order Proximity for Microblog Topic Detection

Topic detection in social media is a challenging task due to large-scale short, noisy and informal nature of messages. Most existing methods only consider textual content or simultaneously model the posts and the first-order structural characteristics of social networks. They ignore the impact of larger neighborhoods in microblog conversations on topics. Moreover, the simple combination of separated content and structure representations fails to capture their nonlinear correlation and different importance in topic inference. To this end, we propose a novel random walk based Parallel Social Contexts Fusion Topic Model (PCFTM) for weibo conversations. Firstly, a user-level conversation network with content information is built by the reposting and commenting relationships among users. Through random walks of different lengths on network, we obtain the user sequences containing the parallel content and structure contexts, which are used to acquire the flexible-order proximity of users. Then we propose a self-fusion network embedding to capture the nonlinear correlation between parallel social contexts. It is achieved by taking the content embedding sequence processed by CNN as the initial value of structure embedding sequence fed to Bi-LSTM. Meanwhile, a user-level self-attention is further used to mine the different importance of users to topics. Lastly, the user sequence embedding is incorporated into neural variational inference for detecting topics, which adaptively balances the intrinsic complementarity between content and structure, and fully uses both local and global social contexts in topic inference. Extensive experiments on three real-world weibo datasets demonstrate the effectiveness of our proposed model.