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With the explosive growth of short documents generated from streaming textual sources (e.g., Twitter), latent topic discovery has become a critical task for short text stream clustering. However, most online clustering models determine the probability of producing a new topic by manually setting some hyper-parameter/threshold, which becomes barrier to achieve better topic discovery results. Moreover, topics generated by using existing models often involve a wide coverage of the vocabulary which is not suitable for online social media analysis. Therefore, we propose a n on p ara m etric m odel (NPMM) which exploits auxiliary word embeddings to infer the topic number and employs a "spike and slab" function to alleviate the sparsity problem of topic-word distributions in online short text analyses. NPMM can automatically decide whether a given document belongs to existing topics, measured by the squared Mahalanobis distance. Hence, the proposed model is free from tuning the hyper-parameter to obtain the probability of generating new topics. Additionally, we propose a nonparametric sampling strategy to discover representative terms for each topic. To perform inference, we introduce a one-pass Gibbs sampling algorithm based on Cholesky decomposition of covariance matrices, which can further be sped up using a Metropolis-Hastings step. Our experiments demonstrate that NPMM significantly outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recent research has focused on extracting personal interest data from social media. Although many methods have been developed, accurately estimating users’ interests is often difficult because messages on social media are short and are not classified into any predefined categories. We propose a new method to overcome this problem by incorporating heterogeneous media, such as news. In our method, we first extract explicit features and implicit topics of categories using news media, where implicit topics are determined using a refined topic model. Next, we describe social media messages using these features and topics to estimate users’ interests. Compared with several other approaches, our approach provides more accurate estimations of users’ interests. We also demonstrate that the accuracy of friend recommendations is increased using the users’ interests estimated by our method. Thus, we expect that the proposed approach could be helpful for enhancing the personalization of social media services. [ABSTRACT FROM AUTHOR]