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Semi-supervised Dirichlet-Hawkes process with applications of topic detection and tracking in Twitter
Conference proceeding

Semi-supervised Dirichlet-Hawkes process with applications of topic detection and tracking in Twitter

Wanying Ding, Yue Zhang, Chaomei Chen and Xiaohua Hu
2016 IEEE International Conference on Big Data (Big Data), pp 869-874
Dec 2016
DOI: https://doi.org/10.1109/BigData.2016.7840680

Abstract

Correlation Dirichlet Process Hawkes Process Kernel Mathematical model Tagging Topic Detection and Tracking Twitter Semantics
Understanding ongoing topics and their evolutions in social media is of great importance. Although topic analysis is not a novel research question, social media environment has presented new challenges. First, with insufficient co-occurrence information, short text have undermined many word co-occurrence oriented topic models' applicability. Second, real time message streams make traditional discretized topic tracking methods hard to function. Third, topics' evolution mechanisms are of great importance in social media context, but many studies have ignored them. Forth, topics have more complicated correlation among each other. Considering the existing problems, this paper has proposed a Semi-Supervised Dirichlet-Hawkes Process (SDHP) to deal with topic detection and tracking from social media. The main contributions of this paper are reflected in: (1) SDHP can handle short text problem efficiently; (2) SDHP can track topics from continuous message stream; (3) SDHP can reveal topics' underlying evolution patterns; and (4) SDHP can capture topics' correlations We have evaluated SDHP's ability in both topic detection and tracking in 8 real datasets from Twitter, and the algorithm's performances are very promising.

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2 citations in Web of Science
6 citations in Scopus

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