Identifying interesting Twitter contents using topical analysis

Min Chul Yang, Hae Chang Rim

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

Social media platforms such as Twitter are becoming increasingly mainstream which provides valuable user-generated information by publishing and sharing contents. Identifying interesting and useful contents from large text-streams is a crucial issue in social media because many users struggle with information overload. Retweeting as a forwarding function plays an important role in information propagation where the retweet counts simply reflect a tweet's popularity. However, the main reason for retweets may be limited to personal interests and satisfactions. In this paper, we use a topic identification as a proxy to understand a large number of tweets and to score the interestingness of an individual tweet based on its latent topics. Our assumption is that fascinating topics generate contents that may be of potential interest to a wide audience. We propose a novel topic model called Trend Sensitive-Latent Dirichlet Allocation (TS-LDA) that can efficiently extract latent topics from contents by modeling temporal trends on Twitter over time. The experimental results on real world data from Twitter demonstrate that our proposed method outperforms several other baseline methods.

Original languageEnglish
Pages (from-to)4330-4336
Number of pages7
JournalExpert Systems With Applications
Volume41
Issue number9
DOIs
Publication statusPublished - 2014 Jul

Keywords

  • Interesting content
  • LDA
  • Social media
  • Topic model
  • Twitter

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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