An unsupervised feature selection framework for social media data

Jiliang Tang, Huan Liu

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


The explosive usage of social media produces massive amount of unlabeled and high-dimensional data. Feature selection has been proven to be effective in dealing with high-dimensional data for efficient learning and data mining. Unsupervised feature selection remains a challenging task due to the absence of label information based on which feature relevance is often assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, e.g., social media data is inherently linked, which makes invalid the independent and identically distributed assumption, bringing about new challenges to unsupervised feature selection algorithms. In this paper, we investigate a novel problem of feature selection for social media data in an unsupervised scenario. In particular, we analyze the differences between social media data and traditional attribute-value data, investigate how the relations extracted from linked data can be exploited to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We systematically design and conduct systemic experiments to evaluate the proposed framework on data sets from real-world social media websites. The empirical study demonstrates the effectiveness and potential of our proposed framework.

Original languageEnglish (US)
Article number6807781
Pages (from-to)2914-2927
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
StatePublished - Dec 1 2014


  • Unsupervised feature selection
  • linked data
  • pseudo labels
  • social dimension regularization
  • social media

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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