ActNeT: Active learning for networked texts in microblogging

Xia Hu, Jiliang Tang, Huiji Gao, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Supervised learning, e.g., classification, plays an important role in processing and organizing microblogging data. In microblogging, it is easy to mass vast quantities of unlabeled data, but would be costly to obtain labels, which are essential for supervised learning algorithms. In order to reduce the labeling cost, active learning is an effective way to select representative and informative instances to query for labels for improving the learned model. Different from traditional data in which the instances are assumed to be independent and identically distributed (i.i.d.), instances in microblogging are networked with each other. This presents both opportunities and challenges for applying active learning to microblogging data. Inspired by social correlation theories, we investigate whether social relations can help perform effective active learning on networked data. In this paper, we propose a novel Active learning framework for the classification of Networked Texts in microblogging (ActNeT). In particular, we study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from microblogging for labeling by taking advantage of social network structure. Experimental results on Twitter datasets show the benefit of incorporating network information in active learning and that the proposed framework outperforms existing state-of-the-art methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
EditorsJoydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy
PublisherSiam Society
Pages306-314
Number of pages9
ISBN (Electronic)9781611972627
DOIs
StatePublished - 2013
EventSIAM International Conference on Data Mining, SDM 2013 - Austin, United States
Duration: May 2 2013May 4 2013

Publication series

NameProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

Other

OtherSIAM International Conference on Data Mining, SDM 2013
CountryUnited States
CityAustin
Period5/2/135/4/13

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Theoretical Computer Science
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'ActNeT: Active learning for networked texts in microblogging'. Together they form a unique fingerprint.

  • Cite this

    Hu, X., Tang, J., Gao, H., & Liu, H. (2013). ActNeT: Active learning for networked texts in microblogging. In J. Ghosh, Z. Obradovic, J. Dy, Z-H. Zhou, C. Kamath, & S. Parthasarathy (Eds.), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 (pp. 306-314). (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013). Siam Society. https://doi.org/10.1137/1.9781611972832.34