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Facilitating Time Critical Information Seeking in Social Media

Suhas Ranganath,Suhang Wang,Xia Hu,Jiliang Tang,Huan Liu

Published 2017 in IEEE Transactions on Knowledge and Data Engineering

ABSTRACT

Social media plays a major role in helping people affected by natural calamities. These people use social media to request information and help in situations where time is a critical commodity. However, generic social media platforms like Twitter and Facebook are not conducive for obtaining answers promptly. Algorithms to ensure prompt responders for questions in social media have to understand and model the factors affecting their response time. In this paper, we draw from sociological studies on information seeking and organizational behavior to identify users who can provide timely and relevant responses to questions posted on social media. We first draw from these theories to model the future availability and past response behavior of the candidate responders and integrate these criteria with user relevance. We propose a learning algorithm from these criteria to derive optimal rankings of responders for a given question. We present questions posted on Twitter as a form of information seeking activity in social media and use them to evaluate our framework. Our experiments demonstrate that the proposed framework is useful in identifying timely and relevant responders for questions in social media.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    IEEE Transactions on Knowledge and Data Engineering

  • Publication date

    2017-10-01

  • Fields of study

    Sociology, Computer Science

  • Identifiers
    DOI 10.1109/TKDE.2017.2701375
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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