Issue No. 10 - Oct. (2017 vol. 29)
Suhas Ranganath , School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ
Suhang Wang , School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ
Xia Hu , Department of Computer Science and Engineering, Texas A& M University, College Station, TX
Jiliang Tang , Computer Science and Engineering Department, Michigan State University, 428 S Shaw Ln Rm 3115, East Lansing, MI
Huan Liu , Department of Computer, Computing, Informatics and Decision Systems, Arizona State University, Tempe, AZ
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.
Media, Time factors, Twitter, Hurricanes, Facebook, Real-time systems, Earthquakes
S. Ranganath, S. Wang, X. Hu, J. Tang and H. Liu, "Facilitating Time Critical Information Seeking in Social Media," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 10, pp. 2197-2209, 2017.