Identifying Search Keywords for Finding Relevant Social Media Posts

Authors

  • Shuai Wang University of Illinois at Chicago
  • Zhiyuan Chen University of Illinois at Chicago
  • Bing Liu University of Illinois at Chicago
  • Sherry Emery University of Illinois at Chicago

DOI:

https://doi.org/10.1609/aaai.v30i1.10387

Keywords:

Search keywords, Topic Keyword Mining, Social Media Tracking

Abstract

In almost any application of social media analysis, the user is interested in studying a particular topic or research question. Collecting posts or messages relevant to the topic from a social media source is a necessary step. Due to the huge size of social media sources (e.g., Twitter and Facebook), one has to use some topic keywords to search for possibly relevant posts. However, gathering a good set of keywords is a very tedious and time-consuming task. It often involves a lengthy iterative process of searching and manual reading. In this paper, we propose a novel technique to help the user identify topical search keywords. Our experiments are carried out on identifying such keywords for five (5) real-life application topics to be used for searching relevant tweets from the Twitter API. The results show that the proposed method is highly effective.

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Published

2016-03-05

How to Cite

Wang, S., Chen, Z., Liu, B., & Emery, S. (2016). Identifying Search Keywords for Finding Relevant Social Media Posts. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10387

Issue

Section

Technical Papers: NLP and Text Mining