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Coalescing Twitter Trends: The Under-Utilization of Machine Learning in Social Media

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2 Author(s)
Michael Brennan ; Dept. of Comput. Sci., Drexel Univ., Philadelphia, PA, USA ; Rachel Greenstadt

We demonstrate the effectiveness that machine learning can bring to improving social media platforms through a case study on Twitter trending topics. Social media relies heavily on tagging and often does not take advantage of machine learning advances. Twitter is no exception. Individual tweets are identified as being part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85% precision without using the identifying keyword as a feature. This can aid in improving the quality of topic categorization by ensuring on-topic tweets that are missing the trend keyword are included, as well as suggest keywords to include in new tweets.

Published in:

Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on

Date of Conference:

9-11 Oct. 2011