TweetSense: Context recovery for orphan tweets by exploiting social signals in Twitter

Manikandan Vijayakumar, Tejas Mallapura Umamaheshwar, Subbarao Kambhampati, Kartik Talamadupula

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

Abstract

As the popularity of Twitter, and the volume of tweets increased dramatically, hashtags have naturally evolved to become a de facto context providing/categorizing mechanism on Twitter. Despite their wide-spread adoption, fueled in part by hashtag recommendation systems, lay users continue to generate tweets without hashtags. When such "orphan" tweets show up in a (browsing) user's time-line, it is hard to make sense of their context. In this paper, we present a system called TweetSense which aims to rectify such orphan tweeets by recovering their context in terms of their missing hashtags. TweetSense enables this context recovery by using both the content and social network features of the orphan tweet. We characterize the context recovery problem, present the details of TweetSense and present a systematic evaluation of its effectiveness over a 7 million tweet corpus.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 ACM Web Science Conference
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450336727
DOIs
StatePublished - Jun 28 2015
Event7th ACM Web Science Conference, WebSci 2015 - Oxford, United Kingdom
Duration: Jun 28 2015Jul 1 2015

Other

Other7th ACM Web Science Conference, WebSci 2015
CountryUnited Kingdom
CityOxford
Period6/28/157/1/15

    Fingerprint

Keywords

  • Context
  • Hashtags
  • Rectification
  • Regression model
  • Social network
  • Twitter

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Vijayakumar, M., Umamaheshwar, T. M., Kambhampati, S., & Talamadupula, K. (2015). TweetSense: Context recovery for orphan tweets by exploiting social signals in Twitter. In Proceedings of the 2015 ACM Web Science Conference Association for Computing Machinery, Inc. https://doi.org/10.1145/2786451.2790157