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TweetSense: Context Recovery for Orphan Tweets by Exploiting Social Signals in Twitter

Published: 28 June 2015 Publication History

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.

References

[1]
W. Feng and J. Wang. We can learn your hashtags: Connecting tweets to explicit topics. In Data Engineering (ICDE), 2014 IEEE 30th International Conference on, pages 856--867, March 2014.
[2]
J. She and L. Chen. Tomoha: Topic model-based hashtag recommendation on twitter. In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, WWW Companion '14, pages 371--372, Republic and Canton of Geneva, Switzerland, 2014. International World Wide Web Conferences Steering Committee.
[3]
E. Zangerle, W. Gassler, and G. Specht. On the impact of text similarity functions on hashtag recommendations in microblogging environments. Eva 2013, 3(4):889--898, 2013.

Cited By

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  • (2021)Understanding Twitter Hashtags from Latent Themes Using Biterm Topic ModelRecent Patents on Engineering10.2174/187221211366619032818351714:3(440-447)Online publication date: 19-Jan-2021
  • (2021)Research topics and trends of the hashtag recommendation domainScientometrics10.1007/s11192-021-03874-6Online publication date: 14-Feb-2021

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  1. TweetSense: Context Recovery for Orphan Tweets by Exploiting Social Signals in Twitter

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      cover image ACM Conferences
      WebSci '15: Proceedings of the ACM Web Science Conference
      June 2015
      366 pages
      ISBN:9781450336727
      DOI:10.1145/2786451
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 28 June 2015

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      Author Tags

      1. Context
      2. Hashtags
      3. Rectification
      4. Regression Model
      5. Social Network
      6. Twitter

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      • Refereed limited

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      WebSci '15
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      WebSci '15: ACM Web Science Conference
      June 28 - July 1, 2015
      Oxford, United Kingdom

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      Overall Acceptance Rate 245 of 933 submissions, 26%

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      View all
      • (2021)Understanding Twitter Hashtags from Latent Themes Using Biterm Topic ModelRecent Patents on Engineering10.2174/187221211366619032818351714:3(440-447)Online publication date: 19-Jan-2021
      • (2021)Research topics and trends of the hashtag recommendation domainScientometrics10.1007/s11192-021-03874-6Online publication date: 14-Feb-2021

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