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

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

doi:10.1145/2786451.2790157

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 proceeding › Conference contribution

1 Scopus citations

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 language	English (US)
Title of host publication	Proceedings of the 2015 ACM Web Science Conference
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450336727
DOIs	https://doi.org/10.1145/2786451.2790157
State	Published - Jun 28 2015
Event	7th ACM Web Science Conference, WebSci 2015 - Oxford, United Kingdom Duration: Jun 28 2015 → Jul 1 2015

Other

Other	7th ACM Web Science Conference, WebSci 2015
Country	United Kingdom
City	Oxford
Period	6/28/15 → 7/1/15

Keywords

Context
Hashtags
Rectification
Regression model
Social network
Twitter

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1145/2786451.2790157

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Cite this

TweetSense : Context recovery for orphan tweets by exploiting social signals in Twitter. / Vijayakumar, Manikandan; Umamaheshwar, Tejas Mallapura; Kambhampati, Subbarao; Talamadupula, Kartik.

Proceedings of the 2015 ACM Web Science Conference. Association for Computing Machinery, Inc, 2015.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Vijayakumar, M, Umamaheshwar, TM, 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, 7th ACM Web Science Conference, WebSci 2015, Oxford, United Kingdom, 6/28/15. https://doi.org/10.1145/2786451.2790157

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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.",

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