Skip to main content

TweetSense: Recommending Hashtags for Orphaned Tweets by Exploiting Social Signals in Twitter

Abstract Twitter is a micro-blogging platform where the users can be social, informational or both. In certain cases, users generate tweets that have no "hashtags" or "@mentions"; we call it an orphaned tweet. The user will be more interested to find more "context" of an orphaned tweet presumably to engage with his/her friend on that topic. Finding context for an Orphaned tweet manually is challenging because of larger social graph of a user , the enormous volume of tweets generated per second, topic diversity, and limited information from tweet length of 140 characters. To help the user to get the context of an orphaned tweet, this thesis aims at building a hashtag recommendation system called TweetSense, to suggest ha... (more)
Created Date 2014
Contributor Vijayakumar, Manikandan (Author) / Kambhampati, Subbarao (Advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Artificial intelligence / Data Mining / Hashtag Recommendation / Hashtag Rectification / Information Retrieval / Twitter Search
Type Masters Thesis
Extent 75 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2014
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

  Full Text
9.1 MB application/pdf
Download Count: 994

Description Dissertation/Thesis
4.4 MB application/zip
Download Count: 917

Description Defense Presentation Slides