Name
Title | ||
|---|---|---|
TweetSense: Context Recovery for Orphan Tweets by Exploiting Social Signals in Twitter. |
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2786451.2790157 | WebSci |
Field | DocType | Citations |
Recommender system,World Wide Web,Social network,Computer science,Popularity | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manikandan Vijayakumar | 1 | 0 | 0.34 |
| Tejas Mallapura Umamaheshwar | 2 | 0 | 0.34 |
| Subbarao Kambhampati | 3 | 3453 | 450.74 |
| Kartik Talamadupula | 4 | 186 | 20.93 |