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A.: Tweets beget propinquity: Detecting highly interactive communities on twitter using tweeting links (2012)
| Venue: | In: WI ’12: Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence |
| Citations: | 5 - 3 self |
BibTeX
@INPROCEEDINGS{Lim12a.:tweets,
author = {Kwan Hui Lim and Amitava Datta},
title = {A.: Tweets beget propinquity: Detecting highly interactive communities on twitter using tweeting links},
booktitle = {In: WI ’12: Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence},
year = {2012},
pages = {214}
}
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Abstract
Abstract—Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hashtags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users. I.
Keyphrases
interactive community detected community topological aspect viral marketing topological information implicit tweeting pattern abstract many community detection algorithm direct tweeting targeted advertising product service information high frequency different interest group share common interest many topological link specific interest actual user interaction interactive twitter community many member topological link online social network
