SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Collabio: a game for annotating people within social networks
Proceedings of the 22nd annual ACM symposium on User interface software and technology
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Eddi: interactive topic-based browsing of social status streams
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
Topical semantics of twitter links
Proceedings of the fourth ACM international conference on Web search and data mining
Who says what to whom on twitter
Proceedings of the 20th international conference on World wide web
Examining lists on Twitter to uncover relationships between following, membership and subscription
Proceedings of the 22nd international conference on World Wide Web companion
Hi-index | 0.00 |
In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collective tweets. Our key insight is that the List meta-data (names and descriptions) provides valuable semantic cues about who the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, we can infer a user's expertise by analyzing the meta-data of crowdsourced Lists that contain the user. We show that our methodology can accurately and comprehensively infer attributes of millions of Twitter users, including a vast majority of Twitter's influential users (based on ranking metrics like number of followers). Our work provides a foundation for building better search and recommendation services on Twitter.