Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Automatic generation of personalized annotation tags for Twitter users
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Automatic keyphrase extraction via topic decomposition
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Topical keyphrase extraction from Twitter
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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We build a system to extract user interests from Twitter messages. Specifically, we extract interest candidates using linguistic patterns and rank them using four different keyphrase ranking techniques: TFIDF, TextRank, LDA-TextRank, and Relevance-Interestingness-Rank (RI-Rank). We also explore the complementary relation between TFIDF and TextRank in ranking interest candidates. Top ranked interests are evaluated with user feedback gathered from an online survey. The results show that TFIDF and TextRank are both suitable for extracting user interests from tweets. Moreover, the combination of TFIDF and TextRank consistently yields the highest user positive feedback.