Maintaining knowledge about temporal intervals
Communications of the ACM
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Probabilistic models for discovering e-communities
Proceedings of the 15th international conference on World Wide Web
Sifting micro-blogging stream for events of user interest
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Terminology mining in social media
Proceedings of the 18th ACM conference on Information and knowledge management
TWinner: understanding news queries with geo-content using Twitter
Proceedings of the 6th Workshop on Geographic Information Retrieval
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
Characterization of social media response to natural disasters
Proceedings of the 21st international conference companion on World Wide Web
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Social networks have emerged as hubs of user generated content. Online social conversations can be used to retrieve users interests towards given topics and trends. Microblogging platforms like Twitter are primary examples of social networks with significant volumes of topical message exchanges between users. However, unlike traditional online discussion forums, blogs and social networking sites, explicit discussion threads are absent from microblogging networks like Twitter. This inherent absence of any conversation framework makes it challenging to distinguish conversations from mere topical interests. In this work, we explore semantic, social and temporal relationships of topical clusters formed in Twitter to identify conversations. We devise an algorithm comprising of a sequence of steps such as text clustering, topical similarity detection using TF-IDF and Wordnet, and intersecting social, semantic and temporal graphs to discover social conversations around topics. We further qualitatively show the presence of social localization of discussion threads. Our results suggest that discussion threads evolve significantly over social networks on Twitter. Our algorithm to find social discussion threads can be used for settings such as social information spreading applications and information diffusion analyses on microblog networks.