Communications of the ACM - Adaptive middleware
Beyond Microblogging: Conversation and Collaboration via Twitter
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Citizen Sensing, Social Signals, and Enriching Human Experience
IEEE Internet Computing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Unsupervised modeling of Twitter conversations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Continuous Semantics to Analyze Real-Time Data
IEEE Internet Computing
Mark my words!: linguistic style accommodation in social media
Proceedings of the 20th international conference on World wide web
Contextual bearing on linguistic variation in social media
LSM '11 Proceedings of the Workshop on Languages in Social Media
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The information overload created by social media messages in emergency situations challenges response organizations to find targeted content and users. We aim to select useful messages by detecting the presence of conversation as an indicator of coordinated citizen action. Using simple linguistic indicators drawn from conversation analysis in social science, we model the presence of coordination in the communication landscape of Twitter using a corpus of 1.5 million tweets for various disaster and non-disaster events spanning different periods, lengths of time, and varied social significance. Within replies, retweets and tweets that mention other Twitter users, we found that domain-independent, linguistic cues distinguish likely conversation from non-conversation in this online form of mediated communication. We demonstrate that these likely conversation subsets potentially contain more information than non-conversation subsets, whether or not the tweets are replies, retweets, or mention other Twitter users, as long as they reflect conversational properties. From a practical perspective, we have developed a model for trimming the candidate tweet corpus to identify a much smaller subset of data for submission to deeper, domain-dependent semantic analyses for the identification of actionable information nuggets for coordinated emergency response.