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Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling ...
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Cross-Lingual Text Classification of Transliterated Hindi and Malayalam ...
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What Kind of #Communication is Twitter? A Psycholinguistic Perspective on Communication in Twitter for the Purpose of Emergency Coordination
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In: Valerie Shalin (2017)
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What Kind of #Conversation is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
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In: Valerie Shalin (2017)
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Intent Classification of Short-Text on Social Media
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In: Valerie Shalin (2017)
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Intent Classification of Short-Text on Social Media
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In: Amit P. Sheth (2016)
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Intent Classification of Short-Text on Social Media
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In: Krishnaprasad Thirunarayan (2016)
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Intent Classification of Short-Text on Social Media
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In: Publications (2015)
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Intent Classification of Short-Text on Social Media
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In: Publications (2015)
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Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination
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In: John M. Flach (2015)
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What Kind of #Conversation is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
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In: John M. Flach (2015)
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Abstract:
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 associated with conversation analysis in social science, we model the presence of conversation in the communication landscape of Twitter in a large corpus of 1.5M 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 (mediated) communication. We demonstrate that conversation subsets within Replies, Retweets and tweets that mention other Twitter users potentially contain more information than non-conversation subsets. Information density also increases for tweets that are not Replies, Retweets or mentioning 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.
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Keyword:
Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Psychology; Science and Technology Studies; Social and Behavioral Sciences
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URL: https://works.bepress.com/john_flach/82
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Intent Classification of Short-Text on Social Media
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In: Kno.e.sis Publications (2015)
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What Kind of #Communication is Twitter? A Psycholinguistic Perspective on Communication in Twitter for the Purpose of Emergency Coordination
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In: John M. Flach (2015)
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Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination
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In: Publications (2014)
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Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination
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In: Amit P. Sheth (2014)
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What Kind of #Conversation is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
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In: Amit P. Sheth (2014)
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Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination
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In: Kno.e.sis Publications (2014)
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What Kind of #Communication is Twitter? A Psycholinguistic Perspective on Communication in Twitter for the Purpose of Emergency Coordination
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In: Amit P. Sheth (2014)
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Twitris v3: From Citizen Sensing to Analysis, Coordination and Action
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In: Amit P. Sheth (2014)
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