| 1 |
Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, Proceedings part II
|
|
|
|
In: ISSN: 0302-9743 ; Lecture Notes in Computer Science ; 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017) ; https://hal.archives-ouvertes.fr/hal-03120290 ; Benslimane, Djamal; Damiani, Ernesto; Grosky, William I.; Hameurlain, Abdelkader; Sheth, Amit P.; Wagner, Roland R. 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017), Aug 2017, Lyon, France. Lecture Notes in Computer Science, 10439 (Part II), Springer, 2017, Database and Expert Systems Applications 28th International Conference, DEXA 2017, Lyon, France, 978-3-319-64470-7. ⟨10.1007/978-3-319-64471-4⟩ ; https://link.springer.com/book/10.1007%2F978-3-319-64471-4 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
|
|
|
|
In: Publications (2017)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
|
|
|
|
In: Kno.e.sis Publications (2017)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
What Kind of #Communication is Twitter? A Psycholinguistic Perspective on Communication in Twitter for the Purpose of Emergency Coordination
|
|
|
|
In: Valerie Shalin (2017)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
What Kind of #Conversation is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
|
|
|
|
In: Valerie Shalin (2017)
|
|
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.
|
|
Keyword:
Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Social and Behavioral Sciences
|
|
URL: https://works.bepress.com/valerie_shalin/64
|
|
BASE
|
|
Hide details
|
|
|
| 6 |
RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
|
|
|
|
In: Amit P. Sheth (2017)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Intent Classification of Short-Text on Social Media
|
|
|
|
In: Valerie Shalin (2017)
|
|
BASE
|
|
Show details
|
|
|
|
|
