Name
Abstract | ||
|---|---|---|
Social mediating technologies have engendered radically new ways of information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and American presidential election. Billions of people create trillions of connections through social media each day, but few of us consider how each click and key press builds relation-ships that, in aggregate, form a vast social network. This paper is based on data obtained from Twitter because of its popularity and sheer data volume. This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. Taking to account all the difficulties, we propose a new event detection approach combining linguistic features and Twitter features. Finally, we present our event detection system from microblogs that aims (1) detect new events, (2) to recognize temporal markers pattern of an event, (3) and to classify important events according to thematic pertinence, author pertinence and tweet volume. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE/ACS 13TH INTERNATIONAL CONFERENCE OF COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) | microblogs, event detection, temporal markers pattern, social network analysis, NLP |
Field | DocType | ISSN |
Data science,World Wide Web,Social event detection,Social network,Social media,Computer science,Popularity,Microblogging,Computer network,Natural disaster,Big data | Conference | 2161-5322 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soumaya cherichi | 1 | 6 | 2.60 |
| Rim Faiz | 2 | 98 | 36.23 |