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Predicting Influence of User's Twitter Activity.
- Source :
- Proceedings of the European Conference on e-Learning (ECEL); 2015, p255-261, 7p
- Publication Year :
- 2015
-
Abstract
- The micro-blogging social platform Twitter is being increasingly used nowadays for real-time sharing of news related to a wide range of events, such as elections, protests, disasters, and other news-intensive incidents. In the recent past, the social activity of twittered with sympathies to different political causes has played a major role in several well-known socio-political events, such as the Arab Spring. In a different context, users' twitter feed has helped in a humanitarian disaster relief and rescue in the wake of the 2012 earthquake in Japan. Vast amounts of real-time as well as historical twitter data can be analysed to monitor targets of interest, identify trends in twitter activity, and predict actions on them. In this paper, we consider a case study related to the on-going large- scale and prolonged anti-government protests marches and demonstrations organized by PTI (Pakistan Tehreek Insaf). We analyses the data on twitter activity to identify key hash-tags and propose a model to identify key influences resulting from user's tweet activity. [ABSTRACT FROM AUTHOR]
- Subjects :
- INTERNET users
DISASTER relief
EARTHQUAKES
ARAB Spring Uprisings, 2010-2012
Subjects
Details
- Language :
- English
- ISSN :
- 20488637
- Database :
- Complementary Index
- Journal :
- Proceedings of the European Conference on e-Learning (ECEL)
- Publication Type :
- Conference
- Accession number :
- 108723156