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Eleventh hour samaritians in high impact events on twitter
| Content Provider | Indraprastha Institute of Information Technology, Delhi |
|---|---|
| Author | Lamba, Hemank |
| Abstract | In uencers have been de ned as the set of users who can maximize the spread of an information through a network. Finding in uencers in online social networks have applications in viral mar- keting, word of mouth information di usion, expertise nding and search engine optimization. In this work, we analyzed fourteen real world high impact events around the world in 2011 and their corresponding tweets. We try to nd out what kind of tweet will propogate the most, thus allowing users to make decisions regarding changing the content and the way it is propogated. We construct a multi-dimensional feature space derived from source based, content based and network based features. We applied both classi cation and regression algorithms and found out that despite randomness and dynamics of human behaviour, it is possible to predict the extent of di usion within a range with 82% accuracy. |
| File Format | |
| Language | English |
| Access Restriction | Authorized |
| Subject Keyword | Online Social Media Social Analytics Data Mining Machine Learning Influencers |
| Content Type | Text |
| Educational Degree | Bachelor of Technology (B.Tech.) |
| Resource Type | Thesis |
| Subject | Data processing & computer science |
