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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Sentiment leaning of influential communities in social networks |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Computational Social Networks |
| Publisher | Springer Verlag |
| Geographical Reach | international |
| ISSN | 2197-4314 |
| Volume | 2 |
| Number | 9 |
| Page Range | 1 - 21 |
| Date | 2015 |
| Abstract Text | Social media and social networks contribute to shape the debate on societal and policy issues, but the dynamics of this process is not well understood. As a case study, we monitor Twitter activity on a wide range of environmental issues. First, we identify influential users and communities by means of a network analysis of the retweets. Second, we carry out a content-based classification of the communities according to the main interests and profile of their most influential users. Third, we perform sentiment analysis of the tweets to identify the leaning of each community towards a set of common topics, including some controversial issues. This novel combination of network, content-based, and sentiment analysis allows for a better characterization of groups and their leanings in complex social networks. |
| Related URLs | |
| Digital Object Identifier | 10.1186/s40649-015-0016-5 |
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