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A Data Science Approach to Social Media Analytics for Discovering Topic-Specific Influential patterns from Tweets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 11 |
| Year of Publication: 2022 |
| Authors: Suneetha Dwarapu, Shashi Mogalla |
10.5120/ijca2022922088
|
Suneetha Dwarapu, Shashi Mogalla . A Data Science Approach to Social Media Analytics for Discovering Topic-Specific Influential patterns from Tweets. International Journal of Computer Applications. 184, 11 ( May 2022), 33-40. DOI=10.5120/ijca2022922088
Abstract
Social media services like Twitter facilitate communication among people beyond geographical boundaries on the topics / events of their current interest which might differ from time to time. Topic detection from Twitter data would be helpful to find trendy topics / products and current events for target marketing in recommender systems. The information associated with each tweet should be analyzed in three different perspectives involving textual content, social context and the temporal aspects to discover high quality clusters representing the topics and accordingly generate topic-specific influential patterns relating the users. This paper proposes a new framework for discovering topic specific influential patterns and maintaining them as snapshots along the time line. It involves topic identification through tweet clustering based on the textual content, followed by their refinement based on social context that involves information inherent in the tweeting behavior of users in addition to the timing of the tweet. Finally the framework proceeds to find the topic-specific influential patterns relating the users interested in a common topic. Dynamically finding patterns of influence enables tracking of information diffusion from influential users to their potential followers.
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