An Influence-based Clustering Model on Twitter
This paper introduces a temporal framework for detecting and clustering emergent and viral topics on social networks. Endogenous and exogenous influence on developing viral content is explored using a clustering method based on the a user's behavior on social network and a dataset from Twitter API. Results are discussed by introducing metrics such as popularity, burstiness, and relevance score. The results show clear distinction in characteristics of developed content by the two classes of users.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringmodelSimilar Papers 제목 키워드 기반
Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media
The growing popularity of social media (e.g, Twitter) allows users to easily share information with each other and influence others by expressing their own sentiments on various subjects. In this work, we propose an unsu…
ClusteringGraph ClusteringSentiment AnalysisTwigraph: Discovering and Visualizing Influential Words between Twitter Profiles
The social media craze is on an ever increasing spree, and people are connected with each other like never before, but these vast connections are visually unexplored. We propose a methodology Twigraph to explore the conn…
ArticlesClusteringDetecting Real-World Influence Through Twitter
In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activi…
Detecting Propagators of Disinformation on Twitter Using Quantitative Discursive Analysis
Efforts by foreign actors to influence public opinion have gained considerable attention because of their potential to impact democratic elections. Thus, the ability to identify and counter sources of disinformation is i…
Community DetectionDissecting a Social Botnet: Growth, Content and Influence in Twitter
Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In thi…