An Influence-based Clustering Model on Twitter
This work addresses the challenge of understanding viral content dynamics on social media for researchers and platform analysts, but it appears incremental as it builds on existing clustering methods with new metrics.
The paper tackled the problem of detecting and clustering emergent viral topics on social networks by exploring endogenous and exogenous influences, using a dataset from Twitter API, and found clear distinctions in characteristics of developed content between two user classes.
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.