Understanding social engagements: A comparative analysis of user and text features in Twitter.

ABSTRACT: Information is spread as individuals engage with other users in the underlying social network. Analysis of social engagements can therefore provide insights to understand the motivation behind how and why users engage with others in different activities. In this study, we aim to understand the driving factors behind four engagement types in Twitter, namely like, reply, retweet, and quote. We extensively analyze a diverse set of features that reflect user behaviors, as well as tweet attributes and semantics by natural language processing, including a deep learning language model, BERT. The performance of these features is assessed in a supervised task of engagement prediction by learning social engagements from over 14 million multilingual tweets. In the light of our experimental results, w

SUBMITTER: Toraman C 

PROVIDER: S-EPMC8968783 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature