Shared Feelings: Understanding Facebook Reactions to Scholarly Articles
This work addresses the gap in social media research by focusing on click-based responses for scholars, but it is incremental as it builds on existing supervised learning methods with new data.
The paper tackled the problem of understanding Facebook Reactions to scholarly articles by creating a new dataset and analyzing statistical trends, with preliminary tests suggesting stratification in user following based on page subject matter.
Research on social-media platforms has tended to rely on textual analysis to perform research tasks. While text-based approaches have significantly increased our understanding of online behavior and social dynamics, they overlook features on these platforms that have grown in prominence in the past few years: click-based responses to content. In this paper, we present a new dataset of Facebook Reactions to scholarly content. We give an overview of its structure, analyze some of the statistical trends in the data, and use it to train and test two supervised learning algorithms. Our preliminary tests suggest the presence of stratification in the number of users following pages, divisions that seem to fall in line with distinctions in the subject matter of those pages.