# Shared Feelings: Understanding Facebook Reactions to Scholarly Articles

## TL;DR

This paper introduces a new dataset of Facebook reactions to scholarly articles, analyzing social media engagement features beyond text and demonstrating their potential for understanding online scholarly communication.

## Contribution

It provides a novel dataset of Facebook reactions to academic content and explores their statistical properties and predictive modeling capabilities.

## Key findings

- Reactions show stratification based on page followings.
- Subject matter influences reaction patterns.
- Preliminary models can predict engagement trends.

## Abstract

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.