Shared Feelings: Understanding Facebook Reactions to Scholarly Articles

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...

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Published in:2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) pp. 301 - 304
Main Authors: Freeman, Cole, Roy, Mrinal Kanti, Fattoruso, Michele, Alhoori, Hamed
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2019
Subjects:
Altmetrics
Data Collection
Facebook Reactions
Research Community
Social Clicks
Social Media Analytics
Supervised Learning
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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.
AbstractList 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.
Author Fattoruso, Michele
Alhoori, Hamed
Freeman, Cole
Roy, Mrinal Kanti
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  givenname: Hamed
  surname: Alhoori
  fullname: Alhoori, Hamed
  organization: Northern Illinois University
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Snippet Research on social-media platforms has tended to rely on textual analysis to perform research tasks. While text-based approaches have significantly increased...
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SubjectTerms Altmetrics
Data Collection
Facebook Reactions
Research Community
Social Clicks
Social Media Analytics
Supervised Learning
Title Shared Feelings: Understanding Facebook Reactions to Scholarly Articles
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