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Tracking sentiments toward fat acceptance over a decade on Twitter.

Sadie Bograd1, Benjamin Chen2, Ramakanth Kavuluru3

  • 1326741Paul Laurence Dunbar High School, Lexington, KY USA.

Health Informatics Journal
|January 6, 2022
PubMed
Summary

The fat acceptance (FA) movement faces challenges online, with overall negative sentiment on Twitter despite some users showing support. Understanding these trends is crucial for combating weight stigma.

Keywords:
Fat acceptanceTwitter datamachine learningnatural language processingsentiment analysissocial mediatext mining

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Area of Science:

  • Social Sciences
  • Computer Science
  • Public Health

Background:

  • The fat acceptance (FA) movement seeks to reduce weight stigma and discrimination.
  • Online platforms like Twitter are significant spaces for public discourse on social issues.

Purpose of the Study:

  • To classify sentiment toward the FA movement in tweets using a neural network.
  • To identify correlations between FA sentiment and Twitter user characteristics.

Main Methods:

  • Collected 48,974 tweets mentioning "fat acceptance" or "#fatacceptance" (2010-2019).
  • Developed and trained an AWD-LSTM neural network model for sentiment classification.
  • Achieved nearly 80% precision and recall in classifying supportive and opposing tweets.

Main Results:

  • Tweet sentiment towards FA trended negatively from 2010-2016, with overall negative public sentiment.
  • Users actively tweeting about FA or using related hashtags showed more support.
  • The model successfully classified tweet sentiment toward the FA movement.

Conclusions:

  • The FA movement experiences significant negative sentiment on Twitter, indicating societal challenges.
  • Active engagement within FA communities correlates with increased support.
  • Findings offer insights into the FA movement's dynamics and strategies to reduce weight stigma.