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The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack
We describe the evolution of a stereotype as it emerged in tweets about the Charlie Hebdo terrorist attack in Paris in early 2015. Our focus is on terms associated with the Muslim community and the Islamic world. The data (400k tweets) were collected via Twitter streaming API and consisted of tweets...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413988/ https://www.ncbi.nlm.nih.gov/pubmed/32793820 http://dx.doi.org/10.1016/j.heliyon.2020.e04311 |
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author | Marzouki, Yousri Barach, Eliza Srinivasan, Vidhushini Shaikh, Samira Feldman, Laurie Beth |
author_facet | Marzouki, Yousri Barach, Eliza Srinivasan, Vidhushini Shaikh, Samira Feldman, Laurie Beth |
author_sort | Marzouki, Yousri |
collection | PubMed |
description | We describe the evolution of a stereotype as it emerged in tweets about the Charlie Hebdo terrorist attack in Paris in early 2015. Our focus is on terms associated with the Muslim community and the Islamic world. The data (400k tweets) were collected via Twitter streaming API and consisted of tweets that contained at least one of 16 hashtags associated with the Charlie Hebdo attack (e.g., #JeSuisCharlie, #IAmCharlie, #ParisAttacks), collected between January 14th and February 9th. From these data, we generated pairwise co-occurrence frequencies between key words such as “Islam”, “Muslim(s)”, “Arab(s)”, and “The Prophet” and possible associates such as: “terrorism”, “terror”, “terrorist(s)”, “kill(ed)”, “free”, “freedom” and “love”. We use changes in frequency of co-occurring words to define ways in which acute negative and positive stereotypes towards Muslims and Islam arise and evolve in three phases during the period of interest. We identify a positively-valenced backlash in a subset of tweets associated with the “origins of Islam”. Results depict the emergence and transformation of implicit online stereotypes related to Islam from naturally occurring social media data and how pro-as well as anti-Islam online small-world networks evolve in response to a terrorist attack. |
format | Online Article Text |
id | pubmed-7413988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-74139882020-08-12 The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack Marzouki, Yousri Barach, Eliza Srinivasan, Vidhushini Shaikh, Samira Feldman, Laurie Beth Heliyon Article We describe the evolution of a stereotype as it emerged in tweets about the Charlie Hebdo terrorist attack in Paris in early 2015. Our focus is on terms associated with the Muslim community and the Islamic world. The data (400k tweets) were collected via Twitter streaming API and consisted of tweets that contained at least one of 16 hashtags associated with the Charlie Hebdo attack (e.g., #JeSuisCharlie, #IAmCharlie, #ParisAttacks), collected between January 14th and February 9th. From these data, we generated pairwise co-occurrence frequencies between key words such as “Islam”, “Muslim(s)”, “Arab(s)”, and “The Prophet” and possible associates such as: “terrorism”, “terror”, “terrorist(s)”, “kill(ed)”, “free”, “freedom” and “love”. We use changes in frequency of co-occurring words to define ways in which acute negative and positive stereotypes towards Muslims and Islam arise and evolve in three phases during the period of interest. We identify a positively-valenced backlash in a subset of tweets associated with the “origins of Islam”. Results depict the emergence and transformation of implicit online stereotypes related to Islam from naturally occurring social media data and how pro-as well as anti-Islam online small-world networks evolve in response to a terrorist attack. Elsevier 2020-08-06 /pmc/articles/PMC7413988/ /pubmed/32793820 http://dx.doi.org/10.1016/j.heliyon.2020.e04311 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Marzouki, Yousri Barach, Eliza Srinivasan, Vidhushini Shaikh, Samira Feldman, Laurie Beth The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack |
title | The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack |
title_full | The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack |
title_fullStr | The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack |
title_full_unstemmed | The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack |
title_short | The dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the Charlie Hebdo terrorist attack |
title_sort | dynamics of negative stereotypes as revealed by tweeting behavior in the aftermath of the charlie hebdo terrorist attack |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413988/ https://www.ncbi.nlm.nih.gov/pubmed/32793820 http://dx.doi.org/10.1016/j.heliyon.2020.e04311 |
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