Exposing Hate -- Understanding Anti-Immigration Sentiment Spreading on Twitter

Fuente: arXiv
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Main Authors: Nasuto, Andrea, Rowe, Francisco
Format: Preprint
Published: 2024
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author Nasuto, Andrea
Rowe, Francisco
author_facet Nasuto, Andrea
Rowe, Francisco
contents Immigration is one of the most salient topics in public debate. Social media heavily influences opinions on immigration, often sparking polarized debates and offline tensions. Studying 220,870 immigration-related tweets in the UK, we assessed the extent of polarization, key content creators and disseminators, and the speed of content dissemination. We identify a high degree of online polarization between pro and anti-immigration communities. We found that the anti-migration community is small but denser and more active than the pro-immigration community with the top 1% of users responsible for over 23% of anti-immigration tweets and 21% of retweets. We also discovered that anti-immigration content spreads also 1.66 times faster than pro-immigration messages and bots have minimal impact on content dissemination. Our findings suggest that identifying and tracking highly active users could curb anti-immigration sentiment, potentially easing social polarization and shaping broader societal attitudes toward migration.
format Preprint
id arxiv_https___arxiv_org_abs_2401_06658
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Exposing Hate -- Understanding Anti-Immigration Sentiment Spreading on Twitter
Nasuto, Andrea
Rowe, Francisco
Social and Information Networks
Immigration is one of the most salient topics in public debate. Social media heavily influences opinions on immigration, often sparking polarized debates and offline tensions. Studying 220,870 immigration-related tweets in the UK, we assessed the extent of polarization, key content creators and disseminators, and the speed of content dissemination. We identify a high degree of online polarization between pro and anti-immigration communities. We found that the anti-migration community is small but denser and more active than the pro-immigration community with the top 1% of users responsible for over 23% of anti-immigration tweets and 21% of retweets. We also discovered that anti-immigration content spreads also 1.66 times faster than pro-immigration messages and bots have minimal impact on content dissemination. Our findings suggest that identifying and tracking highly active users could curb anti-immigration sentiment, potentially easing social polarization and shaping broader societal attitudes toward migration.
title Exposing Hate -- Understanding Anti-Immigration Sentiment Spreading on Twitter
topic Social and Information Networks
url https://arxiv.org/abs/2401.06658