Dynamic Emotions of Supporters and Opponents of Anti-racism Movement
from George Floyd Protests
- URL: http://arxiv.org/abs/2109.14269v2
- Date: Fri, 1 Oct 2021 15:32:48 GMT
- Title: Dynamic Emotions of Supporters and Opponents of Anti-racism Movement
from George Floyd Protests
- Authors: Jaihyun Park
- Abstract summary: This study attempts to empirically examine a recent anti-racism movement initiated by the death of George Floyd with the lens of stance prediction and aspect-based sentiment analysis (ABSA)
First, this study found the stance of the tweet and users do change over the course of the protest. Furthermore, there are more users who shifted the stance compared to those who maintained the stance.
- Score: 4.628652869726037
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Social media empowers citizens to raise the voice and expressed civil outrage
leads to collective action to change the society. Since social media welcomes
anyone regardless of the political ideology or perspectives, social media is
where the supporters and opponents of specific issue discuss. This study
attempts to empirically examine a recent anti-racism movement initiated by the
death of George Floyd with the lens of stance prediction and aspect-based
sentiment analysis (ABSA). First, this study found the stance of the tweet and
users do change over the course of the protest. Furthermore, there are more
users who shifted the stance compared to those who maintained the stance.
Second, both supporters and opponents expressed negative sentiment more on nine
extracted aspects. This indicates that there was no significant difference of
sentiment among supporters and opponents and raise a caution in predicting
stance based on the sentiment. The contribution of the study is two-fold.
First, ABSA was explored in the context of computational social science and
second, stance prediction was first attempted at scale.
Related papers
- Dynamics of Ideological Biases of Social Media Users [0.0]
We show that the evolution of online platform-wide opinion groups is driven by the desire to hold popular opinions.
We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users.
arXiv Detail & Related papers (2023-09-27T19:39:07Z) - The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.
We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - Whose Opinions Do Language Models Reflect? [88.35520051971538]
We investigate the opinions reflected by language models (LMs) by leveraging high-quality public opinion polls and their associated human responses.
We find substantial misalignment between the views reflected by current LMs and those of US demographic groups.
Our analysis confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs.
arXiv Detail & Related papers (2023-03-30T17:17:08Z) - What are People Talking about in #BlackLivesMatter and #StopAsianHate?
Exploring and Categorizing Twitter Topics Emerging in Online Social Movements
through the Latent Dirichlet Allocation Model [27.53788299995914]
Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter.
This study adopts a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics.
We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics.
arXiv Detail & Related papers (2022-05-29T17:29:40Z) - Counter Hate Speech in Social Media: A Survey [2.8532545355403123]
We review the most important research in the past and present with a main focus on CHS's impact on social media.
The CHS generation is based on the optimistic assumption that any attempt to intervene the hate speech in social media can play a positive role in this context.
arXiv Detail & Related papers (2022-02-21T06:16:46Z) - Unraveling Social Perceptions & Behaviors towards Migrants on Twitter [1.6904475483445451]
We identify two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants.
Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outper-formed other models.
arXiv Detail & Related papers (2021-12-04T20:45:26Z) - Annotators with Attitudes: How Annotator Beliefs And Identities Bias
Toxic Language Detection [75.54119209776894]
We investigate the effect of annotator identities (who) and beliefs (why) on toxic language annotations.
We consider posts with three characteristics: anti-Black language, African American English dialect, and vulgarity.
Our results show strong associations between annotator identity and beliefs and their ratings of toxicity.
arXiv Detail & Related papers (2021-11-15T18:58:20Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
Country image has a profound influence on international relations and economic development.
In the worldwide outbreak of COVID-19, countries and their people display different reactions.
In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
arXiv Detail & Related papers (2020-09-12T15:54:51Z) - Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis
and Deep Learning -- a Case Study on COVID-19 [2.983310828879753]
Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, hashtags past couple of months.
This study tends to detect and analyze sentiment polarity and emotions demonstrated during the initial phase of the pandemic and the lockdown period.
arXiv Detail & Related papers (2020-08-23T12:43:26Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.