#RoeOverturned: Twitter Dataset on the Abortion Rights Controversy

Chang, Rong-Ching; Rao, A. S.; Zhong, Qiankun; Wojcieszak, Magdalena; Lerman, Kristina

doi:10.1609/icwsm.v17i1.22207

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…Interestingly, fear is a negative emotion that deviates from this pattern. These findings are consistent across datasets containing discussions of the COVID-19 pandemic 21 , abortion 22 on Twitter.…”

Section: Introductionsupporting

confidence: 74%

“…We used a public corpus of tweets about the COVID-19 pandemic 21 , focusing on tweets posted between January 21, 2020 and April 22, 2020 in the analysis of polarization. Our second dataset is a public corpus of tweets about abortion rights collected between January 1, 2022 to January 6, 2023 22 . The tweets contain keywords and hashtags that reflect both sides of the abortion rights debate in US during the period that Roe v Wade was overturned.…”

Section: Datamentioning

confidence: 99%

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Affective Polarization and Dynamics of Information Spread in Online Networks

Lerman,

Feldman,

et al. 2024

Preprint

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Different ideological groups within a heterogeneous online social networks not only disagree but also dislike and distrust each other. This phenomenon, called affective polarization, widens political divisions and also impacts how information spreads online. We directly measure affective polarization on social media by quantifying emotions and toxicity of reply interactions. We demonstrate that interactions between users with same ideology (in-group replies) tend to be positive, while interactions between opposite-ideology users (out-group replies) are characterized by negativity and toxicity. Second, we show that affective polarization generalizes beyond the in-group/out-group dichotomy and can be considered a structural property of social networks: emotions vary with network distance between users, with closer interactions eliciting positive emotions and more distant interactions leading to higher anger, disgust, and toxicity. Finally, we show that similar information can exhibit different dynamics when spreading in polarized groups. These findings are consistent across diverse discussions on topics such as the COVID-19 pandemic and abortion. Our research provides new insights into the complex social dynamics of affective polarization in the digital age and its implications for online discourse.

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Section: Introductionsupporting

confidence: 74%

Section: Datamentioning

confidence: 99%

Affective Polarization and Dynamics of Information Spread in Online Networks

Lerman,

Feldman,

et al. 2024

Preprint

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“…Its vast pool of information not only serves to heighten public awareness but also acts as a beacon, illuminating the locations and contexts of outbreaks. This wealth of real-time data from Twitter proves invaluable in shedding light on the multifaceted aspects of a wide range of topics and matters of interest to the scientific community from different disciplines, such as infectious disease outbreaks [57][58][59][60][61], cryptocurrency and stock markets [62,63], public health concerns [64][65][66][67], societal problems [68][69][70][71][72], emerging technologies [73,74], human behavior analysis [75][76][77][78], and humanitarian issues [79][80][81][82][83], as can be seen from several prior works in these fields, which focused on sentiment analysis and other forms of content analysis of Tweets. Following the COVID-19 epidemic, a growing corpus of studies have used Twitter data to analyze public reactions during this global health emergency [84,85].…”

Section: Relevance Of Mining and Analysis Of Social Media Data During...mentioning

confidence: 99%

Investigating Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

Thakur

2023

Preprint

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Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

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