Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

Memon, Shahan Ali; Carley, Kathleen M.

doi:10.48550/arxiv.2008.00791

Cited by 32 publications

(43 citation statements)

References 10 publications

(10 reference statements)

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“…They also showed that before the suspension of his Twitter account, Donald Trump was the main driver of anti-vaccine misinformation on Twitter. Lastly, Menon and Carley characterized COVID-19 misinformation communities on Twitter [21]. Their analysis suggested that a large majority of misinformed users may be anti-vaxxers.…”

Section: Related Workmentioning

confidence: 99%

Psycho-linguistic differences among competing vaccination communities on social media

Shi¹,

Ghasiya²,

Sasahara³

2021

Preprint

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Currently, the significance of social media in disseminating noteworthy information on topics such as health, politics, and the economy is indisputable. During the COVID-19 pandemic, anti-vaxxers use social media to distribute fake news and anxiety-provoking information about the vaccine, which may harm the public. Here, we characterize the psycho-linguistic features of anti-vaxxers on the online social network Twitter. For this, we collected COVID-19 related tweets from February 2020 to June 2021 to analyse vaccination stance, linguistic features, and social network characteristics. Our results demonstrated that, compared to pro-vaxxers, anti-vaxxers tend to have more negative emotions, narrative thinking, and worse moral tendencies. This study can advance our understanding of the online anti-vaccination movement, and become critical for social media management and policy action during and after the pandemic.

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Section: Related Workmentioning

confidence: 99%

Psycho-linguistic differences among competing vaccination communities on social media

Shi¹,

Ghasiya²,

Sasahara³

2021

Preprint

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“…Data. We compile an evaluation set from six popular datasets for detecting COVID-19 misinformation in social media, news articles, and scientific publications, i.e., CORD-19 [33], CoAID [7], COVID-CQ [19], ReCOVery [36], CMU-MisCov19 [17], and COVID19FN. 13 Table 1 gives an overview of the datasets and Appendix A.1 presents more details.…”

Section: Methodsmentioning

confidence: 99%

“…COVID-BERT 7 and COVID-SciBERT 8 are pre-trained on the CORD-19 dataset and only available via the Huggingface API. Others, such as COVID-CQ [19] and CMU-MisCov19 [17] are used to either investigate intrinsic details (e.g., how dense misinformed communities are) or to explore the applicability of statistical techniques.…”

Section: Related Workmentioning

confidence: 99%

“…CMU-MisCov19 [17] is a Twitter dataset created by collecting posts from unknowingly misinformed users, users who actively spread misinformation, and users who disseminate facts or call out misinformation. CMU-MisCov19 is composed of 4 573 annotated tweets divided into 17 classes (e.g., conspiracy, fake cure, news, sarcasm).…”

Section: A Appendixmentioning

confidence: 99%

“…CORD-19 has no specific Task or Label as it provides a general collection of documents. † Details on the labels are given in Memon et al[17].…”

mentioning

confidence: 99%

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Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection

Wahle,

Ashok,

Ruas

et al. 2021

Preprint

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A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive body of data on the topic is crucial to prevent harm. Researchers proposed many methods for flagging online misinformation related to COVID-19. However, these methods predominantly target specific content types (e.g., news) or platforms (e.g., Twitter). The methods' capabilities to generalize were largely unclear so far. We evaluate fifteen Transformer-based models on five COVID-19 misinformation datasets that include social media posts, news articles, and scientific papers to fill this gap. We show tokenizers and models tailored to COVID-19 data do not provide a significant advantage over general-purpose ones. Our study provides a realistic assessment of models for detecting COVID-19 misinformation. We expect that evaluating a broad spectrum of datasets and models will benefit future research in developing misinformation detection systems.

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COVID‐19 Infodemic: A study on the Fragile Five countries

Yıldırım

2022

Journal of Public Affairs

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During the COVID‐19 pandemic, many rumors and conspiracy theories spread in various media outlets. The purpose of this study is to reveal the nature of the misinformation detected by fact check platforms that spread in the Fragile Five countries. To determine the themes of misinformation about COVID‐19 and from which media it is disseminated are a possible way to prevent it. The data of the study were obtained from International Fact‐checking Network's CoronaVirusFacts database. One thousand seven hundred thirty‐four piece of misinformation collected by web scraping method during the period January 24, 2020 to November 14, 2020 and analyzed with MAXQDA Analytics Pro 2020. As a result of the research, it was found that rumors (96.3%) spread more in the Fragile Five countries than conspiracy theories (3.7%). While the main theme of the rumors is about illness (26.9%) and diagnosis‐treatment (25%), conspiracy theories are mostly related to the cause of the disease (68.8%). 53.06% of misinformation was spread through the Facebook platform. 15.32% on Twitter; 13.34% on WhatsApp. Misinformation has been heavily false (85.12%) in both rumor and conspiracy theories. In the second place, misleading (10.09%) news spread.

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Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

Cited by 32 publications

References 10 publications

Psycho-linguistic differences among competing vaccination communities on social media

Psycho-linguistic differences among competing vaccination communities on social media

Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection

COVID‐19 Infodemic: A study on the Fragile Five countries

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