Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics

Broniatowski, David A.; Kerchner, Daniel; Farooq, Fouzia; Huang, Xiaolei; Jamison, Amelia M.; Dredze, Mark; Quinn, Sandra Crouse; Ayers, John W.

doi:10.1371/journal.pone.0261768

Cited by 31 publications

(26 citation statements)

References 11 publications

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“…To account for group-level effects we present a number of sensitivity analyses, and note that our findings are consistent over two geographical scales. Our source-based approach to detect misinformation at scale might not capture the totality of misleading and harmful content related to vaccines, and many low-credibility sources publish a mixture of false and true information 39 , 40 . Our results are also limited to a small period of time.…”

Section: Discussionmentioning

confidence: 99%

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

Pierri

Perry

DeVerna

et al. 2022

Sci Rep

118

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Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest across Democratic rather than Republican counties. Granger causality analysis shows evidence for a directional relationship from online misinformation to vaccine hesitancy. Our results support a need for interventions that address misbeliefs, allowing individuals to make better-informed health decisions.

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Section: Discussionmentioning

confidence: 99%

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

Pierri

Perry

DeVerna

et al. 2022

Sci Rep

118

View full text Add to dashboard Cite

show abstract

“…The first part addressed the extraction of health-related tweets using the model proposed in our previous study [ 31 ]. In that study, we used a health lexicon that focused more on general health keywords rather than specific outbreaks, as a recent study suggested that general health misinformation is more likely to spread than, for example, COVID-19 [ 40 ]. In contrast, Table 1 illustrates that most studies in this area focused on a specific domain or disease outbreak.…”

Section: Methodsmentioning

confidence: 99%

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Albalawi¹,

Nikolov²,

Buckley³

2022

JMIR Form Res

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Background In recent years, social media has become a major channel for health-related information in Saudi Arabia. Prior health informatics studies have suggested that a large proportion of health-related posts on social media are inaccurate. Given the subject matter and the scale of dissemination of such information, it is important to be able to automatically discriminate between accurate and inaccurate health-related posts in Arabic. Objective The first aim of this study is to generate a data set of generic health-related tweets in Arabic, labeled as either accurate or inaccurate health information. The second aim is to leverage this data set to train a state-of-the-art deep learning model for detecting the accuracy of health-related tweets in Arabic. In particular, this study aims to train and compare the performance of multiple deep learning models that use pretrained word embeddings and transformer language models. Methods We used 900 health-related tweets from a previously published data set extracted between July 15, 2019, and August 31, 2019. Furthermore, we applied a pretrained model to extract an additional 900 health-related tweets from a second data set collected specifically for this study between March 1, 2019, and April 15, 2019. The 1800 tweets were labeled by 2 physicians as accurate, inaccurate, or unsure. The physicians agreed on 43.3% (779/1800) of tweets, which were thus labeled as accurate or inaccurate. A total of 9 variations of the pretrained transformer language models were then trained and validated on 79.9% (623/779 tweets) of the data set and tested on 20% (156/779 tweets) of the data set. For comparison, we also trained a bidirectional long short-term memory model with 7 different pretrained word embeddings as the input layer on the same data set. The models were compared in terms of their accuracy, precision, recall, F1 score, and macroaverage of the F1 score. Results We constructed a data set of labeled tweets, 38% (296/779) of which were labeled as inaccurate health information, and 62% (483/779) of which were labeled as accurate health information. We suggest that this was highly efficacious as we did not include any tweets in which the physician annotators were unsure or in disagreement. Among the investigated deep learning models, the Transformer-based Model for Arabic Language Understanding version 0.2 (AraBERTv0.2)-large model was the most accurate, with an F1 score of 87%, followed by AraBERT version 2–large and AraBERTv0.2-base. Conclusions Our results indicate that the pretrained language model AraBERTv0.2 is the best model for classifying tweets as carrying either inaccurate or accurate health information. Future studies should consider applying ensemble learning to combine the best models as it may produce better results.

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“…Building on this evidence, the data we present is a pilot study investigating the following research questions: (1) to what extent does misinformation with narrative elaboration about COVID-19 affect believability in misinformation; (2) to what extent does misinformation with narrative elaboration about COVID-19 affect willingness to share information online; (3) to what extent does the effect of misinformation with narrative elaboration about COVID-19 differ by age. We hypothesized that narrative elaboration would increase believability and willingness to share misinformation about COVID-19, in particular in younger adults.…”

Section: Open Accessmentioning

confidence: 99%

“…Misinformation is broadly understood an umbrella term to include all false or inaccurate information that is spread in social media [1]. Since the outbreak of the SARS-CoV-2 virus in 2020 online platforms have been awash with COVID-19 pandemic-related information, however these are often abound with misinformation containing content which is misleading or false [2] (see [3] for an evaluation of online source credibility). Thus, the influence of misinformation is highly impactive as differences in believability between credible content and misinformation can cause a split between scientific consensus and public opinion [4].…”

Section: Introductionmentioning

confidence: 99%

Narrative elaboration makes misinformation and corrective information regarding COVID-19 more believable

2022

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Objective People gather information about health topics from online channels oftentimes awash with misinformation. Investigating this problem during the COVID-19 pandemic is important, as the misinformation effect occurs when misleading details are embedded in narratives and questions. This pilot study investigated whether narrative elaboration increases believability in misinformation statements about COVID-19, and willingness to share these statements online. Results Results from our online survey (n = 80) demonstrated that narrative elaboration increased believability in both misinformation and accurate statements, with a more pronounced effect on younger adults. Future research may investigate cognitive vulnerabilities imposed by elaborate narratives embedded in online health misinformation with increased attention on developing misinformation resilience among younger adults.

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Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics

Cited by 31 publications

References 11 publications

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal

Pretrained Transformer Language Models Versus Pretrained Word Embeddings for the Detection of Accurate Health Information on Arabic Social Media: Comparative Study

Narrative elaboration makes misinformation and corrective information regarding COVID-19 more believable

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