Investigation of the determinants for misinformation correction effectiveness on social media during COVID-19 pandemic

Zhang, Yuqi; Guo, Bin; Ding, Yasan; Liu, Jiaqi; Qiu, Chen; Yu, Zhiwen

doi:10.1016/j.ipm.2022.102935

Cited by 30 publications

(23 citation statements)

References 42 publications

(57 reference statements)

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“…We were able to demonstrate a process for identifying potential events related to peaks in the number of messages containing polarized opinions about the vaccination campaign against COVID-19 in Brazil, following premises we observed in previous studies [ 20 , 21 , 22 , 58 ] and opening space for continuities by applying other techniques capable of further filtering useful information by collecting messages from social network users. The analytical process we applied demonstrates that it is possible to generate mechanisms for automating searches for news of benign or harmful events to verify if they were the focus of misinformation or fake news [ 5 , 59 ].…”

Section: Discussionmentioning

confidence: 59%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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This article presents a study that applied opinion analysis about COVID-19 immunization in Brazil. An initial set of 143,615 tweets was collected containing 49,477 pro- and 44,643 anti-vaccination and 49,495 neutral posts. Supervised classifiers (multinomial naïve Bayes, logistic regression, linear support vector machines, random forests, adaptative boosting, and multilayer perceptron) were tested, and multinomial naïve Bayes, which had the best trade-off between overfitting and correctness, was selected to classify a second set containing 221,884 unclassified tweets. A timeline with the classified tweets was constructed, helping to identify dates with peaks in each polarity and search for events that may have caused the peaks, providing methodological assistance in combating sources of misinformation linked to the spread of anti-vaccination opinion.

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Section: Discussionmentioning

confidence: 59%

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Carvalho

Nepomuceno²,

Poleto³

et al. 2022

TropicalMed

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“…On this basis, the followers’ stickiness should be ensured at the same time so that users can get official answers to their questions about different kinds of misinformation in time. Furthermore, concise information expression means describing the complete information content in a concise length which is recommended to be less than 500 words ( Zhang, Y., 2022 ). The false rumors refutation effectiveness drops sharply when the length of the microblog content is too long.…”

Section: Discussionmentioning

confidence: 99%

“…On quantitative side, researchers were more concerned about the identification of rumors and their influence factors of rumor spreading ( Liu, 2019 ; Lu, 2019 ), and the establishment of rumor spreading models ( Hosseini & Zandvakili, 2022 ; Guilherme, 2022 ). Lewandowsky et al (2012) and Zhang, Y. et al (2022) explored determinants and cognitive factors governing the effectiveness of misinformation refutations. Kim and Dennis (2018) found influencing factors on social media article believability.…”

Section: Literature Reviewmentioning

confidence: 99%

Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA

Zhao

Duan

et al. 2023

Information Processing & Management

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“…First, current research predominantly utilizes simulated experiments or user studies [9,32,55], approaches that may not accurately mirror real-world scenarios. Second, relevant research works and datasets [70,76] do not consist of conversational-style narratives with tripled misinformation posts, counter-replies, and responses, as shown in Figure 1. The analysis of these conversations can reveal the complex interactions among misinformation spreaders, those who counter-reply, and the responders to these counter-replies.…”

Section: Introductionmentioning

confidence: 99%

“…Third, related research works do not conduct fine-grained investigation of user responses. The traditional four-class stance [70] or two-class sentiment [76] categorization of user responses only provides a shallow classification of user responses. A more comprehensive taxonomy of user actions behind their responses is needed to provide a better understanding of how users respond differently to social correction.…”

Section: Introductionmentioning

confidence: 99%

Corrective or Backfire: Characterizing and Predicting User Response to Social Correction

He,

Ma,

Ahamad

et al. 2024

ACM Web Science Conference

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Online misinformation poses a global risk with harmful implications for society. Ordinary social media users are known to actively reply to misinformation posts with counter-misinformation messages, which is shown to be effective in containing the spread of misinformation. Such a practice is defined as "social correction". Nevertheless, it remains unknown how users respond to social correction in real-world scenarios, especially, will it have a corrective or backfire effect on users. Investigating this research question is pivotal for developing and refining strategies that maximize the efficacy of social correction initiatives.To fill this gap, we conduct an in-depth study to characterize and predict the user response to social correction in a datadriven manner through the lens of X (Formerly Twitter), where the user response is instantiated as the reply that is written toward a counter-misinformation message. Particularly, we first create a novel dataset with 55, 549 triples of misinformation tweets, countermisinformation replies, and responses to counter-misinformation replies, and then curate a taxonomy to illustrate different kinds of user responses. Next, fine-grained statistical analysis of reply linguistic and engagement features as well as repliers' user attributes is conducted to illustrate the characteristics that are significant in determining whether a reply will have a corrective or backfire effect. Finally, we build a user response prediction model to identify whether a social correction will be corrective, neutral, or have a backfire effect, which achieves a promising F1 score of 0.816. Our work enables stakeholders to monitor and predict user responses effectively, thus guiding the use of social correction to maximize their corrective impact and minimize backfire effects. The code and data is accessible on https://github.com/claws-lab/response-tosocial-correction. CCS CONCEPTS• Information systems → Social networks.

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Investigation of the determinants for misinformation correction effectiveness on social media during COVID-19 pandemic

Cited by 30 publications

References 42 publications

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation

Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA

Corrective or Backfire: Characterizing and Predicting User Response to Social Correction

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