Unmasking the Mask Debate on Social Media

Cerbin, Luca; DeJesus, Jason; Warnken, Julia; Gokhale, Swapna S.

doi:10.1109/compsac51774.2021.00098

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The basis of this framework is a set of features extracted from the structured and unstructured tweet data that we expect to remain invariant in conversations on various controversial topics and issues. Using these features, accompanied by feature selection and processing along with tuning of hyperparameters of machine learning models, we have also successfully applied this framework to detect tweets that spread anti-mask ( Cerbin et al, 2021 ) and anti-vaccination ( Paul & Gokhale, 2020 ), and those that support Proud Boys, an extremist, radical group ( Fahim & Gokhale, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests

Nguyen¹,

Gokhale²

2022

PeerJ Computer Science

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Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.

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Section: Resultsmentioning

confidence: 99%

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests

Nguyen¹,

Gokhale²

2022

PeerJ Computer Science

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“…Anecdotally, it is now known that the use of pro-or anti-mask hashtags in a tweet is not a reliable indicator of whether a tweet as a whole leans pro-or anti-mask. Many anti-mask hashtags are creatively embedded in pro-mask tweets and vice versa (Cerbin 2021), and some tweets contain both pro-mask and anti-mask hashtags in an attempt to reach a wider audience and increase engagement with the tweet. Therefore, we manually annotated each tweet into 'A' for anti-mask, and 'P' for pro-mask.…”

Section: Data Labelingmentioning

confidence: 99%

“…Given the massive volumes of content shared on social media platforms each day, it is impractical that the content that carries falsehoods and misinformation can be separated manually. Although machine learning may be employed for such classification from a specific data set (Cerbin 2021), it remains a challenge to transfer over or to apply these classifiers to tweets shared outside of that specific period. This is because the percentage of anti-mask content may vary with time and the anti-mask rhetoric incorporates new information about the virus and also presents social and political circumstances.…”

Section: Introductionmentioning

confidence: 99%

Classifying the Evolving Mask Debate: A Transferable Machine Learning Framework

Warnken¹,

Gokhale²

2022

J. Comp. Assist. Linguist. Res.

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Anti-maskers represent a community of people that opposes the use of face masks on grounds that they infringe personal freedoms. This community has thoroughly exploited the convenience and reach of online social media platforms such as Facebook and Twitter to spread discordant information about the ineffectiveness and harm caused by masks in order to persuade people to shun their use. Automatic detection and demoting of anti-mask tweets is thus necessary to limit their damage. This is challenging because the mask dialogue continuously evolves with creative arguments that embed emerging knowledge about the virus, changing socio-political landscape, and present policies of public health officers and organizations. Therefore, this paper builds a transferrable machine learning framework that can separate between anti-mask and pro-mask tweets from longitudinal data collected at four epochs during the pandemic. The framework extracts content, emotional, and engagement features that faithfully capture the patterns that are relevant to anti-mask rhetoric, but ignores those related to contextual details. It trains two ensemble learners and two neural network architectures using these features. Ensemble classifiers can identify anti-mask tweets with approximately 80% accuracy and F1-score from both individual and combined data sets. The invariant linguistic features extracted by the framework can thus form the basis of automated classifiers that can efficiently separate other types of falsehoods and misinformation from huge volumes of social media data.

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Automatic detection of health misinformation: a systematic review

Schlicht

Fernandez²,

Chulvi

et al. 2023

J Ambient Intell Human Comput

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The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts. To counteract the sheer amount of misinformation, there is a need to use automatic detection methods. In this paper we conduct a systematic review of the computer science literature exploring text mining techniques and machine learning methods to detect health misinformation. To organize the reviewed papers, we propose a taxonomy, examine publicly available datasets, and conduct a content-based analysis to investigate analogies and differences among Covid-19 datasets and datasets related to other health domains. Finally, we describe open challenges and conclude with future directions.

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Unmasking the Mask Debate on Social Media

Cited by 4 publications

References 14 publications

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests

Classifying the Evolving Mask Debate: A Transferable Machine Learning Framework

Automatic detection of health misinformation: a systematic review

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