This paper is in the following e-collection/theme issue:

Infodemic Management (49) JMIR Theme Issue: COVID-19 Special Issue (2452) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Machine Learning (1465) Quality/Credibility of eHealth Information and Trust Issues (531)

Published on in Vol 2, No 2 (2022): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38756, first published .
COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic

COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic

COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic

Authors of this article:

Nikhil Kolluri1 Author Orcid Image ;   Yunong Liu2 Author Orcid Image ;   Dhiraj Murthy3 Author Orcid Image
Article Authors Cited by (9) Tweetations (8) Metrics

    Original Paper

    1Computational Media Lab, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States

    2School of Engineering, College of Science and Engineering, University of Edinburgh, Edinburgh, United Kingdom

    3Computational Media Lab, School of Journalism and Media, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States

    *these authors contributed equally

    Corresponding Author:

    Dhiraj Murthy, BA, MSc, DPhil

    Computational Media Lab

    School of Journalism and Media, Moody College of Communication

    The University of Texas at Austin

    300 W Dean Keeton (A0900)

    Austin, TX, 78712

    United States

    Phone: 1 512 471 5775

    Email: Dhiraj.Murthy@austin.utexas.edu


    Background: The volume of COVID-19–related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning–based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19–related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed.

    Objective: The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response.

    Methods: We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19–related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19–related misinformation data sets from fact-checked “false” content combined with programmatically retrieved “true” content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set.

    Results: The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19–specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%.

    Conclusions: External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models’ accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a “high-confidence” subsection comprised of machine-learned and human labels suggests that crowdsourced votes can optimize machine-learned labels to improve accuracy above human-only levels. These results support the utility of supervised machine learning to deter and combat future health-related disinformation.

    JMIR Infodemiology 2022;2(2):e38756

    doi:10.2196/38756

    Keywords

    COVID-19; misinformation; machine learning; fact-checking; infodemiology; infodemic management; model performance; model accuracy; content analysis 


    Background

    Low information quality has led to adverse health outcomes for individuals during the COVID-19 pandemic [1-3]. Claims were being made on social media of dangerous home remedies and perceived preventative measures (eg, gargling with bleach-infused water) [4]. Low-quality and biased sources of information can be more alluring to some, as they easily capture attention and offer simpler solutions with unambiguous evidence. Due to their persuasive, “simple” messaging [2], these sources can appear more convincing to some because they confirm existing biases or better align with ideological narratives. Information veracity around COVID-19 is fundamentally important to the health outcomes of individuals worldwide [5]. For example, the information that has been circulated in social media spaces that masks do not prevent COVID-19 transmission or that wearing a mask is unhealthy [6] has been a major issue in terms of increased cases in the United States, but also in India, Brazil, and Turkey. Social media represent a key avenue where COVID-19–related disinformation and misinformation have been disseminated [7].

    To tackle this misinformation, manual intervention alone is insufficient. Indeed, in the first quarter of 2020 alone, English-language fact checks of COVID-19–related content jumped 900% [8]. Despite checks increasing, there are a limited number of fact checkers. Moreover, they cannot check the high volume of content that needs evaluation [8]. Thus, creating any interventions for providing automated solutions to evaluate the credibility of COVID-19–related content being circulated remains critical.

    In this study, we importantly compared COVID-19–related, general, and combined data sets for veracity classification applications, and developed a successful bidirectional long short-term memory (Bi-LSTM) machine-learning model (achieving internal and external validation accuracies of 93% and 75%, respectively). When crowdsourced human labels agreed with machine-learned outputs, the accuracy of 90% exceeded that of either approach alone. Our study provides critical, empirical evidence that small amounts of human labeling and machine learning can be an effective infodemic response to health disinformation.

    Misinformation and Disinformation

    Misinformation is defined as “incorrect or misleading information” [9]. For example, a family member likely does not have intent to mislead you when they provide misinformation about politics or health, as they believe what they are sharing is actually true. Although misinformation is not inherently intentional, it can also cause real harm, as seen with COVID-19 misinformation being attributed to fatalities [10]. Disinformation refers to intentionally and surreptitiously disseminated false information aiming to obscure the truth [11]. Although both words refer to incorrect or inaccurate information, only disinformation is intentionally incorrect. A well-known example of a disinformation campaign is the 2016 Russian hacking of the Hillary Clinton campaign, and distribution of politically damaging propaganda on Facebook, Twitter, YouTube, and Instagram [12]. Russia’s social media disinformation campaign was found to have likely influenced the 2016 US election [13].

    COVID-19 and Social Media

    Early COVID-19–related research was critical in documenting keywords, topics that were emerging, as well as temporal patterns [14-16]. Some work specifically highlighted instances of rumors [17], racism against individuals of Asian descent, and released data sets [18]. Other studies documented COVID-19–related misinformation and disinformation [8,19]. This work found that misinformation was widely diffused, which included that neem leaves can cure coronavirus [20], certain ethnic and racial groups were immune (particularly if they had darker skin), individuals in warmer countries would not be affected, and the disease was no more harmful than the common flu [21].

    Other studies used machine-learned methods to try to classify misinformation and disinformation that was being circulated online [22-24]. By training machine-learned classifiers on labeled misinformation and disinformation data sets, these approaches were able to achieve accuracy ranging from 16.7% to 96% as measured by F1 scores. Early work was mostly focused on deploying rapid results rather than optimizing classifiers for the best accuracy to COVID-19–specific misinformation and disinformation. The presumption was that there would be a reasonable similarity of misinformation detection approaches more broadly with the misinformation being spread during COVID-19. As studies emerged, it became clear that COVID-19–specific data sets and platforms were needed.

    COVID-19–Related Misinformation Data Sets, Machine Learning, and Automated Detection

    Due to the vast amount of COVID-19–related information circulating in public domains, automatic machine-learned identification and classification remains a critical method for detecting harmful content at scale. Six machine-learning algorithms with ensemble learning were used to study COVID-19–related Twitter data [25]. Combinations of several machine-learning approaches and natural language processing (NLP) are being used to develop large-scale misinformation detection. For example, ReCOVery, a repository for COVID-19 news credibility checking, evaluates various machine-learned methods [26]. One of the key issues hindering machine-learned methods remains the lack of large, verified, and labeled misinformation data sets [27]. A reason for this lack is that robust labeled data sets require involvement of humans with specific domain knowledge. Moreover, misinformation is a diverse dynamic phenomenon that changes rapidly [28]. Additionally, there remains a dearth of automated solutions that are scalable to incorporate content from multiple platforms. Although global studies indicate a high prevalence of misinformation (which disproportionately impacts low-income countries) [29], currently available data sets may not be large enough to be scalable [30].

    To help address this gap, FakeCovid is a database of 5182 fact-checked news articles that uses 40 languages from 105 countries and classifies data using machine learning [31]. COVIDLIES is another database comprising 6761 expert-annotated COVID-19–related tweets [22]. Effective NLP methodology has also been used for detecting COVID-19 misinformation through YouTube videos by studying user comments [23]. More than 100 million Twitter messages have been collected and classified to build the “Infodemic Risk Index” to estimate the magnitude of exposure to misinformation across various regions and countries [2]. A manually labeled data set related to COVID-19 misinformation was released [32]. COVID-19–specific data sets have also been developed with non-English–language content, including Arabic [33], Portuguese [34], Italian [35], Chinese [36], and multiple Indic languages [37]. Machine-learned approaches have also been developed to complement manually labeled data sets related to COVID-19 [35].

    Machine-Learning Methods for Text Classification

    NLP applications for text classification include news categorization, sentiment analysis, emotion detection, and authorship attribution [38,39]. Most classical machine-learning models in text classification tasks extract features (eg, bag of words) from the documents and then feed them to a classifier to make a prediction [38]. Note that, following prior works [40], we use the word “classical” to describe traditional supervised and unsupervised machine-learning methods.

    The classical machine-learning models have some limitations, including tedious feature engineering in the process to extract hand-crafted features and the fact that they are difficult to generalize to new tasks due to their strong reliance on domain knowledge when designing features [38]. Deep-learning models make use of embedding models to map text into a feature vector with lower dimensions, thus limiting the need to rely on hand-crafted features (which often require domain knowledge) [38]. ELMo [41], a 3-layer Bi-LSTM model with 93 million parameters developed in 2017, achieved better performance than the previous most popular word2vec models [42,43] developed by Google in 2013. In 2018, OpenAI developed Generative Pre-trained Transformer (GPT) [42], and Google developed Bidirectional Encoder Representations from Transformers (BERT) [43], which inspired the creation of several different pretrained language models (PLMs) of large size based on transformers [38]. For example, XLNet, a generalized autoregressive pretraining method, allows for the learning of bidirectional contexts, and its autoregressive formulation overcomes some limitations of BERT [44]. Moreover, Facebook developed RoBERTa [45], which is trained on a larger data set than BERT. Large models based on transformers, including BERT, RoBERTa, and XLNet, achieved a high level of success in many NLP tasks [43-45].

    Objective

    The objective of this study was to ameliorate the impact of online misinformation through automated, machine-learned, and scalable methods. Our study sought to answer the following three core research questions (RQs):

    RQ1: Can approaches leveraging automated and scalable strategies such as machine learning, information retrieval, and crowdsourcing help combat misinformation when information growth exceeds fact-checker capabilities?

    RQ2: Does training a machine-learning model on only COVID-19–related misinformation data, only on general misinformation data, or on both result in the highest performance on COVID-19–related data?

    RQ3: Does combining crowdsourced labels with machine-learning model outputs improve accuracy over either approach individually?


    Machine-Learned Classification

    We first developed a classifier using the CoAID data set [46]; specifically, the 05-01-2020 and 07-01-2020 folders of the CoAID data set were used. Since there are more pieces of news deemed to be accurate (“true”) than those deemed to be inaccurate (“false”), we included all inaccurate news, but limited the quantity of true news to be equal to the amount of false news to have a balanced data set. For the Bi-LSTM model, we split our input data into a training set (75%) and test set (25%). Pandas [47] and scikit-learn [48] were used in our classifier development and implementation.

    We evaluated different architectures, dropouts, activation functions, optimizers, regularizers, and batch sizes. We ultimately chose an embedding layer, Bi-LSTM layer, Dropout layer with a rate of 0.7, and Dense layer with a 1-dimensional output and sigmoid activation function. We used an Adam optimizer with a learning rate of 0.0001, binary cross-entropy loss, and a batch size of 1. The Bi-LSTM model has a kernel regularizer with l1 and l2 regularization factors of 1e-5 and 1e-4, respectively. In addition, we employed several state-of-the-art models for text classification, including PLMs such as BERT, RoBERTa, and XLNet. We selected RoBERTa, as it is an optimized BERT approach, and XLNet, as it is an autoregressive BERT-like model. We employed four transformers: BERT-base [43], XLNet [44], and two models fine-tuned on RoBERTa-base [45,49,50] for this specific classification task on the 7 data sets described in Table 1 for 3 epochs with default training arguments in HuggingFace Trainer [51]. Moreover, we trained a convolutional neural network (CNN) model for text classification [52], as this method has been extensively used in text classification [38].

    All source code files for our models are publicly available as open source [53].

    Table 1. Data set sources and specifications.
    Data setSourceTime rangeSize (number of articles)Type



    		Noncredible newsTrue newsTotal
    CoAIDaTweetsUntil May 1, 2020572

    		13241896COVID-19–specific
    FNNbPolitiFactN/Ac4727971270General news
    FNNGossip CopN/A16,818533522,153General news
    Validation data set 1dPoynter.org (noncredible news); Washington Post, Associated Press, Politico (true news)July 20, 2020, to August 8, 2020387431777051COVID-19–specific
    Validation data set 2dPoynter.org (noncredible news); BBC, AXIOS, CBS News, The Globe and Mail (true news)January 20, 2020, to June 15, 202214,39816,23230,630COVID-19–specific

    aOnly the 05-01-2020 folder of the CoAID data set was used.

    bFNN: FakeNewsNet.

    cN/A: not applicable.

    dScraped with the query term “COVID-19.”

    Data Evaluation

    To develop our external validation data sets, we used data from Poynter [54], which had several thousand instances of COVID-19–related content with a “false” label. For “true” news, we inherited article accuracy from the credibility of the media source on which the documents were published, following an approach similar to the ReCOVery [26] and CoAID [46] COVID-19–related data sets. We created two external validation data sets with different “true” news sources to test the generalization ability of the models. The first external validation data set consists of ~4000 pieces of false-news content scraped from Poynter and ~3000 pieces of true-news content collected from several news outlets that we deemed to be reliable by inheriting source credibility. We used NewsAPI’s application programming interface [55] to retrieve content from the following news outlets: Reuters, BBC, The Wall Street Journal, The Washington Post, Associated Press, and Politico. We searched for articles from July 20, 2020, to August 8, 2020, with the query term “COVID-19.” With these parameters, we queried just over 3000 news articles and stored their labels, titles, sources, descriptions, URLs, and publication dates. The second external validation data set consists of ~14,000 pieces of noncredible news scraped from Poynter in the time range from March 20, 2020, to February 23, 2022, and ~16,000 pieces of true news scraped from BBC, AXIOS, CBS News, and The Globe and Mail with the query term “COVID-19” in the time range from January 20, 2020, to June 15, 2022. In total, after removing elements due to nonapplicable Poynter labels, the first data set had 7051 labeled pieces of COVID-19–related content within the time range from July 20, 2020, to August 8, 2020, and the second data set had 30,630 pieces of COVID-19–related content within the time range from January 20, 2020, to June 15, 2022.

    We developed methods to evaluate whether training a machine-learning model on only COVID-19–related misinformation data, only on general misinformation data, or on both would result in the highest performance on new, unseen COVID-19 data sets. When evaluating general data sets, FakeNewsNet (FNN) [56,57] provided a data format matching our needs and with a sufficient volume for the scale of our training. For COVID-19–related data, we found that CoAID, a COVID-19 health care misinformation data set, with 1896 news articles, 183,564 related user engagements, 516 social platform posts about COVID-19, and ground truth labels [46], allowed us to achieve high internal validation accuracy in preliminary trials. To be as consistent across the two data sets as possible, we drew from standard benchmarking practices performed on data sets using default machine-learning model implementations. We trained on 7 different combinations of data sources to mimic different situations in the real world: (1) only CoAID, used to mimic the situation when sufficient topic-specific data are available; (2) partial (using only the 05-01-2020 folder of the CoAID data set) CoAID and FNN; (3) partial CoAID and PolitiFact; (4) partial CoAID and the GossipCop content from FNN, used to mimic the situation when we have a limited quantity of topic-specific data; (5) FNN; (6) PolitiFact; and (7) GossipCop, used to mimic the situation when no topic-specific data are available. For three classical models (support vector machine [SVM], logistic regression [LR], and Bernoulli naïve Bayes [BNB]) and six deep-learning models (Bi-LSTM, BERT-based model, two RoBERTa-based models [45,49,50], XLNet [44], and Text-CNN [52]) on all seven data source combinations, we computed precision, recall, and F1-score for both internal validation and the two external validation data sets described above. These were taken as a weighted average of both labels and rounded to the nearest hundredth, as detailed in Multimedia Appendix 1-3, and are available as a CSV file on our data repository [53].

    Ethics Considerations

    The University of Texas at Austin Institutional Review Board (IRB) approved this study for human subjects research on April 20, 2021 (STUDY00000962). Informed consent from all study participants was obtained.

    Crowdsourced Classification

    We recruited annotators from the crowdsourcing platform Prolific to vote on pieces of news content from the data set we created. On Prolific, we set the study distribution to “standard sample,” which launched the study to the whole participant pool [58]. In line with the IRB protocol, we limited voting to US residents only. We established approximately 10 rounds of Prolific tasks with each participant being paid varying amounts of ~$8 an hour, which resulted in 31,441 votes from 756 voters.

    After completing the crowdsourced voting, we then processed the data both manually and with Python scripts for usability. We removed duplicate votes for the same label (two “true” votes) and votes from Prolific IDs that we could not find in the set of IDs reported to us by Prolific. The processed data set had more than 6800 pieces of content with at least 3 votes for either the “true” or “false” label. We took the initial ground truth labels from Poynter and credible news sources and mapped them to 0 or 1. “True” was coded as 1 and “false” was coded as 0. Additionally, “correct” labels were coded as 1 (2 labels), and all other labels were converted to 0 (690 labels). Mapping our labels to 0 or 1 allowed us to collect certain metrics for our data set. Some examples from the crowdsourced data set are provided in Table 2 (also see Multimedia Appendix 1). Voter soft labels of 0.0 or 1.0 indicate that the vote results are concordant (ie, all votes were for the same label), whereas a voter soft label range of 0.4-0.6 implies that (nearly) half of the voters have different opinions.

    We also computed the percentage of agreeing decisions, which we defined as the probability that the label decided on by the crowdsourced votes was the same as the ground truth label. The percentage of agreeing decisions (human voter accuracy) was ~0.73, or 73%. We also calculated interannotator agreements to determine the agreement among voters. As the number of voters varied (from 3 to 7) for each piece of news content, Cohen and Fleiss κ statistics were not suitable for our data set. We therefore computed the percent agreement between users to determine interrater reliability (68.5%) for our data. As percent agreement does not take chance agreement into consideration, we calculated Krippendorff α (0.428). As percent agreement is considered to be acceptable when above 75% [59] and α is “acceptable at 0.667≤α≤0.823 and unacceptable at α<0.667” [60], there was low agreement among all voters in the crowdsourced data. Ultimately, crowdsourced voters had low accuracy (~73%) when identifying COVID-19–related noncredible content, and there was a high level of disagreement among them. Given that this data set was not used as the ground truth, but rather to evaluate whether labeled data from nonexperts could improve model performance, low agreement is not an issue for our use case. Moreover, this low agreement indicates that nonprofessionals respond to misinformation differently rather than consistently.

    Given this high level of variability, we next evaluated whether our crowdsourced data could actually improve machine-learning model predictions. With this in mind, we developed and answered the following questions: (1) Which model best predicted crowdsourced labels? (2) Can model performance be improved after being blended with crowdsourced labels? (3) Which model performs best when blended with crowdsourced labels? (4) If we only take the subset of the data set where machine-learning models and human votes have agreeing labels, will the performance of prediction be improved?; if so, which model has the highest performance?

    Table 2. Examples from the crowdsourced data set.
    News titleGround truthVoter soft labelaVoter labelTotal votesResults
    Concordant human votes

    		COVID-19 pandemic derails Germany’s push for migrant integration-Reuters11.013Correctly classified by humans

    		Photo shows the last meeting of a Turkish doctor who died due to COVID-19 with his child in Munich01.014Misclassified by humans

    		3M brings on another lobbying firm11.015Correctly classified by humans

    		Video shows that the Italian government/Brisbane police used zombie robots/drones to chase their citizen and make them stay home00.004Correctly classified by humans

    		British vaccine provokes immune response in first human studies10.003Misclassified by humans

    		This video shows a woman eating a bat soup in Wuhan00.005Correctly classified by humans
    Discordant human votes

    		An emergency department closed in a Spanish hospital00.516Misclassified by human

    		Majority of Caledonian hotel jobs under review in Edinburgh10.514Correctly classified by humans

    		England v Ireland: Captain Eoin Morgan relishes \'new journey\' in ODI series10.615Correctly classified by humans

    		Panic scene in Germany with people rushing into a supermarket00.405Correctly classified by humans

    aVoter soft label is calculated by the number of true labels/total votes.


    Machine-Learned Classification

    RQ1 asks whether automated systems can help combat COVID-19–related misinformation. We found that machine learning predicts veracity better than random. We developed a Bi-LSTM model trained on the CoAID data set. Specifically, we used 1257 entries from CoAID for training and tested our model on 419 entries from CoAID. We achieved a weighted average F1-score of 0.93 (with equal precision, recall, and accuracy) across both labels. Using the same model, the external validation results on our data set was an F1-score of 0.75, with equal precision, recall, and accuracy. In addition, we fine-tuned BERT-base, RoBERTa-fake-news, Fake-News-BERT-Detect, XLNet, and trained Text-CNN on 7 data set combinations and tested them on the two external validation data sets. The results are shown in Multimedia Appendix 1-2. We achieved accuracies of up to 91%, 93%, 97%, 94%, and 87% on the first external validation data set from BERT-base, RoBERTa-fake-news, Fake-News-BERT-Detect, XLNet, and trained Text-CNN, respectively. Accuracies of up to 93%, 84%, 93%, 91%, and 85% were achieved on the second external data sets from the same models. Given these results, RQ1 can be answered in the affirmative.

    Data Evaluation

    RQ2 asks whether training a machine-learning model on only COVID-19–related misinformation data, on only general misinformation data, or on both results in the highest performance on COVID-19–related data. We found that machine-learned models benefit from COVID-19–related data. Specifically, after training on 7 different data sets (see Multimedia Appendix 1-3), RQ2 can be answered as follows: for classical models, the combination of topic-specific and general-topic data results in the best performance; however, pretrained models benefit from purely topic-specific data the most. In this study, we investigated the efficacy of three scenarios: (1) training on COVID-19–related misinformation, (2) training on non-COVID-19–related misinformation, and (3) training on both COVID-19–related misinformation and non-COVID-19–related misinformation. Our results indicate that including COVID-19–related misinformation (in our case CoAID data) helped—or, at least, maintained—model performance.

    Examples of classical classification models include LR, SVM, BNB, hidden Markov model, and random forests [39]. In our experiment, classical models used included LR, SVM, and BNB. All three classical models shown in Multimedia Appendix 3 achieved the best accuracy when trained on the combination of CoAID and PolitiFact, whereas for deep-learning pretrained models, which have already “studied” the behavior of the English language, the best model performance was obtained when fine-tuned on CoAID only (see Multimedia Appendix 1-3). In instances where we are lacking additional COVID-19–related misinformation content, our findings suggest that incorporation of prior misinformation data sets in conjunction with COVID-19–specific misinformation data sets could potentially be useful to detect new COVID-19–related misinformation when using classical models. However, using PLMs (eg, BERT), which normally have much better performance on language tasks than classical models, fine-tuning on a topic-specific data set tended to give a better result. By combining COVID-19–related (ie, CoAID) and broad, multitopic misinformation data sets (ie, FNN, GossipCop, and PolitiFact), we evaluated the performance of our machine-learning models. Combining labeled data sets from different sources coupled with various machine-learning models is a novel contribution of our study in terms of producing a scalable and generalizable framework. As detailed in Multimedia Appendix 1-3, we found that the accuracy of models where we used only GossipCop data sets was very low. The lowest BNB accuracy we obtained (0.37) was also obtained for GossipCop, indicating the important role that labeled data sets play in the validity of misinformation detection. As GossipCop is considered a credible source of celebrity news, the labeled data sets of GossipCop are specific and have limited value to COVID-19 misinformation detection on their own. Conversely, combining CoAID and GossipCop as the input data to train our models significantly improved the accuracy (0.64) for the BNB model (Multimedia Appendix 3). As the best result, an accuracy of 96.55% was achieved when we fine-tuned Fake-News-BERT-Detect using only the CoAID data set (Multimedia Appendix 1). With these findings, RQ2 can be answered positively.

    Crowdsourced Classification

    RQ3 asks whether combining crowdsourced labels with machine-learning model outputs improves accuracy over either approach individually. We found that combining human votes with machine-learned outputs allowed us to create higher performance models. Specifically, deep-learning models are able to predict human votes at an accuracy up to 70%. Combining human votes with machine-learned outputs allowed us to create a model with 99.1% accuracy. We achieved accuracy up to 98.59% when only considering the subset where model and human votes agreed.

    We first evaluated how well our models could predict our crowdsourced values or the labels we generated from our Prolific labeling (see Multimedia Appendix 4-9). A label of 0 indicates that most voters voted false, while a label of 1 indicates that greater than or equal to half of the voters voted true. Using the models trained on the 7 data set combinations and testing on our data set of 7051 votes, the success at predicting the crowdsourced values from Prolific had accuracies up to 0.70 (see Multimedia Appendix 7). All values were rounded to the nearest hundredth.

    Second, we blended the soft predictions (ie, probabilities) from the models and soft vote (combining the probabilities of each prediction in contrast to hard voting, which chooses the prediction that receives the most votes) results from crowdsourcing data in different proportions to assess both the maximum improvements and highest accuracies that can be achieved after blending. The soft vote results were computed by taking the number of votes for label 1 (credible) and dividing by the number of total votes. The results shown in Table 3 (predictions from blended models) were calculated by the following formula:

    a×(soft predictions from model)+(1–a)×(soft vote results from crowdsourcing data)

    Table 3 illustrates that models had higher accuracy on average after blending, and the highest accuracy we achieved was 99.1% on the first external validation data set (when blending 10% of user vote results with 90% of the machine-learning model prediction). Therefore, we found that models trained on general news were improved. Those models achieved much higher accuracies (up to 99.7%) after blending with user vote results. This represents a considerable improvement over the human vote accuracy of ~73%. As shown in Table 3, when a=0.9, the performance of Text-CNN trained on GossipCop could be improved from 42.6% to 99.1% after blending with crowdsourced data.

    Third, as discussed in the Machine-Learned Classification section above, the machine-learning models had accuracies ranging from 41% to 98% and the human votes had approximately 73% accuracy. Out of the 7051 pieces of content, 39.24%-69.58% (for the best-performing model) showed agreement in both the human votes and the machine-learning model. We were therefore able to make reduced sets of 2766 to 4906 pieces of content. For each piece of content, we assigned its label to whichever value both the machine-learning model and human votes agreed on. Using this approach, our best accuracy was 98.59% (see Multimedia Appendix 10), which was from the Fake-News-BERT-Detect model fine-tuned on the CoAID data set. This is in comparison with an accuracy of 73% for human votes and 96.55% for the entire validation data set. All models achieved the best performance when the models were previously fine-tuned on COVID-19–specific data sets (ie, CoAID).

    The performance of models trained/fine-tuned on a general-topic data set could be improved with crowdsourced data (eg, in low-data situations such as pandemics). Specifically, the base model achieved an accuracy of 71.01% on the whole validation data set. For example, for the subset, we achieved an accuracy of 89.96% at best (by BERT-base fine-tuned on PolitiFact). In addition, models trained on the combination general-topic and COVID-19–specific data set were also improved by this approach. Specifically, accuracies of up to 89.93% on the whole data sets (see Multimedia Appendix 1) were improved to up to 96.26% (for the subset). Practically speaking, both credibility tests could be applied to a piece of content and receive a label of “true” or “false” with up to 98.59% accuracy. Combining human votes with machine-learned outputs therefore outperformed models with human votes alone. Our response to RQ3 is that both blending crowdsourced labels with model predictions and reducing the data set to a “high-confidence” data subset increased model performance.

    Table 3. Analysis of accuracy for blended models, evaluated on the first external validation data set.
    Metrica=0.9a=0.7a=0.5a=0.3a=0.1

    		Average improvement0.0690.0820.0840.0630.029
    Maximum improvement

    		Maximum improvement0.5650.5620.4630.3850.415

    		Model nameText-CNN trained on GossipCopText-CNN trained on GossipCopText-CNN trained on GossipCopText-CNN trained on GossipCopFake-News-BERT-Detect fine-tuned on GossipCop

    		Model accuracy (before blending)0.4260.4260.4260.4260.302

    		Model accuracy (after blending)0.9910.9810.8890.8040.717
    Best performance

    		Model nameText-CNN trained on CoAIDText-CNN trained on CoAIDText-CNN trained on CoAID and PolitiFactText-CNN trained on GossipCopText-CNN trained on PolitiFact

    		Model accuracy (before blending)0.8740.8740.7980.4260.499

    		Model accuracy (after blending)0.9910.9840.8910.8040.728

    Principal Results

    Our results indicate that RQ1 (which asks whether automated systems and scalable strategies can help combat misinformation) can be answered in the affirmative. The models we trained showed an accuracy of 98% on our first external validation data set (of ~7000 posts and true news from July 20, 2020, to August 8, 2020) and an accuracy of 93% on our second validation data set (of ~15,000 posts and true news from January 20, 2020, to June 15, 2022). Labeling by fact-checkers can be time-consuming, labor-intensive, and expensive, whereas machine-learning models can be used at will and at scale once trained. These results support our finding that machine learning significantly improves fact checking given the reality that human fact-checkers are overburdened and cannot feasibly keep up with the increasing volume of online misinformation.

    Regarding RQ2 (which asks what kind of data set is most helpful to machine learning), we found that training/fine-tuning on pandemic-specific content tends to result in higher accuracy. Specifically, our best-performing models were fine-tuned on COVID-19 topic content only. We evaluated three classical models and five deep-learning models trained on seven different data sets, including one topic-specific data set (CoAID only), three general-topic data sets (FNN, GossipCop, and PolitiFact), and three combinations of topic-specific and general-topic data sets (CoAID and FNN, GossipCop and CoAID, PolitiFact and CoAID). Classical models achieved the best accuracy when trained on a combination of general-topic and COVID-19–specific data (the combination of CoAID and PolitiFact), while deep-learning PLMs (eg, BERT), which have already been trained on English-language text and therefore could be considered as having “studied” the behavior of the English language, obtained the best model performance when fine-tuned on a COVID-19–specific data set (ie, CoAID).

    Regarding RQ3, which asks whether combining crowdsourced labels with models can improve model performance, we found that blending crowdsourced labels with model predictions increased model performance. The blended model (crowdsourced votes mixed with a machine-learning model) was able to achieve an accuracy of 99.1%. Given that the accuracy of crowdsourced votes was 73% and the highest accuracy of our machine-learning models was 96.55%, our results therefore show that crowdsourcing can be used in conjunction with machine learning to boost accuracy. In addition, models trained on general news could be improved to achieve much higher accuracies after blending with user vote results. Specifically, we found improvements of up to 57.1% after blending (see Table 3). That being said, the performance of models trained/fine-tuned on a general-topic data set could only be improved when considering the subset. With neither crowdsourcing nor machine learning requiring time from expert fact-checkers, both are viable options for addressing COVID-19 and other health-related misinformation at scale.

    Future Work

    Future work can further optimize our machine-learning model and extend and develop our labeled data set. Moreover, we hope that our findings encourage others to develop COVID-19–specific disinformation and misinformation data sets. As the quantity of COVID-19–related labeled data increases, the combination of COVID-19–related labeled data and general misinformation data should be further evaluated and benchmarked by others to enhance machine-learning model accuracy. Our results would therefore benefit from replication in future work with a data set consisting of both COVID-19–related and broad, multitopic content. Since we only crowdsourced votes for the first external validation data set (which spans one month), future work could crowdsource vote results on the second validation data set to strengthen the validity of our conclusions. Furthermore, the size of the crowdsourcing data set is relatively small (31,441 pieces of content and 4.46 average votes each), which could be strengthened with the accumulation of more votes and would increase the generalizability of our results. Thus, future work would benefit from extending our framework to a larger crowdsourced data set. Since collecting crowdsourced data could be time-consuming, using machine-learning models to generate pseudohuman votes can potentially be another way to strengthen the crowdsourced data set. After collecting crowdsourced data for a small news data set, the pseudohuman votes model trained on that data set can be used to predict human labels on a larger data set. This method would be especially useful with unlabeled news data sets, on which we could simulate human votes in the absence of ground truth labels.

    Future work could also measure whether there are sufficient advantages of using machine-learning models rather than expert fact-checkers (given that the former method allows for cheaper and quicker large-scale data labeling). There is also the possibility that machine-learning models and professional fact-checkers combined together could deliver better results. For example, fact-checkers could use models to flag news to speed up their work, and the results from fact-checkers could be used to refine models. Human-in-loop models could be developed by using this method. A live news browser displaying news alongside fact-checker results or model predictions (if no fact-checker is available) could help assess credibility even when there is more misinformation than experts can check manually. Lastly, future work could further examine the relationship between crowdsourced outputs and ground truth labels for COVID-19–related data, a line of inquiry we minimally investigated in this study. Specifically, future work could examine when humans are more likely to make misjudgments by exploring the scenarios in which crowdsourced and ground truth labels are most likely to disagree. Research could explore crowdsourced data in different problem domains to identify the misinformation in problem domains that interventions should pay most attention to, using metrics such as the disagreement between human votes and ground truth labels.

    Limitations

    A limitation of our work is that our study did not rigorously test the ceiling of possible model optimization on all combinations of FNN and CoAID models. Another minor limitation is that we assigned “false” to all labels (except two “correct” labels) in the Poynter data set when evaluating our model, even though a small portion of labels could be interpreted as true (<0.5% with labels such as “half true” and “mostly true”). The crowdsourced data set quality was potentially limited due to the number of votes per item and the time span of the labeled data set. Lastly, we were only able to crowdsource votes for the first external validation data set due to time and funding constraints.

    Conclusion

    Manual fact checking is unable to cope with the large volumes of COVID-19–related misinformation that now exists [8]. To help address the proliferation of COVID-19–related misinformation, we developed an automated, machine-learned, and scalable approach. Since the best-performing models we evaluated were fine-tuned on COVID-19–specific content only, topic-specific data sets are much more helpful than general-topic data sets or the combination of the two. The 96.55% and 94.6% accuracy on the first and second external validation data set, respectively, suggest that machine learning can be used to achieve significantly better than random results for the difficult task of determining the veracity of COVID-19–related content. Our study also found that in the cases when only considering the reduced set of the content that both human votes and model outputs agreed on, the models achieved up to 99.1% accuracy. Models trained/fine-tuned on general-topic content can be improved to an acceptable level after combining with human votes, and may be used to supplement limited amounts of topic-specific content in low-data situations (eg, pandemics) to increase accuracy.

    Our findings also suggest that machine-learning models can be augmented with the labels of lay, crowdsourced voters to boost accuracy without additional input from expert fact-checkers. Blending human votes with model prediction results achieved an accuracy up to 99.1% (by combining 10% of a human vote label with 90% of a label from the model). We have released our topic-related data set of 7000 ground truth and crowdsourced labels, machine-learning model, and code in open-source form to promote the development by others of automated, scalable solutions to the COVID-19 infodemic.

    COVID-19 infodemic responses need to acknowledge that misinformation can be amorphous and highly decentralized. The machine-learned and automated approaches developed in this study rely on text features, making them powerful in that they can be extended (eg, by researchers or technology companies) to study a variety of platforms and contexts (eg, news and social media) in which online misinformation exists. Automation and machine learning offer the ability to exchange a small decrease in accuracy for scalability, which is an important consideration when misinformation growth exceeds fact-checking capabilities as continues to be the case during the COVID-19 pandemic.

    Acknowledgments

    The authors wish to thank Kami Vinton for her insightful comments and suggestions, as well as for her assistance proofreading the manuscript. This work was supported by Good Systems, a research Grand Challenge at the University of Texas at Austin, and an Undergraduate Research Fellowship at The University of Texas at Austin.

    Authors' Contributions

    NK and DM jointly architected the study, wrote the first version of the manuscript, and collaborated to obtain crowdsourcing funding. DM obtained further funding for the deep-learning aspects of the project. NK wrote all of the code for the first version of the manuscript, performed the experiments for the first version of the manuscript, collected crowdsourced data, and provided data for Multimedia Appendix 4 and part of Multimedia Appendix 5. YL wrote substantial sections of the manuscript revision, performed the experiments during the revision process, provided the second validation data set, performed the experiment regarding deep-learning models, and provided data for the other tables. All authors collaborated on the revised manuscript. NK and YL contributed equally to the study and should be viewed as joint first authors.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Model performances on the first external validation data set.

    DOCX File , 15 KB
    ‎
    Multimedia Appendix 2

    Model performances on the second external validation data set.

    DOCX File , 15 KB
    ‎
    Multimedia Appendix 3

    Benchmarking results using classical models.

    DOCX File , 20 KB
    ‎
    Multimedia Appendix 4

    Results for the bidirectional long short-term memory (Bi-LSTM) model trained on CoAID and tested on crowdsourced labels.

    DOCX File , 14 KB
    ‎
    Multimedia Appendix 5

    Results for BERT-base tested on crowdsourced labels.

    DOCX File , 14 KB
    ‎
    Multimedia Appendix 6

    Results for RoBERTa-Fake-News tested on crowdsourced labels.

    DOCX File , 14 KB
    ‎
    Multimedia Appendix 7

    Results for Fake-News-BERT-Detect tested on crowdsourced labels.

    DOCX File , 14 KB
    ‎
    Multimedia Appendix 8

    Results for XLNet tested on crowdsourced labels.

    DOCX File , 14 KB
    ‎
    Multimedia Appendix 9

    Results for Text-CNN tested on crowdsourced labels.

    DOCX File , 14 KB
    ‎
    Multimedia Appendix 10

    Model performances on the reduced set of content when human and machine-learned votes agree.

    DOCX File , 17 KB
    1. Shim J, Lee Y, Ahn H. A link2vec-based fake news detection model using web search results. Expert Syst Appl 2021 Dec;184:115491. [CrossRef]
    2. Gallotti R, Valle F, Castaldo N, Sacco P, De Domenico M. Assessing the risks of 'infodemics' in response to COVID-19 epidemics. Nat Hum Behav 2020 Dec;4(12):1285-1293. [CrossRef] [Medline]
    3. Cinelli M, Quattrociocchi W, Galeazzi A, Valensise CM, Brugnoli E, Schmidt AL, et al. The COVID-19 social media infodemic. Sci Rep 2020 Oct 06;10(1):16598. [CrossRef] [Medline]
    4. Litman L, Rosen Z, Rosenzweig C, Weinberger-Litman SL, Moss AJ, Robinson J. Did people really drink bleach to prevent COVID-19? A tale of problematic respondents and a guide for measuring rare events in survey data. MedRxiv. Preprint published on January 2, 2021.   URL: https://www.medrxiv.org/content/10.1101/2020.12.11.20246694v3 [accessed 2022-08-12]
    5. An ad hoc WHO technical consultation managing the COVID-19 infodemic: call for action, 7-8 April 2020. World Health Organization, Institutional Repository for Information Sharing. 2020.   URL: https://apps.who.int/iris/handle/10665/334287 [accessed 2020-12-03]
    6. Khazan O. How a bizarre claim about masks has lived on for months. The Atlantic. 2020 Oct 09.   URL: https://www.theatlantic.com/politics/archive/2020/10/can-masks-make-you-sicker/616641/ [accessed 2022-04-11]
    7. Bridgman A, Merkley E, Loewen P, Owen T, Ruths D, Teichmann L, et al. The causes and consequences of COVID-19 misperceptions: understanding the role of news and social media. HKS Misinfo Review 2020 Jun 18;19:1-18 [FREE Full text] [CrossRef]
    8. Simon F, Howard PN, Nielsen RK. Types, sources, and claims of COVID-19 misinformation. Reuters Institute. 2020 Apr 07.   URL: https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation [accessed 2022-08-12]
    9. Misinformation. Merriam-Webster Dictionary.   URL: https://www.merriam-webster.com/dictionary/misinformation [accessed 2022-07-05]
    10. Coleman A. 'Hundreds dead' because of Covid-19 misinformation. BBC. 2020 Aug 12.   URL: https://www.bbc.co.uk/news/world-53755067 [accessed 2022-07-17]
    11. Disinformation. Merriam-Webster Dictionary.   URL: https://www.merriam-webster.com/dictionary/disinformation [accessed 2022-07-05]
    12. MacLellan K, Kerry F. Britain says Russian troll factory is spreading disinformation on social media. Reuters.   URL: https:/​/www.​reuters.com/​world/​europe/​britain-says-russian-troll-factory-is-spreading-disinformation-social-media-2022-04-30/​ [accessed 2022-07-17]
    13. Jamieson KH. Cyberwar: how Russian hackers and trolls helped elect a president: what we don't, can't, and do know. Oxford, UK: Oxford University Press; 2020.
    14. Banda JM, Tekumalla R, Wang G, Yu J, Liu T, Ding Y, et al. A large-scale COVID-19 Twitter chatter dataset for open scientific Research—an international collaboration. Epidemiologia 2021 Aug 05;2(3):315-324. [CrossRef]
    15. Chen E, Lerman K, Ferrara E. Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus Twitter data set. JMIR Public Health Surveill 2020 May 29;6(2):e19273 [FREE Full text] [CrossRef] [Medline]
    16. Gao Z, Yada S, Wakamiya S. NAIST COVID: Multilingual COVID-19 Twitter and Weibo dataset. arXiv. Preprint published on April 17, 2020.   URL: https://arxiv.org/abs/2004.08145 [accessed 2022-08-12]
    17. Haouari F, Hasanain M, Suwaileh R. ArCOV19-Rumors: Arabic COVID-19 Twitter dataset for misinformation detection. arXiv. Preprint published on March 13, 2021.   URL: https://arxiv.org/abs/2010.08768 [accessed 2022-08-12]
    18. He B, Ziems C, Soni S, Ramakrishan N, Yang D, Kumar S. Racism is a virus: anti-Asian hate and counterspeech in social media during the COVID-19 crisis. arXiv. Preprint published on November 10, 2021.   URL: https://arxiv.org/abs/2005.12423?context=cs.CL [accessed 2022-08-12]
    19. Kouzy R, Abi Jaoude J, Kraitem A, El Alam MB, Karam B, Adib E, et al. Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus 2020 Mar 13;12(3):e7255 [FREE Full text] [CrossRef] [Medline]
    20. FALSE: A claim that neem leaves can cure the novel coronavirus and relieve its symptoms has been shared thousands of times in multiple Facebook posts. Poynter. 2020 Mar 22.   URL: https://tinyurl.com/yc22cz8z [accessed 2022-07-17]
    21. Singh L, Bansal S, Bode L, Budak C, Chi G, Kawintiranon K, et al. A first look at COVID-19 information and misinformation sharing on Twitter. ArXiv. Preprint published on March 31, 2020.   URL: https://arxiv.org/abs/2003.13907 [accessed 2022-08-12]
    22. Hossain T, Logan RI, Ugarte A. COVIDLies: detecting COVID-19 misinformation on social media. 2020 Presented at: 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020; November 20, 2020; virtual. [CrossRef]
    23. Serrano JCM, Papakyriakopoulos O, Hegelich S. NLP-based feature extraction for the detection of COVID-19 misinformation videos on YouTube. 2020 Presented at: 1st Workshop on NLP for COVID-19 at ACL 2020; July 2020; virtual.
    24. Dharawat A, Lourentzou I, Morales A, Zai CX. Drink bleach or do what now? Covid-HeRA: A dataset for risk-informed health decision making in the presence of COVID19 misinformation. arXiv. Preprint published on October 17, 2020.   URL: https://arxiv.org/abs/2010.08743 [accessed 2022-08-12]
    25. Al-Rakhami MS, Al-Amri AM. Lies kill, facts save: detecting COVID-19 misinformation in Twitter. IEEE Access 2020;8:155961-155970 [FREE Full text] [CrossRef] [Medline]
    26. Zhou X, Mulay A, Ferrara E, Zafarani R. ReCOVery: a multimodal repository for COVID-19 news credibility research. arXiv. Preprint published on June 9, 2020.   URL: https://arxiv.org/abs/2006.05557 [accessed 2022-08-12]
    27. Hua J, Shaw R. Corona Virus (COVID-19) "infodemic" and emerging issues through a data lens: the case of China. Int J Environ Res Public Health 2020 Mar 30;17(7):2309 [FREE Full text] [CrossRef] [Medline]
    28. Zhang X, Ghorbani AA. An overview of online fake news: characterization, detection, and discussion. Inf Process Manag 2020 Mar;57(2):102025. [CrossRef]
    29. Cha M, Cha C, Singh K, Lima G, Ahn Y, Kulshrestha J, et al. Prevalence of misinformation and factchecks on the COVID-19 pandemic in 35 countries: observational infodemiology study. JMIR Hum Factors 2021 Feb 13;8(1):e23279 [FREE Full text] [CrossRef] [Medline]
    30. Asr FT, Taboada M. Big Data and quality data for fake news and misinformation detection. Big Data & Society 2019 Jan;6(1). [CrossRef]
    31. Shahi GK, Nandini D. FakeCovid--a multilingual cross-domain fact check news dataset for COVID-19. arXiv. Preprint published on June 19, 2020.   URL: https://arxiv.org/abs/2006.11343 [accessed 2022-08-12]
    32. Patwa P, Sharma S, Pykl S, Guptha V, Kumari G, Akhtar M, et al. Fighting an infodemic: COVID-19 fake news dataset. In: Chakraborty T, Shu K, Bernard HR, Liu H, Akhtar MS, editors. Combating Online Hostile Posts in Regional Languages during Emergency Situation. CONSTRAINT 2021. Communications in Computer and Information Science, vol 1402. Cham: Springer; 2021.
    33. Addawood A. Coronavirus: Public Arabic Twitter Data Set. OpenReview. 2020 Aug 12.   URL: https://openreview.net/forum?id=ZxjFAfD0pSy [accessed 2022-08-12]
    34. Melo T, Figueiredo CMS. A first public dataset from Brazilian twitter and news on COVID-19 in Portuguese. Data in Brief 2020;32:106179. [CrossRef]
    35. Rovetta A, Bhagavathula AS. COVID-19-related web search behaviors and infodemic attitudes in Italy: infodemiological Study. JMIR Public Health Surveill 2020 May 05;6(2):e19374 [FREE Full text] [CrossRef] [Medline]
    36. Yang C, Zhou X, Zafarani R. CHECKED: Chinese COVID-19 fake news dataset. Soc Netw Anal Min 2021;11(1):58 [FREE Full text] [CrossRef] [Medline]
    37. Kar D, Bhardwaj M, Samanta S, Azad AP. No rumours please! A multi-indic-lingual approach for COVID fake-tweet detection. arXiv. Preprint published on October 14, 2020.   URL: https://arxiv.org/abs/2010.06906 [accessed 2022-08-12]
    38. Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J. Deep learning–based text classification. ACM Comput Surv 2022 Apr 30;54(3):1-40. [CrossRef]
    39. Alam T, Khan A, Alam F. Bangla text classification using transformers. arXiv. Preprint published on November 9, 2020.   URL: https://arxiv.org/abs/2011.04446 [accessed 2022-08-12]
    40. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature 2017 Sep 13;549(7671):195-202. [CrossRef] [Medline]
    41. Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, et al. Deep contextualized word representations. arXiv. Preprint published on February 15, 2018.   URL: https://arxiv.org/abs/1802.05365 [accessed 2022-08-12]
    42. Radford A, Narasimhan K, Salimans T, Sutskever I. Improving language understanding by generative pre-training. Amazon Simple Storage System (S3). 2018.   URL: https:/​/s3-us-west-2.​amazonaws.com/​openai-assets/​research-covers/​language-unsupervised/​language_understanding_paper.​pdf [accessed 2022-08-12]
    43. Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv. Preprint published on October 11, 2018.   URL: https://arxiv.org/abs/1810.04805 [accessed 2022-08-12]
    44. Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV. XLNet: generalized autoregressive pretraining for language understanding. 2019 Presented at: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); December 8-14, 2019; Vancouver, BC.
    45. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al. RoBERTa: a robustly optimized bert pretraining approach. arXiv. Preprint publihsed on Jul, 26 2019.   URL: https://arxiv.org/abs/1907.11692 [accessed 2022-08-12]
    46. Cui L, Lee D. CoAID: COVID-19 healthcare misinformation dataset. arXiv. Preprint published on May 22, 2020.   URL: https://arxiv.org/abs/2006.00885 [accessed 2022-08-12]
    47. The Pandas Development Team. Zenodo. 2020 Dec 07.   URL: https://zenodo.org/record/4309786#.YvfOIC9E3mp [accessed 2022-08-12]
    48. Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, et al. Machine learning for neuroimaging with scikit-learn. Front Neuroinform 2014;8:14. [CrossRef] [Medline]
    49. Liu J. Transformer-fake-news-detection. GitHub. 2022.   URL: https://github.com/Jiayif/Transformer-Fake-News-Detection [accessed 2022-08-12]
    50. Tatti GV. roberta-fake-news. Hugging Face. 2021.   URL: https://huggingface.co/ghanashyamvtatti/roberta-fake-news [accessed 2022-08-12]
    51. Thomas W, Lysandre D, Victor S, Julien C, Clement D, Anthony M, et al. Transformers: State-of-the-Art Natural Language Processing. 2020 Presented at: 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations; October 2020; online. [CrossRef]
    52. Gong M, Shou L, Lin W, Sang Z, Yan Q, Yang Z, et al. NeuronBlocks: building your NLP DNN models like playing Lego. arXiv. Preprint published on April 21, 2019.   URL: https://arxiv.org/abs/1904.09535 [accessed 2022-08-12]
    53. COVID-19 misinformation detection: machine-learned solutions to the infodemic. GitHub.   URL: https:/​/github.​com/​yunongLiu1/​COVID-19-Misinformation-Detection--Machine-Learned-Solutions-to-the-Infodemic [accessed 2022-08-13]
    54. Poynter. 2020.   URL: https://www.poynter.org/ [accessed 2022-04-11]
    55. News API.   URL: https://newsapi.org/ [accessed 2020-12-06]
    56. Shu K, Mahudeswaran D, Wang S, Lee D, Liu H. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data 2020;8(3):171-188. [CrossRef]
    57. Shu K, Sliva A, Wang S, Tang J, Liu H. Fake news detection on social media: a data mining perspective. arXiv. Preprint published on August 7, 2017.   URL: https://arxiv.org/abs/1708.01967 [accessed 2022-08-12]
    58. Setting up a study on Prolific. Prolific.   URL: https://researcher-help.prolific.co/hc/en-gb/articles/4407449546002-Audience [accessed 2022-07-17]
    59. Inter-rater reliability IRR: definition, calculation. Statistics How To.   URL: https://www.statisticshowto.com/inter-rater-reliability/ [accessed 2022-07-17]
    60. Shabankhani B, Charati JY, Shabankhani K, Cherati SK. Survey of agreement between raters for nominal data using Krippendorff's alpha. Arch Pharma Pract 2020;10(S1):160-164.

    ‎
    BERT: Bidirectional Encoder Representations from Transformers
    Bi-LSTM: bidirectional long short-term memory
    BNB: Bernoulli naïve Bayes
    CNN: convolutional neural network
    FNN: FakeNewsNet
    GPT: Generative Pre-trained Transformer
    IRB: institutional review board
    LR: logistic regression
    NLP: natural language processing
    PLM: pretrained language model
    RQ: research question
    SVM: support vector machine

    Edited by W Ahmed; submitted 14.04.22; peer-reviewed by J Banda, N Döring; comments to author 08.06.22; revised version received 20.07.22; accepted 08.08.22; published 25.08.22

    Copyright

    ©Nikhil Kolluri, Yunong Liu, Dhiraj Murthy. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 25.08.2022.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.