COVID-19 Misinformation Research Articles

COVID-19-Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages.

South Asians, inclusive of individuals originating in India, Pakistan, Maldives, Bangladesh, Sri Lanka, Bhutan, and Nepal, comprise the largest diaspora in the world, with large South Asian communities residing in the Caribbean, Africa, Europe, and elsewhere. There is evidence that South Asian communities have disproportionately experienced COVID-19 infections and mortality. WhatsApp, a free messaging app, is widely used in transnational communication within the South Asian diaspora. Limited studies exist on COVID-19-related misinformation specific to the South Asian community on WhatsApp. Understanding communication on WhatsApp may improve public health messaging to address COVID-19 disparities among South Asian communities worldwide. We developed the COVID-19-Associated misinfoRmation On Messaging apps (CAROM) study to identify messages containing misinformation about COVID-19 shared via WhatsApp. We collected messages forwarded globally through WhatsApp from self-identified South Asian community members between March 23 and June 3, 2021. We excluded messages that were in languages other than English, did not contain misinformation, or were not relevant to COVID-19. We deidentified each message and coded them for one or more content categories, media types (eg, video, image, text, web link, or a combination of these elements), and tone (eg, fearful, well intentioned, or pleading). We then performed a qualitative content analysis to arrive at key themes of COVID-19 misinformation. We received 108 messages; 55 messages met the inclusion criteria for the final analytic sample; 32 (58%) contained text, 15 (27%) contained images, and 13 (24%) contained video. Content analysis revealed the following themes: "community transmission" relating to misinformation on how COVID-19 spreads in the community; "prevention" and "treatment," including Ayurvedic and traditional remedies for how to prevent or treat COVID-19 infection; and messaging attempting to sell "products or services" to prevent or cure COVID-19. Messages varied in audience from the general public to South Asians specifically; the latter included messages alluding to South Asian pride and solidarity. Scientific jargon and references to major organizations and leaders in health care were included to provide credibility. Messages with a pleading tone encouraged users to forward them to friends or family. Misinformation in the South Asian community on WhatsApp spreads erroneous ideas regarding disease transmission, prevention, and treatment. Content evoking solidarity, "trustworthy" sources, and encouragement to forward messages may increase the spread of misinformation. Public health outlets and social media companies must actively combat misinformation to address health disparities among the South Asian diaspora during the COVID-19 pandemic and in future public health emergencies.

Reducing Younger and Older Adults’ Engagement with COVID-19 Misinformation: The Effects of Accuracy Nudge and Exogenous Cues

COVID-19 misinformation got a lot of engagement on social media, which has been a big threat to public health and international relations. Therefore, this study designed an accuracy nudge intervention to induce people to discern misinformation and adopted exogenous cues to reinforce their discernment ability. The two interventions were expected to reduce people’s engagement in COVID-19 misinformation. To test the hypothesis, 80 younger adults and 80 older adults completed this experiment. As expected, the accuracy nudge motivated people to judge misinformation to a large extent. Most of the participants (91.3%) voluntarily used the accuracy nudge, which also decreased the sharing of misinformation by 62.7%. While the intervention combining the accuracy nudge and exogenous cues did not work better than any single intervention in reducing misinformation engagement. Besides, older adults tend to like or share articles that just rely on their glance at headlines, which may account for their vulnerability to misinformation.

A moth to a flame? Fulfilling connectedness needs through romantic relationships protects conspiracy theorists against COVID-19 misinformation

Conspiracy theorists’ unpopular opinions likely make them more apprehensive about interactions with others, frustrating their need to belong. Therefore, they may be susceptible to believing misinformation because evidence that others share their beliefs provides “social proof” that they can expect interactions with others to be positive and rewarding. The present research examined whether alternatively fulfilling the need for social connection through romantic relationships could protect conspiracy theorists against COVID-19 misinformation. In a 3-week daily diary study (N = 555), experimental participants implicitly learned to associate their romantic partners with positive experiences (by repeatedly pairing their partner with highly positive and approachable stimuli, McNulty et al., 2017). We then assessed how much participants trusted individuals they might normally distrust, as a manipulation check, and how much participants tuned their daily personal beliefs and behavior to match the U.S. public's daily susceptibility to COVID-19 misinformation. Participants high on conspiratorial thinking trusted fellow community members more in the experimental than control condition. Participants high on conspiratorial thinking in the experimental condition were also less likely to treat the U.S. public's greater daily susceptibility to COVID-19 misinformation as proof that they could discount the virus. The present findings suggest that rewarding romantic connections might be leveraged to limit conspiracy theorists’ susceptibility to believing public skepticism about COVID-19.

Agreement with COVID-19 disinformation among Portuguese-speaking older adults: an international study.

to assess agreement with COVID-19 disinformation among Portuguese-speaking individuals aged 50 years or older. a descriptive and analytical study involving 1,214 older adults born in Portuguese-speaking countries. Data collection occurred through online information mining to recognize COVID-19 and disinformation content, and the application of a structured questionnaire. agreement with disinformation content was 65.2%. Residing outside Brazil is a protective factor for agreement with disinformation content, and those who believe in the truthfulness of the information sources they receive were 31% more likely to agree with disinformation content. there is a high prevalence of disinformation among the older population in two Portuguese-speaking countries, which should raise the attention of healthcare professionals and guide coping strategies.

Explainable Misinformation Detection Across Multiple Social Media Platforms

Web Information Processing (WIP) has enormously impacted modern society since a huge percentage of the population relies on the internet to acquire information. Social Media platforms provide a channel for disseminating information and a breeding ground for spreading misinformation, creating confusion and fear among the population. One of the techniques for the detection of misinformation is machine learning-based models. However, due to the availability of multiple social media platforms, developing and training AI-based models has become a tedious job. Despite multiple efforts to develop machine learning-based methods for identifying misinformation, there has been very limited work on developing an explainable generalized detector capable of robust detection and generating explanations beyond black-box outcomes. Knowing the reasoning behind the outcomes is essential to make the detector trustworthy. Hence employing explainable AI techniques is of utmost importance. In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms. DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify and explain its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN model. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets and compared results with and without DANN implementation. It is observed that using DANN significantly improves the F1 score of classification and increases the accuracy by 5% and AUC by 11%. The results show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation. This can enable trustworthy information processing and extraction to combat misinformation effectively.

Misinformation about COVID-19 among middle-aged and older migrants residing in Brazil and Portugal.

The aim of this study was to assess the prevalence of COVID-19 misinformation among migrants aged 50 or older residing in Brazil and Portugal. This was a cross-sectional analytical study conducted among migrants from Portuguese-speaking countries living in Brazil and Portugal, who were 50 years of age or older. The prevalence ratios (PR) were estimated using the Poisson regression model. Out of the 304 participants included in the study, 188 (61.8%) agreed with at least one piece of misinformation. Factors such as having a religious affiliation (aPR: 1.24), higher educational attainment (aPR: 1.17), knowing someone who died from COVID-19 (aPR: 1.78), and having no intention to get vaccinated (aPR: 1.36) were associated with a higher likelihood of agreeing with COVID-19 misinformation. The findings suggest that access to misinformation was influenced by social, economic, and religious factors among elderly migrants with low digital literacy, thus contributing to the dissemination of false content within this population.

COVID-19 misinformation on YouTube: An analysis of its impact and subsequent online information searches for verification.

COVID-19 vaccination misinformation on YouTube can have negative effects on users. Some, after being exposed to such misinformation, may search online for information that either debunks or confirms it. This study's objective is to examine the impact of YouTube videos spreading misinformation about COVID-19 vaccination and the influencing variables, as well as subsequent information seeking and its effect on attitudes toward vaccination. In this observational and survey study, we used a three-group pre-test and post-tests design (N = 106 participants). We examined the effects of YouTube videos containing misinformation about COVID-19 vaccination on attitudes toward vaccination via surveys, employed screen recordings with integrated eye tracks to examine subsequent online information searches, and again surveyed participants to examine the effects of the individual searches on their attitudes. Receiving misinformation via video tended to have negative effects, mostly on unvaccinated participants. After watching the video, they believed and trusted less in the effectiveness of the vaccines. Internet searches led to more positive attitudes toward vaccination, regardless of vaccination status or prior beliefs. The valences of search words entered and search duration were independent of the participants' prior attitudes. Misinforming content was rarely selected and perceived (read). In general, participants were more likely to perceive supportive and mostly neutral information about vaccination. Misinformation about COVID-19 vaccination on YouTube can have a negative impact on recipients. Unvaccinated citizens in particular are a vulnerable group to online misinformation; therefore, it is important to take action against misinformation on YouTube. One approach could be to motivate users to verify online content by doing their own information search on the internet, which led to positive results in the study.

Abstract A125: Vaccine hesitancy: Can we convince the unvaccinated to vaccinate?

Abstract Background: Vaccine hesitancy is defined as “delay in acceptance or refusal of vaccination despite the availability of vaccination services”. When individuals choose not to get vaccinated, efforts to control the spread of the disease are hindered. Despite scientific evidence that COVID-19 vaccines are safe and effective, one in four adults in the United States is not fully vaccinated. However, some individuals, despite being previously vaccine-hesitant got the COVID-19 vaccine and vice versa, leading to an intention-behavior gap. In this study, we ask an important question: What socio-demographic, psychological, ideological, and communication-related factors can contribute to the vaccination-related intention-behavior gap? An understanding of these factors can also help create communication strategies to encourage the vaccine-hesitant to get vaccinated, especially in populations where there are greater vaccine disparities. Methods: This longitudinal study uses two nationally representative surveys of Americans from 2020 and 2021 to determine the factors that contribute to COVID-19 vaccination-related intention-behavior gap and overcome vaccine hesitancy, using multivariable logistic regression. Results: The results indicated that 51% of previously vaccine-hesitant individuals were vaccinated against COVID-19, and only 14% of previously pro-vaccine individuals remained unvaccinated. Some factors associated with a higher likelihood of vaccine-hesitant individuals getting vaccinated were high perceptions of risk, high vaccine acceptance, being informed about the vaccines, endorsing less COVID-19 misinformation, having confidence in scientists, being over 60 years old, and being female. Alternatively, some factors associated with a lower likelihood of pro-vaccine individuals not getting vaccinated were high perceptions of risk, high vaccine acceptance, being informed about the vaccines, identifying as a Republican, having confidence in scientists, having access to healthcare, and having higher education. Conclusions: The study indicates that it is possible to convince the vaccine-hesitant individuals to get vaccinated as well as prevent the pro-vaccine individuals from realizing their vaccination intentions. Developing evidence-based persuasive and targeted communication strategies can help convince the unvaccinated and increase the uptake of COVID-19 vaccines. Targeted communication strategies can also help reduce disparities among populations that have lower vaccination rates. Moreover, such strategies may also be useful to increase the uptake of other vaccines such as human papillomavirus (HPV) vaccines to lower the rates of HPV-attributable cancers in the United States. Citation Format: Dhriti Dhawan, Mesfin Awoke Bekalu, Ramya Pinnamaneni, Rachel McCloud, Kasisomayajula Viswanath. Vaccine hesitancy: Can we convince the unvaccinated to vaccinate? [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A125.

Agreement with COVID-19 disinformation among Portuguese-speaking older adults: an international study

RESUMO Objetivos: avaliar a concordância com conteúdos de desinformação relacionados à COVID-19 entre pessoas com 50 anos ou mais de idade, falantes do português. Métodos: estudo descritivo e analítico com 1.214 idosos nascidos em países de língua portuguesa. A coleta de dados ocorreu por meio da mineração de informações online para reconhecimento de conteúdos de desinformação e COVID-19, e aplicação de questionário estruturado. Resultados: a concordância com conteúdos de desinformação foi de 65,2%. Residir fora do Brasil é um fator de proteção para a concordância com conteúdos de desinformação, e aqueles que acreditam na veracidade das fontes de informação que recebem tiveram 31% mais probabilidade de concordar com conteúdos de desinformação. Conclusões: há alta prevalência de desinformação entre a população idosa em dois países de língua portuguesa, o que deve despertar a atenção dos profissionais da saúde e enfermagem, e orientar estratégias de enfrentamento.

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using ELECTRA model, which achieved 85.11% accuracy score, is thus reported in this manuscript. In addition to that, a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been contributed. Using the COVAX-Reality dataset, the performance of FNEC is compared to several traditional learning models i.e., Support Vector Machine (SVM), Naive Bayes (NB), Passive Aggressive Classifier (PAC), Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM) and Bi-directional Encoder Representations from Transformers (BERT). For the evaluation of FNEC, standard metrics (Precision, Recall, Accuracy, and F1-Score) were utilized.

A Multimodal Ensemble Machine Learning Approach to COVID-19 Misinformation Detection in Twitter

The emergence of social media platforms has unquestionably altered the manner in which people ingest information, with tweets now functioning as the primary source for news and other types of content. However, the proliferation of false news on these platforms has become a major concern, as it poses a severe threat to both individuals and society as a whole. Consequently, it is crucial to develop efficient methods for detecting false news in tweets. This study presents a novel hybrid approach that integrates the textual content of tweets with auxiliary features to detect false news. Our approach uses a pre-trained transformer-based language model, COVID-twitter-BERT to encode the text content of tweets into a dense representation that captures their meaning. The auxiliary features, such as sentiment score, credibility score, engagement score, average retweet count, average favourite count, and average followers of followers, are fed into a stacking classifier-based model to predict the trustworthiness score of the tweet. By combining the predictions of both models, we demonstrate that our approach outperforms baseline methods, emphasising the significance of utilising both text content and auxiliary features for Twitter false news detection. Our research considerably advances the field of detecting false news by demonstrating the effectiveness of integrating transformer-based language models and machine learning models for this task. Our findings provide valuable insights for improving the detection of false news on social media.

Antecedents and Consequences of Misinformation Sharing Behavior among Adults on Social Media during COVID-19.

Misinformation has been existed for centuries, though emerge as a severe concern in the age of social media, and particularly during COVID-19 global pandemic. As the pandemic approached, a massive influx of mixed quality data appeared on social media, which had adverse effects on society. This study highlights the possible factors contributing to the sharing and spreading misinformation through social media during the crisis. Preferred Reporting Items and Meta-Analysis guidelines were used for systematic review. Anxiety or risk perception associated with COVID-19 was one of the significant motivators for misinformation sharing, followed by entertainment, information seeking, sociability, social tie strength, self-promotion, trust in science, self-efficacy, and altruism. WhatsApp and Facebook were the most used platforms for spreading rumors and misinformation. The results indicated five significant factors associated with COVID-19 misinformation sharing on social media, including socio-demographic characteristics, financial considerations, political affiliation or interest, conspiracy ideation, and religious factors. Misinformation sharing could have profound consequences for individual and society and impeding the efforts of government and health institutions to manage the crisis. This SLR focuses solely on quantitative studies, hence, studies are overlooked from a qualitative standpoint. Furthermore, this study only looked at the predictors of misinformation sharing behavior during COVID-19. It did not look into the factors that could curb the sharing of misinformation on social media platforms as a whole. The study's findings will help the public, in general, to be cautious about sharing misinformation, and the health care workers, and institutions, in particular, for devising strategies and measures to reduce the flow of misinformation by releasing credible information through concerned official social media accounts. The findings will be valuable for health professionals and government agencies to devise strategies for handling misinformation during public health emergencies.

Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genread hocpipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it)beforethe misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking—a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading.

Digital society social interactions and trust analysis model.

During unprecedented events such as COVID-19, the fabric of society comes under stress and all stakeholders want to increase the predictability of the future and reduce the ongoing uncertainties. In this research, an attempt has been made to model the situation in which the sentiment "trust" is computed so as to map the behaviour of society. However, technically, the purpose of this research is not to determine the "degree of trust in society" as a consequence of some specific emotions or sentiments that the community is experiencing at any particular time. This project is concerned with the construction of a computational model that can assist in improving our understanding of the dynamics of digital societies, particularly when it comes to the attitude referred to as "trust." The digital society trust analysis (D.S.T.A.) model that has been provided is simple to configure and simple to implement. It includes many previous models, such as standing models, Schelling's model of segregation, and tipping points, in order to construct models for understanding the dynamics of a society reeling under the effects of a COVID-19 pandemic, misinformation, fake news, and other sentiments that impact the behaviour of the different groups.

Varieties of corona news: a cross-national study on the foundations of online misinformation production during the COVID-19 pandemic.

The online version contains supplementary material available at 10.1007/s42001-022-00193-5.

Truth on Demand: Influences on How Journalists in Italy, Spain, and Bulgaria Responded to Covid-19 Misinformation and Disinformation

ABSTRACT The COVID-19 pandemic significantly disrupted fundamental social institutions, and the media was no exception. Journalists were challenged to report on an outbreak of pressing health-related information, complicated by malinformation erupting in a time of enhanced public demand for reliable facts. This study uses in-depth interviews with journalists at leading news outlets- both public service and commercial, in Italy, Spain, and Bulgaria (N = 24) to examine how they responded to misinformation and disinformation arising in the initial months of the pandemic. Using the frameworks of journalistic epistemology and the hierarchy-of-influences model, we explored the norms, routines, and practices guiding journalists’ truth claims as well as the individual, routine, organizational, and social-systems influences shaping their decision-making. We found three common narratives of conspiracy theories consistent across different media systems: “big state” concerns, evident in people questioning the very existence of the coronavirus as a pretense for globally enforced state supervision on individual freedoms; “big pharma,” or people blaming the lack of a cure for COVID-19 on pharmaceutical companies pushing for vaccinations; and “big fear” attitude - falsehoods about vaccines potentially causing more harm than good, including changing people’s DNA. We also discuss three common strategies journalists used to respond to COVID-misinformation: deflecting, countering and false equivalency approach.

Overcoming COVID-19 Misinformation: Lessons Learned at the Epicentre of the Outbreak in the USA

THE COVID-19 pandemic has highlighted the dire need to foster increased public confidence in mitigation and prevention strategies through more and better health literacy. More than 2 years into the worst public health crisis of the 21st century, we continue to be consumed by the most basic health questions: should I get tested for COVID-19, should I get vaccinated and boosted against COVID-19, and should I wear a mask? In many countries, the tension between personal freedoms and public good helps to fuel a global threat, with continued transmission of severe acute respiratory syndrome coronavirus 2 and its evolving, more infectious variants. This short essay discusses the negative effects of misinformation and disinformation, and shares recommendations based on lessons learned.

Online extremism and Islamophobic language and sentiment when discussing the COVID-19 pandemic and misinformation on Twitter

ABSTRACT This paper looks at the profiles of those who engaged in Islamophobic language/extremist behaviour on Twitter during the COVID-19 pandemic. This two-part analysis takes into account factors such as anonymity, membership length and postage frequency on language use, and the differences in sentiment expressed between pro-social and anti-social tweets. Analysis includes comparisons between low, moderate and high levels of anonymity, postage frequency and membership length, allowing for differences in keyword use to be explored. Our findings suggest that increased anonymity is not associated with an increase in Islamophobic language and misinformation. The sentiment analysis indicated that emotions such as anger, disgust, fear, sadness and trust were significantly more associated with pro-social Twitter users whereas sentiments such as anticipation, joy and surprise were significantly more associated with anti-social Twitter users. In some cases, evidence for joy in the suffering of others as a result of the pandemic was expressed.

Workplace Deceptions During the Pandemic: Differences in Conspiracy Beliefs, Psychological Functioning, and Covid-19 Experiences.

The global pandemic has disrupted virtually all countries on health, psychological functioning, and economies, to name a few. Accurate information has also fallen victim to the pandemic, which has been rife with misinformation and conspiracy theories. The current study investigated Covid-19 deceptions related to employment. With complete anonymity via MTurk, 389 participants from the United States rated their likelihood of deception regarding hypothetical four workplace scenarios. The first set of analyses examined differences between high and low risk of deceptions for each scenario based on participants' self-appraisals. The largest differences were found for general conspiracy beliefs and affective disorders, specifically major depression and generalized anxiety. The second set of analyses focused across the workplace scenarios on two operationalized groups with Likely-Deceptive (n = 189) vastly outnumbering Likely-Genuine (n = 55). Personal experiences with Covid-19 dramatically increased deceptions. Testing positive for Covid-19 increased the odds of being in the Likely-Deceptive by twelve-fold. Two discriminant models examined cognitive misbeliefs and psychological functioning. When both were combined, depression and Covid-19 misinformation produced the strongest structure coefficients followed closely by general conspiracy beliefs and generalized anxiety. The far-ranging implications of these findings are discussed.

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations.

Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for instance, is crucial for the success of the global vaccination programs and thus achieving herd immunity. To combat the proliferation of COVID-19-related misinformation, considerable research efforts have been and are still being dedicated to building and sharing COVID-19 misinformation detection datasets and models for Arabic and other languages. However, most of these datasets provide binary (true/false) misinformation classifications. Besides, the few studies that support multi-class misinformation classification deal with a small set of misinformation classes or mix them with situational information classes. False news stories about COVID-19 are not equal; some tend to have more sinister effects than others (e.g., fake cures and false vaccine info). This suggests that identifying the sub-type of misinformation is critical for choosing the suitable action based on their level of seriousness, ranging from assigning warning labels to the susceptible post to removing the misleading post instantly. We develop comprehensive annotation guidelines in this work that define 19 fine-grained misinformation classes. Then, we release the first Arabic COVID-19-related misinformation dataset comprising about 6.7K tweets with multi-class and multi-label misinformation annotations. In addition, we release a version of the dataset to be the first Twitter Arabic dataset annotated exclusively with six different situational information classes. Identifying situational information (e.g., caution, help-seeking) helps authorities or individuals understand the situation during emergencies. To confirm the validity of the collected data, we define three classification tasks and experiment with various machine learning and transformer-based classifiers to offer baseline results for future research. The experimental results indicate the quality and validity of the data and its suitability for constructing misinformation and situational information classification models. The results also demonstrate the superiority of AraBERT-COV19, a transformer-based model pretrained on COVID-19-related tweets, with micro-averaged F-scores of 81.6% and 78.8% for the multi-class misinformation and situational information classification tasks, respectively. Label Powerset with linear SVC achieved the best performance among the presented methods for multi-label misinformation classification with micro-averaged F-scores of 76.69%.