Who Does Not Benefit from Fact-checking Websites?: A Psychological Characteristic Predicts the Selective Avoidance of Clicking Uncongenial Facts

Fact-checking messages are shared or ignored subjectively. Users tend to seek like-minded information and ignore information that conflicts with their preexisting beliefs, leaving like-minded misinformation uncontrolled on the Internet. To understand the factors that distract fact-checking engagement, we investigated the psychological characteristics associated with users' selective avoidance of clicking uncongenial facts. In a pre-registered experiment, we measured participants' (N = 506) preexisting beliefs about COVID-19-related news stimuli. We then examined whether they clicked on fact-checking links to false news that they believed to be accurate. We proposed an index that divided participants into fact-avoidance and fact-exposure groups using a mathematical baseline. The results indicated that 43% of participants selectively avoided clicking on uncongenial facts, keeping 93% of their false beliefs intact. Reflexiveness is the psychological characteristic that predicts selective avoidance. We discuss susceptibility to click bias that prevents users from utilizing fact-checking websites and the implications for future design. © 2023 Owner/Author.

A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic

Information disorders on social media can have a significant impact on citizens' participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.

Understanding the causes and consequence of COVID-19 information crisis in Africa: Defining an agenda for effective social media engagement during health pandemics

The chapter argues that the COVID-19 information crisis in Africa needs to be understood within the extant global 'disinformation order,' which is characterized by distorted information flow, the preponderance of fake news, and the general dysfunctions of the social media. Recommendations for enhancing science-driven communication are proposed. The chapter further contends that COVID-19 information context was compounded by a lack of truth and trust between the public and critical influencers, such as health policymakers, scientists, and the media. From the analysis, the author argues that lessons learned from the crisis should inform future risk communication and engagement of social identities in health behavior. This is consistent with the editorial position of Health Promotion International, which posits that health promotion researchers should learn from analyzing the reactions and documenting the learning (Van den Broucke, 2020, p. 5). It also calls for a robust agenda for leveraging the impact of social media during health emergencies and pandemics. Finally, the chapter underscores the challenge of promoting health behavior among different social identities, especially during contested information ecology and extensive health misinformation on social media and the Internet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reserved.

Wisdom of Two Crowds: Misinformation Moderation on Reddit and How to Improve this Process - -A Case Study of COVID-19

Past work has explored various ways for online platforms to leverage crowd wisdom for misinformation detection and moderation. Yet, platforms often relegate governance to their communities, and limited research has been done from the perspective of these communities and their moderators. How is misinformation currently moderated in online communities that are heavily self-governed? What role does the crowd play in this process, and how can this process be improved? In this study, we answer these questions through semi-structured interviews with Reddit moderators. We focus on a case study of COVID-19 misinformation. First, our analysis identifies a general moderation workflow model encompassing various processes participants use for handling COVID-19 misinformation. Further, we show that the moderation workflow revolves around three elements: content facticity, user intent, and perceived harm. Next, our interviews reveal that Reddit moderators rely on two types of crowd wisdom for misinformation detection. Almost all participants are heavily reliant on reports from crowds of ordinary users to identify potential misinformation. A second crowd - participants' own moderation teams and expert moderators of other communities - provide support when participants encounter difficult, ambiguous cases. Finally, we use design probes to better understand how different types of crowd signals - -from ordinary users and moderators - -readily available on Reddit can assist moderators with identifying misinformation. We observe that nearly half of all participants preferred these cues over labels from expert fact-checkers because these cues can help them discern user intent. Additionally, a quarter of the participants distrust professional fact-checkers, raising important concerns about misinformation moderation. © 2023 ACM.

Public sphere and disinformation in a local context

Disinformation is not a new phenomenon. Even so, in recent years its relevance on the public agenda has increased, as the victory of Brexit supporters in the UK or the election of Donald Trump for US president have showed. Academic interest runs parallel to the consideration of disinformation as a growing priority for governments and international organizations. On the other hand, the emergence of the Covid-19 pandemic accelerated the decline of regional media, already affected by the digital transformation and declining business models, now challenged by the platforms, which became essential mediators in the advertising market. The decline of regional media leaves communities in a state of serious vulnerability as information is increasingly consumed through social media, where disinformation easily proliferates. As in the pandemic context, disin-formation is also a virus that spreads quickly and has a high potential for damage to democracy, namely at a local level. It is precisely where we intend to focus the debate, curiously where it has been little present. It is precisely from the local public sphere that responses to disinformation can emerge, namely in a collaborative relationship between journalists and (other) active members of the community.

CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets

Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger to people's health, particularly when false information is shared, for instance recommendations on how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for the medical domain are crucial. While existing fact-checking resources cover COVID-19-related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19-related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge indirectly available in pretrained language models.

Latino-Targeted Misinformation and the Power of Factual Corrections

Do individual, interpersonal, or institutional factors condition the effects of misinformation on beliefs? Can interventions such as fact checks stem the tide of the "infodemic” within marginalized communities? We explore the sudden flood of misinformation and disinformation targeting Latinos during the 2020 election and global COVID-19 pandemic to answer these questions. In a preregistered experiment, we find that exposure to misinformation can decrease factual accuracy, and neither trust in nor consumption of media, including ethnic media, serves as a buffer against these misinformation effects. However, fact checks eliminate the effects of misinformation on false beliefs without "backfiring” and reducing accuracy. Fact checks improve factual accuracy among subgroups varying in levels of political knowledge, trust, and acculturation. These findings provide crucial support for recent investments into fact checking by Latino-oriented media outlets and address gaps within the literature over whether such interventions are also effective within marginalized groups.

Closing the Barn Door? Fact-Checkers as Retroactive Gatekeepers of the COVID-19 "Infodemic”

Based on a study of U.S.-tagged items in a global database of fact-checked statements about the novel coronavirus throughout the first year of the pandemic, this article explores the nature of fact-checkers' "retroactive gatekeeping.” This term is introduced here to describe the process of assessing the veracity of information after it has entered the public domain rather than before. Although an overwhelming majority of statements across 16 thematic categories were deemed false and debunked, often repeatedly, misinformation continued to circulate freely and widely. [ FROM AUTHOR] Copyright of Journalism & Mass Communication Quarterly is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

True or false? Cognitive load when reading COVID-19 news headlines: An eye-tracking study

Misinformation is an important topic in the Information Retrieval (IR) context and has implications for both system-centered and user-centered IR. While it has been established that the performance in discerning misinformation is affected by a person's cognitive load, the variation in cognitive load in judging the veracity of news is less understood. To understand the variation in cognitive load imposed by reading news headlines related to COVID-19 claims, within the context of a fact-checking system, we conducted a within-subject, lab-based, quasi-experiment (N=40) with eye-tracking. Our results suggest that examining true claims imposed a higher cognitive load on participants when news headlines provided incorrect evidence for a claim and were inconsistent with the person's prior beliefs. In contrast, checking false claims imposed a higher cognitive load when the news headlines provided correct evidence for a claim and were consistent with the participants' prior beliefs. However, changing beliefs after examining a claim did not have a significant relationship with cognitive load while reading the news headlines. The results illustrate that reading news headlines related to true and false claims in the fact-checking context impose different levels of cognitive load. Our findings suggest that user engagement with tools for discerning misinformation needs to account for the possible variation in the mental effort involved in different information contexts. © 2023 ACM.

Fact-Checking Journalism: A Palliative Against the COVID-19 Infodemic in Ibero-America

This study explores how fact-checkers understand information disorder in Ibero-America, in particular the COVID-19 disinformation. We conducted a quantitative content analysis of the LatamChequea Coronavirus alliance database and in-depth interviews with journalists from the network. Evidence found that one of the most prevalent disinformation topics was the government's restrictive measures, threatening to jeopardize the effectiveness of public health campaigns. This, added to disinformation that eroded the trust in the institutions and the press, and the opacity of governments constituted a political crisis in Ibero-America. Under this scenario, fact-checkers created relevant journalistic collaborations and strategies to fight disinformation in the region. [ FROM AUTHOR] Copyright of Journalism & Mass Communication Quarterly is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

User agency–based versus machine agency–based misinformation interventions: The effects of commenting and AI fact-checking labeling on attitudes toward the COVID-19 vaccination

This study aimed to examine the effects of commenting on a Facebook misinformation post by comparing a user agency–based intervention and machine agency–based intervention in the form of artificial intelligence (AI) fact-checking labeling on attitudes toward the COVID-19 vaccination. We found that both interventions were effective at promoting positive attitudes toward vaccination compared to the misinformation-only condition. However, the intervention effects manifested differently depending on participants' residential locations, such that the commenting intervention emerged as a promising tool for suburban participants. The effectiveness of the AI fact-checking labeling intervention was pronounced for urban populations. Neither of the fact-checking interventions showed salient effects with the rural population. These findings suggest that although user agency- and machine agency–based interventions might have potential against misinformation, these interventions should be developed in a more sophisticated way to address the unequal effects among populations in different geographic locations. [ FROM AUTHOR] Copyright of New Media & Society is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Automated Fact-Checking of Articles Related to Neurodevelopmental Disorders

False claims or Fake News related to the health care or medicine field on Social Media have garnered increasing amounts of interest, especially in the aftermath of the COVID-19 pandemic. False claims about the pan-demic which spread on social media have contributed to vaccine hesitancy and lack of trust in the advise of medical professionals. If not detected and disproved early, such claims can complicate future pandemic responses. We focus on false claims in the field of Neurodevelopmental Disorders (NDDs), which is an umbrella term for a group of disorders that includes Autism, ADHD, Cerebral Palsy, etc. In this paper we present our approach to automated systems for fact-checking medical articles related to NDDs. We also present an annotated dataset of 116 web pages which we use to test our model and present our results. © 2022 IEEE.

Global political leaders during the COVID-19 vaccination: Between propaganda and fact-checking.

The advent of COVID-19 vaccination meant a moment of hope after months of crisis communication. However, the context of disinformation on social media threatened the success of this public health campaign. This study examines how heads of government and fact-checking organizations in four countries managed communications on Twitter about the vaccination. Specifically, we conduct a content analysis of their discourses through the observation of propaganda mechanisms. The research draws on a corpus of words related to the pandemic and vaccines in France, Spain, the United Kingdom, and the United States (n = 2,800). The data were captured for a five-month period (January-May 2021), during which COVID-19 vaccines became available for elderly people. The results show a trend of clearly fallacious communication among the political leaders, based on the tools of emphasis and appeal to emotion. We argue that the political messages about the vaccination mainly used propaganda strategies. These tweets also set, to a certain extent, the agendas of the most relevant fact-checking initiatives in each country.

A computational model for assessing experts' trustworthiness

The algorithmic detection of disinformation online is currently based on two strategies: on the one hand, research focuses on automated fact-checking;on the other hand, models are being developed to assess the trustworthiness of information sources, including both empirical and theoretical research on credibility and content quality. For debates among experts, in particular, it might be hard to discern (less) reliable information, as all actors by definition are qualified. In these cases, the use of trustworthiness metrics on sources is a useful proxy for establishing the truthfulness of contents. We introduce an algorithmic model for automatically generating a dynamic trustworthiness hierarchy among information sources based on several parameters, including fact-checking. The method is novel and significant, especially in two respects: first, the generated hierarchy represents a helpful tool for laypeople to navigate experts' debates;second, it also allows to identify and overcome biases generated by intuitive rankings held by agents at the beginning of the debates. We provide an experimental analysis of our algorithmic model applied to the debate on the SARS-CoV-2 virus, which took place among Italian medical specialists between 2020 and 2021. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Who is who in fact-checked conspiracy theories? Disseminators, sources, and the struggle for authority in polarized environments

Despite the growing scholarship concerning mis- and disinformation, research has yet to assess how journalism tackles conspiracy theories in settings where news organizations and media professionals have their authority questioned. Against this background, our article poses two research questions: RQ1) Who are the actors mainstream fact-checkers cover when addressing conspiracy theories? RQ2) To what extent does the focus on such actors delegitimize those who challenge news organizations or compete with them for the audience? Using content analysis as our key methodological strategy, we consider 197 fact-checks published between 2018 and 2021 by the Projeto Comprova, a Brazilian initiative currently comprising professionals from 41 media organizations. We found that the most discussed topics were those mobilizing polarized groups, namely, the Covid-19 pandemic and election fraud allegations. The then-President Bolsonaro and his supporters were often cited as disseminators of such plots. In turn, Facebook is pointed out as a thriving environment for the circulation of conspiratorial narratives. The results also reveal the prominence of mainstream news outlets as sources to ground the "factual” information sustained in the fact-checks. More interestingly, our data suggest that fact-checkers have favored specific news values when addressing conspiracy theories. To strengthen our investigation, we use interviews with four professionals contributing to the Comprova to illustrate how the project has brought together rival companies interested in delegitimizing "alternative” sources of information. © The Author(s) 2023.

Impact of fake news on social networks during COVID-19 pandemic in Spain

Purpose: The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021. Design/methodology/approach: A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society. Findings: Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country's situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms. Research limitations/implications: The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z. Originality/value: This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers. © 2023, María Teresa Macarrón Máñez, Antonia Moreno Cano and Fernando Díez.

All the President's Lies: How Brazilian News Media Addressed False and Inaccurate Claims in Their Titles

This paper examines how the Brazilian media covered false and misleading claims made by President Jair Bolsonaro in the first year of the Covid-19 pandemic. To do so, we extracted titles from 111 news articles connected to twenty-one disinformation episodes. The texts were published by six news organizations, and we classified them into three categories using content analysis: contextualization, correction, and reproduction. Among our findings, we have discovered that 60.36% of the content reproduced the president's discourse without acknowledging it was a lie or an error, while 26.13% presented elements of contextualization. Only 13.51% of titles corrected Bolsonaro's false or misleading claims. The results indicate that legacy and digital-native news organizations mostly failed to correct the Brazilian president's errors and lies, evoking questions about ethics and transparency. Consequentially, journalism may increase the noise in an already polluted and high-choice online media environment if such issues are left unaddressed. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Strategies for the Minimisation of Misinformation Spread Through the Local Media Environment

Coping with misinformation in a media environment is one of the challenges that undermine the role of the media. The pandemic of Covid-19 influenced the media organisations in their working processes significantly and a decrease in accuracy was one of the findings of our study. This paper examines the impact of the pandemic in local media to journalists' practices and the role of the media. We aimed to research the effects of the pandemic in the local environment on journalistic procedures and the media's role after this period. In answering the research questions, we used the method of a structured questionnaire for journalists and semi-structured in-depth interviews for local News Editors. We aimed to research the media production practices on various levels of professionals included in local news. We detected those production practices that could endanger the credibility of the local media. The findings provided insights into how Slovenian local journalists and editors define, understand, and practice fact-checking. The results led to an understanding that the perception of the media role in offering information was higher during the pandemic period and that human verification is key in ensuring the credibility of content in local newsrooms. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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

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.

Empowering COVID-19 Fact-Checking with Extended Knowledge Graphs

During the COVID-19 outbreak, fake news regarding the disease have spread at an increasing rate. Let's think, for instance, to face masks wearing related news or various home-made treatments to cure the disease. To contrast this phenomenon, the fact-checking community has intensified its efforts by producing a large number of factchecking reports. In this work, we focus on empowering knowledge-based approaches for misinformation identification with previous knowledge gathered from existing fact-checking reports. Very few works in literature have exploited the information regarding claims that have been already fact-checked. The main idea that we explore in this work is to exploit the detailed information in the COVID-19 fact check reports in order to create an extended Knowledge Graph. By analysing the graph information about the already checked claims, we can verify newly coming content more effectively. Another gap that we aim to fill is the temporal representation of the facts stored in the knowledge graph. At the best of our knowledge, this is the first attempt to associate the temporal validity to the KG relations. This additional information can be used to further enhance the validation of claims.