Fact-checking Websites Research Articles

Comparison of Objectivity, Transparency, Contextualization, and Trust Between Fact-Checking Sites and News Organizations

ABSTRACT This study compares U.S. adults’ perception about the performance of news organizations and independent fact-checking sites. Drawing upon a survey of 1580 respondents, this study finds that rating of the objectivity and trust in news organizations is somewhat higher than that of fact-checking sites. Fact-checking sites, however, are perceived to give more contextual information about current topics than news organizations. In terms of transparency, no significant difference is found. The analysis also finds that the positive perceptions about objectivity, transparency, contextualization, and trust significantly predict fact-checking site use. The results suggest that audiences perceive fact-checking sites to play a journalistic role to some extent, which may blur the boundary of journalism.

Hoaxes’ anatomy: Analysis of disinformation during the coronavirus pandemic in Europe (2020-2022)

The escalating proliferation of disinformation throughout Europe, exacerbated by the onset of the COVID-19 pandemic, necessitates a rigorous examination of its ramifications for public health. This study undertakes an investigation into trends in pandemic-related disinformation in Spain, Germany, the United Kingdom, and Poland spanning the period from 2020 to 2022. In this way, a meticulous content analysis of 704 publications from fact-checking sites was conducted. To augment the study’s robustness and provide a nuanced perspective on the evolving nature of disinformation, case studies were meticulously incorporated. The data reveals that despite of the professionalization of deep fakes, 43% of disinformation propagated through textual means, predominantly leveraging social networks (75%), especially Facebook and WhatsApp. Notably, misinformation pertaining to vaccines constituted the most prevalent narrative among disinformation stories, comprising 25.85% of the total. Moreover, as the pandemic unfolded, the thematic focus of disinformation adapted to the distinct contexts of each country. Furthermore, 47.16% of the content was found to be fabricated, with the primary objective of amplifying a common adversary accounting for 39.77% of cases. Given the contemporary relevance and public significance of this subject, the overarching objective of this research has been to scrutinise the dynamics of disinformation related to the COVID-19 pandemic in Europe. Consequently, the findings of this study carry a distinct European public service orientation.

Why media platforms police the boundaries of impartiality: A comparative analysis of television news and fact-checking in the UK

This article explores whether different media platforms across impartial news media supplied the same level of scrutiny in how they fact-checked political claims. While prior research has largely focused on independent fact-checking organisations, the fact-checking practices of legacy media through a cross-platform perspective have comparatively received limited attention. The study develops new lines of inquiry into the fact-checking practices of legacy media, presenting one of the largest and most forensic cross-platform studies of fact-checking to date. It draws on a systematic content analysis of 355 items from fact-checking sites, including 689 claims and 1850 instances where journalists or sources interacted with them in 2021, and assesses how they were covered by a further 280 television news items. Our findings demonstrate that the selection and degree to which journalists and sources scrutinised political claims varied across media platforms, with television news less inclined to report and analyse policy claims than dedicated fact-checking websites. Overall, we argue that the editorial boundaries of fact-checking are policed by journalists’ interpretations of impartiality, which differ across platforms (in television news or dedicated fact-checking websites) due to a range of editorial factors such as production constraints and news values.

Community Notes vs. Snoping: How the Crowd Selects Fact-Checking Targets on Social Media

Deploying links to professional fact-checking websites (so-called “snoping”) is a common misinformation intervention technique that can be used by social media users to refute misleading claims made by others. However, the real-world effect of snoping may be limited as it suffers from low visibility and distrust towards professional fact-checkers. As a remedy, X (formerly known as Twitter) recently launched its community-based fact-checking system “Community Notes” on which fact-checks are carried out by actual X users and directly shown on the fact-checked posts. Yet, an understanding of how fact-checking via Community Notes differs from regular snoping is largely absent. In this study, we empirically analyze differences in how contributors to Community Notes and Snopers select their targets when fact-checking social media posts. For this purpose, we collect and holistically analyze two unique datasets from X: (a) 25,912 community-created fact-checks from X's Community Notes platform, and (b) 52,505 “snopes” that debunk posts via fact-checking replies that link to professional fact-checking websites. We find that Notes contributors and Snopers focus on different targets when fact-checking social media content. For instance, Notes contributors tend to fact-check posts from larger accounts with higher social influence and are relatively less likely to emphasize the accuracy of non-misleading posts. Fact-checking targets of Notes contributors and Snopers rarely overlap; however, those overlapping exhibit a high level of agreement in the fact-checking assessment. Moreover, we demonstrate that Snopers fact-check social media posts at a higher speed. Altogether, our findings imply that different fact-checking approaches – carried out on the same social media platform – can result in vastly different social media posts getting fact-checked. This has important implications for future research on misinformation, which should not rely on a single fact-checking approach when compiling misinformation datasets. From a practical perspective, our findings imply that different fact-checking approaches complement each other and may help social media providers to optimize strategies to combat misinformation on their platforms.

Redibujando las líneas contra la desinformación: Cómo la IA está modificando el presente y futuro del fact-checking

Artificial intelligence is changing the way our world works, and the journalism and communication field is no exception. The development of high technologies such as NLP or machine learning has modified professional routines, work profiles, and business models. Fact-checking initiatives, which have long battled disinformation, now face a complex context where misleading content grows faster than ever. In this situation, artificial intelligence, or AI, can be a double-edged sword. On the one side, AI-generated content can be created faster than regular content; therefore, there is a huge volume of data to be analysed by fact-checkers. Furthermore, NLP software is not always as reliable as it might be expected. It tends to ‘hallucinate’, creating more misleading content and hoaxes. On the other hand, AI can be a helpful tool in fighting disinformation. This paper analyses 10 independent international fact-checking initiatives through case analysis and questionnaires with fact-checkers. Results show that these sites use AI during different stages of their routines, accelerating processes, simplifying tasks and improving the accuracy of fact-checking results. AI integration shows some risks related to economic restrictions, platform limitations, media distrust, and inequity between countries. To conclude, this research also demonstrates that journalists are still in the loop about fact-checking sites, but more tech profiles and better skills are required.

"Pinocchio had a Nose, You have a Network!": On Characterizing Fake News Spreaders on Arabic Social Media

The detection and analysis of fake news and its origins has become a main task associated with the overall objective of social media regulation in recent years. The majority of work has been dedicated towards detecting misinformation with some focus on analyzing the flow of fake news across social networks. However, there is less attention to the characteristics of social media users who consume this fake news. In this work, we investigate the possibility of predicting users' reactions towards fake news and defining some network characteristics for each users' group. We utilized a set of fact-checking websites in the Arab world that report social media posts spreading fake news and the interactions with them. We defined three sets of users: 1) Spreaders, who spread fake news, 2) Checkers, who constantly share fact-checking threads, and 3) Refuters, who respond to fake-news posts declaring their inaccuracy. We build a classifier that uses users' network graph to predict their reactions with an accuracy exceeding 93%. We applied further analysis for the most significant features of each users group and noticed that spreaders interact with more accounts that use their mother tongue, a considerable number of famous state-sponsored accounts, and accounts that get suspended while checkers and refuters interact with more foreign accounts and news-reporting entities. Central nodes in the networks of spreaders were found to be linked with state-sponsored media, whereas central nodes in the networks of checkers included organizations with a cross-cultural nature.

The geography of corporate fake news.

Although a rich academic literature examines the use of fake news by foreign actors for political manipulation, there is limited research on potential foreign intervention in capital markets. To address this gap, we construct a comprehensive database of (negative) fake news regarding U.S. firms by scraping prominent fact-checking sites. We identify the accounts that spread the news on Twitter (now X) and use machine-learning techniques to infer the geographic locations of these fake news spreaders. Our analysis reveals that corporate fake news is more likely than corporate non-fake news to be spread by foreign accounts. At the country level, corporate fake news is more likely to originate from African and Middle Eastern countries and tends to increase during periods of high geopolitical tension. At the firm level, firms operating in uncertain information environments and strategic industries are more likely to be targeted by foreign accounts. Overall, our findings provide initial evidence of foreign-originating misinformation in capital markets and thus have important policy implications.

Developing a hierarchical model for unraveling conspiracy theories

A conspiracy theory (CT) suggests covert groups or powerful individuals secretly manipulate events. Not knowing about existing conspiracy theories could make one more likely to believe them, so this work aims to compile a list of CTs shaped as a tree that is as comprehensive as possible. We began with a manually curated ‘tree’ of CTs from academic papers and Wikipedia. Next, we examined 1769 CT-related articles from four fact-checking websites, focusing on their core content, and used a technique called Keyphrase Extraction to label the documents. This process yielded 769 identified conspiracies, each assigned a label and a family name. The second goal of this project was to detect whether an article is a conspiracy theory, so we built a binary classifier with our labeled dataset. This model uses a transformer-based machine learning technique and is pre-trained on a large corpus called RoBERTa, resulting in an F1 score of 87%. This model helps to identify potential conspiracy theories in new articles. We used a combination of clustering (HDBSCAN) and a dimension reduction technique (UMAP) to assign a label from the tree to these new articles detected as conspiracy theories. We then labeled these groups accordingly to help us match them to the tree. These can lead us to detect new conspiracy theories and expand the tree using computational methods. We successfully generated a tree of conspiracy theories and built a pipeline to detect and categorize conspiracy theories within any text corpora. This pipeline gives us valuable insights through any databases formatted as text.

Show Me the Facts: Newsroom-Affiliated and Independent Fact-Checkers’ Transparency Acts

In response to skepticism and criticisms of bias, fact-checkers attempt to be transparent in their work, a practice adopted from their journalistic roots. This study identifies the acts of disclosure and participatory transparency practiced by fact-checkers in Asia and compares the differences between newsroom-affiliated and non-newsroom fact-checkers. Through a content analysis of 80 fact-checking websites in Asia, this study identifies the acts of disclosure and participatory transparency practiced by fact-checkers in Asia. This study also finds that newsroom fact-checkers engage in fewer acts of disclosure transparency than non-newsroom fact-checkers. Specifically, non-newsroom fact-checkers are more likely to explain their editorial process, disclose their correction policy, disclose their funding sources, and disclose the names and biography of their editorial team. While newsroom and non-newsroom fact-checkers did not differ in the number of acts of participatory transparency, non-newsroom fact-checkers were more likely to allow users to submit claims. These indicate that akin to journalism, fact-checking as an institution displays some differences in their acceptance of disclosure and participatory transparency as a norm.

Misinformation and harmful language are interconnected, rather than distinct, challenges.

There is considerable concern about users posting misinformation and harmful language on social media. Substantial-yet largely distinct-bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from N = 6,832 Twitter (now called X) users, and a dataset of N = 14,617 true and false headlines from professional fact-checking websites. Our analyses reveal substantial positive associations between misinformation and harmful language. On average, Twitter posts containing links to lower-quality news outlets also contain more harmful language (β = 0.10); and false headlines contain more harmful language than true headlines (β = 0.19). Additionally, Twitter users who share links to lower-quality news sources also use more harmful language-even in non-news posts that are unrelated to (mis)information (β = 0.13). These consistent findings across different datasets and levels of analysis suggest that misinformation and harmful language are related in important ways, rather than being distinct phenomena. At the same, however, the strength of associations is not sufficiently high to make the presence of harmful language a useful diagnostic for information quality: most low-quality information does not contain harmful language, and a considerable fraction of high-quality information does contain harmful language. Overall, our results underscore important opportunities to integrate these largely disconnected strands of research and understand their psychological connections.

The adaptive community-response (ACR) method for collecting misinformation on social media

Social media can be a major accelerator of the spread of misinformation, thereby potentially compromising both individual well-being and social cohesion. Despite significant recent advances, the study of online misinformation is a relatively young field facing several (methodological) challenges. In this regard, the detection of online misinformation has proven difficult, as online large-scale data streams require (semi-)automated, highly specific and therefore sophisticated methods to separate posts containing misinformation from irrelevant posts. In the present paper, we introduce the adaptive community-response (ACR) method, an unsupervised technique for the large-scale collection of misinformation on Twitter (now known as ’X’). The ACR method is based on previous findings showing that Twitter users occasionally reply to misinformation with fact-checking by referring to specific fact-checking sites (crowdsourced fact-checking). In a first step, we captured such misinforming but fact-checked tweets. These tweets were used in a second step to extract specific linguistic features (keywords), enabling us to collect also those misinforming tweets that were not fact-checked at all as a third step. We initially present a mathematical framework of our method, followed by an explicit algorithmic implementation. We then evaluate ACR on the basis of a comprehensive dataset consisting of >25\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>25$$\\end{document} million tweets, belonging to >300\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>300$$\\end{document} misinforming stories. Our evaluation shows that ACR is a useful extension to the methods pool of the field, enabling researchers to collect online misinformation more comprehensively. Text similarity measures clearly indicated correspondence between the claims of false stories and the ACR tweets, even though ACR performance was heterogeneously distributed across the stories. A baseline comparison to the fact-checked tweets showed that the ACR method can detect story-related tweets to a comparable degree, while being sensitive to different types of tweets: Fact-checked tweets tend to be driven by high outreach (as indicated by a high number of retweets), whereas the sensitivity of the ACR method extends to tweets exhibiting lower outreach. Taken together, ACR’s capacity as a valuable methodological contribution to the field is based on (i) the adoption of prior, pioneering research in the field, (ii) a well-formalized mathematical framework and (iii) an empirical foundation via a comprehensive set of indicators.

A STUDY ON RELIABILITY OF NEWS SHARED IN SOCIAL MEDIA PLATFORMS (WITH REFERENCE TO WHATSAPP USERS)

Recent studies indicate that mobile messaging apps such as WhatsApp are widely used for sharing news by consumers. Media outlets have begun to disseminate news via social media sites like Facebook and WhatsApp. When it comes to chat app news, journalists still have a lot to learn. This study compares the trustworthiness of WhatsApp news to that of other social media platforms using the Diffusions of Innovations idea. This study discusses the reliability of news received through Whatsapp and the factchecking websites used by Vellore district Whatsapp users. For this purpose, various secondary data was studied, and a structured questionnaire was circulated among the Vellore district Whatsapp users, 200 samples were collected as primary data. Analysis was made using SPSS and the statistical tool chi-square test, SEM analysis and frequency table are used to derive the result. It is found that WhatsApp users in Vellore District rely on the news shared and they also check the authenticity of the news.

The PolitiFact-Oslo Corpus: A New Dataset for Fake News Analysis and Detection

This study presents a new dataset for fake news analysis and detection, namely, the PolitiFact-Oslo Corpus. The corpus contains samples of both fake and real news in English, collected from the fact-checking website PolitiFact.com. It grew out of a need for a more controlled and effective dataset for fake news analysis and detection model development based on recent events. Three features make it uniquely placed for this: (i) the texts have been individually labelled for veracity by experts, (ii) they are complete texts that strictly correspond to the claims in question, and (iii) they are accompanied by important metadata such as text type (e.g., social media, news and blog). In relation to this, we present a pipeline for collecting quality data from major fact-checking websites, a procedure which can be replicated in future corpus building efforts. An exploratory analysis based on sentiment and part-of-speech information reveals interesting differences between fake and real news as well as between text types, thus highlighting the importance of adding contextual information to fake news corpora. Since the main application of the PolitiFact-Oslo Corpus is in automatic fake news detection, we critically examine the applicability of the corpus and another PolitiFact dataset built based on less strict criteria for various deep learning-based efficient approaches, such as Bidirectional Long Short-Term Memory (Bi-LSTM), LSTM fine-tuned transformers such as Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa, and XLNet.

Sharing Reliable COVID-19 Information and Countering Misinformation: In-Depth Interviews With Information Advocates.

The rampant spread of misinformation about COVID-19 has been linked to a lower uptake of preventive behaviors such as vaccination. Some individuals, however, have been able to resist believing in COVID-19 misinformation. Further, some have acted as information advocates, spreading accurate information and combating misinformation about the pandemic. This work explores highly knowledgeable information advocates' perspectives, behaviors, and information-related practices. To identify participants for this study, we used outcomes of survey research of a national sample of 1498 adults to find individuals who scored a perfect or near-perfect score on COVID-19 knowledge questions and who also self-reported actively sharing or responding to news information within the past week. Among this subsample, we selected a diverse sample of 25 individuals to participate in a 1-time, phone-based, semistructured interview. Interviews were recorded and transcribed, and the team conducted an inductive thematic analysis. Participants reported trusting in science, data-driven sources, public health, medical experts, and organizations. They had mixed levels of trust in various social media sites to find reliable health information, noting distrust in particular sites such as Facebook (Meta Platforms) and more trust in specific accounts on Twitter (X Corp) and Reddit (Advance Publications). They reported relying on multiple sources of information to find facts instead of depending on their intuition and emotions to inform their perspectives about COVID-19. Participants determined the credibility of information by cross-referencing it, identifying information sources and their potential biases, clarifying information they were unclear about with health care providers, and using fact-checking sites to verify information. Most participants reported ignoring misinformation. Others, however, responded to misinformation by flagging, reporting, and responding to it on social media sites. Some described feeling more comfortable responding to misinformation in person than online. Participants' responses to misinformation posted on the internet depended on various factors, including their relationship to the individual posting the misinformation, their level of outrage in response to it, and how dangerous they perceived it could be if others acted on such information. This research illustrates how well-informed US adults assess the credibility of COVID-19 information, how they share it, and how they respond to misinformation. It illustrates web-based and offline information practices and describes how the role of interpersonal relationships contributes to their preferences for acting on such information. Implications of our findings could help inform future training in health information literacy, interpersonal information advocacy, and organizational information advocacy. It is critical to continue working to share reliable health information and debunk misinformation, particularly since this information informs health behaviors.

New Challenges for Indian Libraries Against the Fake News

This paper describes the concept of fake news in the Indian context. Additionally, it covers the various social media platforms and how they contribute to the creation and spread of fake news. The residents of our nation actively use a variety of social media platforms to disseminate information. The various characteristics, types, reasons, and impacts of fake news on society, etc. are also covered in this paper. Additional aspects related to preventing fake news are also included in this article, such as fact-checking websites, detection techniques, and actions to take. The new role and challenges faced by Indian libraries in this regard are also covered in this paper.

The relationship of artificial intelligence (AI) with fake news detection (FND): a systematic literature review

Purpose The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection. Design/methodology/approach “Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review. Findings Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news. Originality/value The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

Bullshit and Lies? How British and Spanish Political Leaders add to Our Information Disorder

Within what is known as the post-truth era, politicians strategically trade in alternative interpretations of data, make bold populist claims and on occasions be completely dishonest for party political gains. Such practices coincide with ever-declining trust in politicians and the democratic system, a phenomenon common to both Spain and the UK. We enquire whether public mistrust is deserved exploring the extent party leaders employ misinformation as part of their strategic communication. The paper analyses falsehoods made by political leaders as determined by major fact-checking sites EFE Verifica and Newtral in Spain, and the UK’s BBC Reality Check and Full Fact. We categorise falsehoods as misinformation, alternative facts, bullshit or lies. Results show right-wing parties most responsible for all forms of falsehoods, or they are most likely to face analysis from factcheckers. Falsehoods are used by governments defending their policies, but also by oppositions to attack the government; especially alternative facts. The overwhelming majority of policy attacks based on false information are from opposition parties, particularly Spanish parties on the right. The flagrant use of bullshit and lies, while simultaneously calling out their more mainstream opponents for similar practices, poisons the notion of democratic pluralism and makes low public trust seem perfectly justified.

Diffusion of Community Fact-Checked Misinformation on Twitter

The spread of misinformation on social media is a pressing societal problem that platforms, policymakers, and researchers continue to grapple with. As a countermeasure, recent works have proposed to employ non-expert fact-checkers in the crowd to fact-check social media content. While experimental studies suggest that crowds might be able to accurately assess the veracity of social media content, an understanding of how crowd fact-checked (mis-)information spreads is missing. In this work, we empirically analyze the spread of misleading vs. not misleading community fact-checked posts on social media. For this purpose, we employ a dataset of community-created fact-checks from Twitter's "Birdwatch" pilot and map them to resharing cascades on Twitter. Different from earlier studies analyzing the spread of misinformation listed on third-party fact-checking websites (e.g., snopes.com), we find that community fact-checked misinformation is less viral. Specifically, misleading posts are estimated to receive 36.62% fewer retweets than not misleading posts. A partial explanation may lie in differences in the fact-checking targets: community fact-checkers tend to fact-check posts from influential user accounts with many followers, while expert fact-checks tend to target posts that are shared by less influential users. We further find that there are significant differences in virality across different sub-types of misinformation (e.g., factual errors, missing context, manipulated media). Moreover, we conduct a user study to assess the perceived reliability of (real-world) community-created fact-checks. Here, we find that users, to a large extent, agree with community-created fact-checks. Altogether, our findings offer insights into how misleading vs. not misleading posts spread and highlight the crucial role of sample selection when studying misinformation on social media.

Insights from a Comparative Study on the Variety, Velocity, Veracity, and Viability of Crowdsourced and Professional Fact-Checking Services

The tension between the increasing need for fact-checking and the limited capacity of fact-check providers inspired several crowdsourced approaches to address this challenge. However, little is known about how effectively crowdsourced fact-checking might perform in the real world at a large scale. We fill this gap by evaluating a Taiwanese crowdsourced fact-checking community and two professional fact-checking sites from four dimensions: Variety, Velocity, Veracity, and Viability. Our analysis shows the different focus these two types of sites have in terms of topic coverage (variety) and demonstrates that while crowdsourced fact-checkers are much faster than professionals (velocity) to answer new requests, these fact-checkers often build on the existing professional knowledge for repeated requests. In addition, our findings indicate that the accuracy of the crowdsourced community (veracity) parallels that of the professional sources; and that the crowdsourced fact-checks are perceived quite close to professionals in terms of objectivity, clarity, and persuasiveness (viability).

Comparing button-based chatbots with webpages for presenting fact-checking results: A case study of health information

The unprecedented proliferation of online health misinformation poses a potential threat to public health. In recent times, several fact-checking organizations have adopted chatbots to present fact-checking results. However, it is unclear whether chatbots are more appropriate than traditional fact-checking websites for presenting these rigorous corrective messages. To answer this question, we compared a button-based chatbot with a traditional webpage for presenting fact-checking results. As fact-checkers’ expertise cues could influence users’ perception of fact-checking, we also considered the effect of expertise cues in our study. We conducted a 2 (interaction type: webpage vs. chatbot) × 2 (expertise cue: non-highlighted vs. highlighted) between-subjects online experiment (N=308). The results show that the chatbot leads to higher perceived ease of use, which in turn increases the effectiveness of fact-checking. The highlighted expertise cue tends to decrease users’ intention to use, especially when they interact with the webpage. Finally, we discuss the feasibility of using chatbots to disseminate fact-checking content and several design implications for the creation of an effective tool to fact-check health information.