True News Research Articles

Punishment Trumps Warning in Prebunking Misinformation: Evidence From Survey Experiments in Taiwan

There have been many attempts to use prebunking strategies to address the problem of misinformation. While extant research supports their efficacy, it also finds that they could make people suspicious even of true information. This study employs survey experiments in Taiwan to assess the effect of warning messages, with or without mentioning punishments one might incur if spreading misinformation, on people’s beliefs in false and true news, and their subsequent intention to share the information with others. The findings suggest that warning messages mentioning monetary punishments are the most effective in affecting people’s beliefs and intentions to behave.

VERA-ARAB: unveiling the Arabic tweets credibility by constructing balanced news dataset for veracity analysis

The proliferation of fake news on social media platforms necessitates the development of reliable datasets for effective fake news detection and veracity analysis. In this article, we introduce a veracity dataset of Arabic tweets called “VERA-ARAB”, a pioneering large-scale dataset designed to enhance fake news detection in Arabic tweets. VERA-ARAB is a balanced, multi-domain, and multi-dialectal dataset, containing both fake and true news, meticulously verified by fact-checking experts from Misbar. Comprising approximately 20,000 tweets from 13,000 distinct users and covering 884 different claims, the dataset includes detailed information such as news text, user details, and spatiotemporal data, spanning diverse domains like sports and politics. We leveraged the X API to retrieve and structure the dataset, providing a comprehensive data dictionary to describe the raw data and conducting a thorough statistical descriptive analysis. This analysis reveals insightful patterns and distributions, visualized according to data type and nature. We also evaluated the dataset using multiple machine learning classification models, exploring various social and textual features. Our findings indicate promising results, particularly with textual features, underscoring the dataset’s potential for enhancing fake news detection. Furthermore, we outline future work aimed at expanding VERA-ARAB to establish it as a benchmark for Arabic tweets in fake news detection. We also discuss other potential applications that could leverage the VERA-ARAB dataset, emphasizing its value and versatility for advancing the field of fake news detection in Arabic social media. Potential applications include user veracity assessment, topic modeling, and named entity recognition, demonstrating the dataset's wide-ranging utility for broader research in information quality management on social media.

When politics trumps truth: Political concordance versus veracity as a determinant of believing, sharing, and recalling the news.

Resistance to truth and susceptibility to falsehood threaten democracies around the globe. The present research assesses the magnitude, manifestations, and predictors of these phenomena, while addressing methodological concerns in past research. We conducted a preregistered study with a split-sample design (discovery sample N = 630, validation sample N = 1,100) of U.S. Census-matched online adults. Proponents and opponents of 2020 U.S. presidential candidate Donald Trump were presented with fake and real political headlines ahead of the election. The political concordance of the headlines determined participants' belief in and intention to share news more than the truth of the headlines. This "concordance-over-truth" bias persisted across education levels, analytic reasoning ability, and partisan groups, with some evidence of a stronger effect among Trump supporters. Resistance to true news was stronger than susceptibility to fake news. The most robust predictors of the bias were participants' belief in the relative objectivity of their political side, extreme views about Trump, and the extent of their one-sided media consumption. Interestingly, participants stronger in analytic reasoning, measured with the Cognitive Reflection Task, were more accurate in discerning real from fake headlines when accurate conclusions aligned with their ideology. Finally, participants remembered fake headlines more than real ones regardless of the political concordance of the news story. Discussion explores why the concordance-over-truth bias observed in our study is more pronounced than previous research suggests, and examines its causes, consequences, and potential remedies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Media literacy tips promoting reliable news improve discernment and enhance trust in traditional media

Media literacy tips typically encourage people to be skeptical of the news despite the small prevalence of false news in Western democracies. Would such tips be effective if they promoted trust in true news instead? A pre-registered experiment (N = 3919, US) showed that Skepticism-enhancing tips, Trust-inducing tips, and a mix of both tips, increased participants’ sharing and accuracy discernment. The Trust-inducing tips boosted true news sharing and acceptance, the Skepticism-enhancing tips hindered false news sharing and acceptance, while the Mixed tips did both. Yet, the effects of the tips were more alike than different, with very similar effect sizes across conditions for true and false news. We experimentally manipulated the proportion of true and false news participants were exposed to. The Trust and Skepticism tips were most effective when participants were exposed to equal proportions of true and false news, while the Mixed tips were most effective when exposed to 75% of true news - the most realistic proportion. Moreover, the Trust-inducing tips increased trust in traditional media. Overall, we show that to be most effective, media literacy tips should aim both to foster skepticism towards false news and to promote trust in true news.

Partisans neither expect nor receive reputational rewards for sharing falsehoods over truth online.

A frequently invoked explanation for the sharing of false over true political information is that partisans are motivated by their reputations. In particular, it is often argued that by indiscriminately sharing news that is favorable to one's political party, regardless of whether it is true-or perhaps especially when it is not true-partisans can signal loyalty to their group, and improve their reputations in the eyes of their online networks. Across three survey studies (total N = 3,038), and an analysis of over 26,000 tweets, we explored these hypotheses by measuring the reputational benefits that people anticipate and receive from sharing different content online. In the survey studies, we showed participants actual news headlines that varied in (ⅰ) veracity, and (ⅱ) favorability to their preferred political party. Across all three studies, participants anticipated that sharing true news would bring more reputational benefits than sharing false news. Critically, while participants also expected greater reputational benefits for sharing news favorable to their party, the perceived reputation value of veracity was no smaller for more favorable headlines. We found a similar pattern when analyzing engagement on Twitter: among headlines that were politically favorable to a user's preferred party, true headlines elicited more approval than false headlines.

A Hybrid Deep Learning Based Fake News Detection System Using Temporal Features

Detecting fake news and missing information is gaining popularity, especially after social media and online news platforms advancements. Social media is the main and speediest source of fake news propagation, whereas online news websites contribute to fake news dissipation. In this study, we propose a framework to detect fake news using the temporal features of text and consider user feedback to determine whether the news is fake or not. In recent studies, the temporal features in text documents gain valuable consideration from Natural Language Processing and user feedback and only try to classify the textual data as fake or true. This research article indicates the impact of recurring and non-recurring events on fake and true news. We use different models such as LSTM, BERT, and CNN- BiLSTM to investigate, and it is concluded that from BERT, we get better results, and 70% of true news is recurring, and the rest of 30% is non-recurring.

Values in British higher education: knowledge, freedom and wellbeing

British higher education is ranked among the best in the world, but some of its core values are under strain. Knowledge and critical thinking can be undermined by biased mind-sets, especially when engaging with the social media. Research demonstrates that false news goes viral much more quickly than true news. Political correctness and the woke movement can militate against freedom, especially in gender-related matters. Surveys show that many staff and students have a lower sense of wellbeing than the rest of the population. Conscious of these problems, the Government has responded with an Act to place extensive new obligations on certain types of online service providers, requiring them to protect their users by managing risks relating to illegal and harmful content. A separate Act has also been passed to protect freedom of speech in universities. The issue of staff and student unhappiness is much less amenable to legislation because it is so multi-faceted. However, it is a very important issue for a healthy, democratic society that fosters cooperation, trust and community. It needs to be addressed.

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Abstract: In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

Is Journalistic Truth Dead? Measuring How Informed Voters Are about Political News

To investigate general patterns in news information in the United States, we combine a protocol for identifying major political news stories, 11 monthly surveys with 15,000 participants, and a model of news discernment. When confronted with a true and a fake news story, 47 percent of subjects confidently choose the true story, 3 percent confidently choose the fake story, and the remaining half are uncertain. Socioeconomic differences are associated with large variations in the probability of selecting the true news story. Partisan congruence between an individual and a news story matters, but its impact is up to an order of magnitude smaller. (JEL D72, D83, L82)

Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning

This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where, at each stage, one user is selected to propagate true news. A challenging issue is episodic reward where the "net" effect of selecting individual debunkers cannot be discerned from the interleaving information propagation on social networks, and only the collective effect from mitigation efforts can be observed. Existing Self-Imitation Learning (SIL) methods have shown promise in learning from episodic rewards, but are ill-suited to the real-world application of fake news mitigation because of their poor sample efficiency. To learn a more effective debunker selection policy for fake news mitigation, this study proposes NAGASIL - Negative sampling and state Augmented Generative Adversarial Self-Imitation Learning, which consists of two improvements geared towards fake news mitigation: learning from negative samples, and an augmented state representation to capture the "real" environment state by integrating the current observed state with the previous state-action pairs from the same campaign. Experiments on two social networks show that NAGASIL yields superior performance to standard GASIL and state-of-the-art fake news mitigation models.

Empowering social media users: nudge toward self-engaged verification for improved truth and sharing discernment

Abstract How can we empower social media users to better discern the veracity of news and share less false news? This survey experiment (N = 636) assessed the effectiveness of two interventions—signing a Pro-Truth Pledge and utilizing a Fact-Checking Guide. Results showed that utilizing the Fact-Checking Guide increased skepticism of news posts, likelihood to verify news posts, verification engagement, and reduced intention to share news without regard to news veracity. Before and after comparisons indicated that after verification engagement activities, truth and sharing discernment improved with higher factual accuracy ratings for true news, lower accuracy ratings for false news, and a greater likelihood to share true news compared to false news. Individual’s engagement in verification was identified as a crucial mechanism through which the Fact-Checking Guide intervention led to better truth and sharing discernment. The study could inform social media designs that promote a truthful news environment.

Social corrections act as a double-edged sword by reducing the perceived accuracy of false and real news in the UK, Germany, and Italy

Corrective or refutational posts from ordinary users on social media have the potential to improve the online information ecosystem. While initial evidence of these social corrections is promising, a better understanding of the effects across different topics, formats, and audiences is needed. In three pre-registered experiments (N = 1944 UK, N = 2467 Italy, N = 2210 Germany) where respondents completed a social media post assessment task with false and true news posts on various topics (e.g., health, climate change, technology), we find that social corrections reduce perceived accuracy of and engagement with false news posts. We also find that social corrections that flag true news as false decrease perceived accuracy of and engagement with true news posts. We did not find evidence to support moderation of these effects by correction strength, anti-expert sentiments, cognitive reflection capacities, or susceptibility to social influence. While social corrections can be effective for false news, they may also undermine belief in true news.

The Impact of Credibility of the News Source DETIK.COM on the Confidence of Tama Jagakarsa Students’ University in the Accuracy of News Reports

Selecting a trustworthy and credibility news source is essential to avoiding falling for hoaxes or fake news, as there are still plenty of online news managers who post news that is interesting but ultimately less significant just for readers' attention to the website. People become extremely picky when they are looking for information, and one of the most crucial things to consider when choosing an information source is its reliability. One example of an online media that helps prioritize journalistic ethical norms and rules is Detik.com. It is the first independent online news site that can survive without the backing of print media. factual and true news presented in a style that is accessible to readers of all backgrounds and employs structured writing. Detik.com constantly aims to give the best for all readers in Indonesia to win the trust of devoted readers. Because there are variables where relationships will be studied, and the goal is to provide a structured, factual description of the facts of the relationship between the variables studied. This research employs a quantitative method with a descriptive approach, through a survey using a questionnaire as a data collection instrument. The independent variable (X) is "Credibility of the Information Source," whereas the dependent variable (Y) is "Trust in the Accuracy of the News." This study takes a quantitative method since it tests variables with variable X (Credibility of News Sources) and variable Y (Trust in News Accuracy). The population in this study was FIKOM students from TAMA Jagakarsa University, with a total of 230 students from all concentrations, and the sample size was 146 responders. The goal of this study is to determine "how significant the credibility of Detik.Com news sources is on TAMA Communication Students' trust in news accuracy." The result of this study has a significant impact on students' faith in Detik.Com news accuracy.

Fake News Fatores de suscetibilidade e de disseminação

By mimicking journalistic formats and blending real facts with fabricated ones, with the deliberate intention of deceiving, fake news affects trust in the media and harms democratic debate. Thus, identifying the characteristics of fake news, how they differ from true news, and their dissemination enables the informing of policies on digital and media literacy. This article represents a review of the literature in this domain. Initially, it presents a clarification of the main concepts, then discusses the determinants of susceptibility and dissemination of fake news most addressed by the scientific community, namely individual, social, and contextual factors.

Susceptibility to cancer misinformation: Predictors of false belief and false memory formation

AbstractPrevious research has shown that people sometimes come to believe in misinformation presented in the form of fake news, and even form false memories for the fabricated events described. This study aimed to investigate the effects of analytical reasoning, attitudes to complementary and alternative medicine, bullshit receptivity, and previous experience with cancer on the formation of false memory and false belief for cancer related misinformation. Participants (N = 466) were exposed to four fake news stories and four true news stories relating to cancer treatment and services. Male gender, low analytical reasoning, receptivity to bullshit, and endorsement of complementary and alternative medicine were all significant predictors of belief in cancer misinformation, while participants with poor analytical reasoning or higher receptivity to bullshit reported more false memories for fake news stories. These results indicate that reflexive, intuitive thinking styles contribute to susceptibility to cancer misinformation, suggesting a potential target for public health interventions.

Fake memories: A meta-analysis on the effect of fake news on the creation of false memories and false beliefs

Abstract Fake news can affect people in negative ways. A recent line of research has demonstrated that when people are exposed to fake news they can form false memories for the events depicted in the news stories. We conducted a meta-analysis to obtain an estimate of the average rate of false memories elicited by fake news. Thirteen articles were included in the final analysis, revealing that nearly 40% and 60% of the participants reported at least one false memory and belief (respectively) after fake news exposure, while each participant remembered or believed 22% of the total number of fake news presented. Individual differences may affect the rate of false memory formation following exposure to false memories. We therefore examined moderating effects of individual difference variables assessed in the included studies. Participants with better analytical reasoning skills and a high level of interest in the news topic were least likely to report false memories for fake news, with level of interest being also a facilitating factor in remembering true news. No effect was detected for cognitive ability and objective knowledge. Our results provide insightful and practical information in the context of world-wide misinformation dissemination and its impact on people's beliefs and memories.

Online searches to evaluate misinformation can increase its perceived veracity.

Considerable scholarly attention has been paid to understanding belief in online misinformation1,2, with a particular focus on social networks. However, the dominant role of search engines in the information environment remains underexplored, even though the use of online search to evaluate the veracity of information is a central component of media literacy interventions3-5. Although conventional wisdom suggests that searching online when evaluating misinformation would reduce belief in it, there is little empirical evidence to evaluate this claim. Here, across five experiments, we present consistent evidence that online search to evaluate the truthfulness of false news articles actually increases the probability of believing them. To shed light on this relationship, we combine survey data with digital trace data collected using a custom browser extension. We find that the search effect is concentrated among individuals for whom search engines return lower-quality information. Our results indicate that those who search online to evaluate misinformation risk falling into data voids, or informational spaces in which there is corroborating evidence from low-quality sources. We also find consistent evidence that searching online to evaluate news increases belief in true news from low-quality sources, but inconsistent evidence that it increases belief in true news from mainstream sources. Our findings highlight the need for media literacy programmes to ground their recommendations in empirically tested strategies and for search engines to invest in solutions to the challenges identified here.

Depth, breadth and structural virality: the influence of emotion, topic, authority and richness on misinformation spread

PurposeThis study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.Design/methodology/approachThe authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.FindingsResults show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.Originality/valueThe influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

People are worse at detecting fake news in their foreign language.

Across two preregistered within-subject experiments (N = 570), we found that when using their foreign language, proficient bilinguals discerned true from false news less accurately. This was the case for international news (Experiment 1) and more local news (Experiment 2). When using a foreign (as opposed to native) language, false news headlines were always judged more believable, while true news headlines were judged equally (Experiment 2) or less believable (Experiment 1). In contrast to past theorizing, the foreign language effect interacted neither with perceived arousal of news (Experiment 1) nor with individual differences in cognitive reflection (Experiments 1 and 2). Finally, using signal detection theory modeling, we showed that the negative effects of using a foreign language were not caused by adopting different responding strategies (e.g., preferring omissions to false alarms) but rather by decreased sensitivity to the truth. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Testing the Effect of Information on Discerning the Veracity of News in Real Time

Abstract Despite broad adoption of digital media literacy interventions that provide online users with more information when consuming news, relatively little is known about the effect of this additional information on the discernment of news veracity in real time. Gaining a comprehensive understanding of how information impacts discernment of news veracity has been hindered by challenges of external and ecological validity. Using a series of pre-registered experiments, we measure this effect in real time. Access to the full article relative to solely the headline/lede and access to source information improves an individual's ability to correctly discern the veracity of news. We also find that encouraging individuals to search online increases belief in both false/misleading and true news. Taken together, we provide a generalizable method for measuring the effect of information on news discernment, as well as crucial evidence for practitioners developing strategies for improving the public's digital media literacy.