False News Stories Research Articles

Us versus them: The role of national identity in the formation of false memories for fake news.

People are prone to forming false memories for fictitious events described in fake news stories. In this preregistered study, we hypothesized that the formation of false memories may be promoted when the fake news includes stereotypes that reflect positively on one's own nationality or negatively on another nationality. We exposed German and Irish participants (N = 1,184) to fabricated news stories that were consistent with positive or negative stereotypes about Germany and Ireland. The predicted three-way interaction was not observed. Exploratory follow-up analyses revealed the expected pattern of results for German participants but not for Irish participants, who were more likely to remember positive stories and stories about Ireland. Individual differences in patriotism did not significantly affect false memory rates; however, higher levels of cognitive ability and analytical reasoning decreased false memories and increased participants' ability to distinguish between true and false news stories. These results demonstrate that stereotypical information pertaining to national identity can influence the formation of false memories for fake news, but variations in cultural context may affect how misinformation is received and processed. We conclude by urging researchers to consider the sociopolitical and media landscape when predicting the consequences of fake news exposure. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

FAKE NEWS DETECTOR USING DEEP LEARNING

In recent years, there has been an increase in worry about the presence of false news on the internet. Real and false news have becoming harder to identify as social media and other online platforms have rapidly replaced print media as many peoples major news sources. Techniques for natural language processing (NLP) have been developed to help identify and categorize bogus news in attempt to solve this problem. Examining the language used in news items is one method of spotting false information. Large datasets of news stories may be used to train NLP models to find linguistic patterns that are suggestive of false news. For instance, false news stories may employ dramatic or exaggerated language, or they may not cite reliable sources to back up their assertions. NLP models may be trained to recognize these patterns and spot news stories that are probably fraudulent by utilizing machine learning methods. One other method for spotting false news is to examine the social environment in which news pieces are circulated. The sharing and consumption of news stories by users is extensively documented by social media companies. NLP algorithms can spot stories that are probably false by looking for trends in social media activity. A sign that a news story is phony can be, for instance, if it is being shared quickly by a lot of bot accounts. Having access to good training data is crucial for developing an NLP-based false news detector. Real and false news stories should also be included in this data, along with information on how the pieces were shared and read on social media. Researchers may create algorithms that can precisely distinguish between real and false news stories by training NLP models on this data. Overall, the development of NLP algorithms for false news identification is a crucial milestone in the fight against the spread of untruth on the internet. We can create tools that assist individuals identify between true and false news and, in turn, foster a more informed and involved society by utilizing the power of machine learning and natural language processing.

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%.

Fake-News Sharers: Highly impulsive people who lean conservative are most likely to pass along false news stories.

Fake-News Sharers: Highly impulsive people who lean conservative are most likely to pass along false news stories.

Identifying and Debunking Environmentally-Related False News Stories – An Experimental Investigation

The vast majority of studies in the realm of false information are currently conducted in the realm of political topics and Covid-19. This study addresses environmentally-related news stories. With the help of two experiments, I explore determinants that can explain who is good at distinguishing between accurate (i.e., factually correct) and false information and compare several intervention scenarios to debunk false information. In experiment one, subjects had to rate environmentally-related news stories as accurate or false. Afterward, subjects received systematically varied information about the correctness of the news stories depending on the experimental condition they had been randomly assigned to. After a period of three weeks, the subjects were asked to evaluate the news stories again (experiment two). In experiment one, I find that the perceived familiarity with news stories increased the propensity to accept them as true. Moreover, actively open-minded thinking helped to distinguish between accurate and false information. But the willingness to think deliberately did not seem to be important. In experiment two, it can be found that by repeating false news stories, subjects were more likely to adequately identify them later (i.e., no evidence for a familiarity backfire effect). However, it decreased the likelihood to adequately identify accurate news stories. A somewhat reverse, but weaker effect occurred when factually correct news stories were repeated: the correct identification of accurate news stories was more successful, but the opposite holds for the identification of false news stories.

Infodemics: Do healthcare professionals detect corona-related false news stories better than students?

False news stories cause welfare losses and fatal health consequences. To limit its dissemination, it is essential to know what determines the ability to distinguish between true and false news stories. In our experimental study, we present subjects corona-related stories taken from the media from various categories (e.g. social isolation, economic consequences, direct health consequences, and strong exaggeration). The subject’s task is to evaluate the stories as true or false. Besides students with and without healthcare background, we recruit healthcare professionals to increase the external validity of our study. Our main findings are: (i) Healthcare professionals perform similar to students in correctly distinguishing between true and false news stories. (ii) The propensity to engage in analytical thinking and actively open-minded thinking is positively associated with the ability to distinguish between true and false. (iii) We find that the residence of the subjects (East- or West-Germany) plays only a minor role. (iv) If news stories are in line with existing narratives, subjects tend to think that the stories are true.

The impact of personality in recognizing disinformation

Purpose The purpose of this paper is to investigate and quantify the effects of personality traits, as defined by the five-factor model (FFM) on an individual’s ability to detect fake news. The findings of this study are increasingly important because of the proliferation of social media news stories and the exposure of organizational stakeholders and business decision makers to a tremendous amount of information, including information that is not correct (a.k.a. disinformation). Design/methodology/approach The data were collected utilizing the snowball sampling methodology. Students in an Management Information Systems course completed the survey. Since a diverse sample was sought, survey participants were instructed to recruit another individual from a different generation. The survey questions of the FFM identify particular personality traits in respondents. Survey respondents were given a collection of nine news stories, five of which were false and four that were true. The number of correctly identified stories was recorded, and the effect of personality traits on the ability of survey respondents to identify fake news was calculated using eta-squared and the effect size index. Findings Each of the five factors in the FFM demonstrated an effect on an individual’s ability to detect disinformation. In fact, every single variable studied had at least a small effect size index, with one exception: gender, which had basically no effect. Therefore, each variable studied (with the exception of gender) explained a portion of the variability in the number of correctly identified false news stories. Specifically, this quantitative research demonstrates that individuals with the following personality traits are better able to identify disinformation: closed to experience or cautious, introverted, disagreeable or unsympathetic, unconscientious or undirected and emotionally stable. Originality/value There is scant research on an individual’s ability to detect false news stories, although some research has been conducted on the ability to detect phishing (a type of social engineering attack to obtain funds or personal information from the person being deceived). The results of this study enable corporations to determine which of their customers, investors and other stakeholders are most likely to be deceived by disinformation. With this information, they can better prepare for and combat the impacts of misinformation on their organization, and thereby avoid the negative financial impacts that result.

An Empirical Study on False News on Internet-Based False News Stories: Experiences, Problem Awareness, and Responsibilities

The Internet significantly reduced the marginal costs of generating and disseminating information. With false news stories in mind, scholars are increasingly interested in redesigning our information ecosystem because well-informed individuals are essential for a functioning democracy. This paper addresses the problem awareness of university students about false news stories. With the help of a questionnaire, we seek for interesting correlations to generate hypotheses that can be analyzed in further studies with new data (i.e. exploratory study). They read as follows: (i) Facebook users are more likely to be suspicious of false news stories if they are interested in political topics. People are less likely to detect false news stories the stronger they trust in others and the more emphasis they put on the opinions of others, (ii) False news stories are perceived as a problem at the societal level, but not at the individual level, (iii) Men more often than women believe to be in touch with false news stories; men overestimate their ability to spot false news stories. People who fear false news stories are likely to believe that they could detect such information better than the average, and (iv) People see operators of platforms to be in charge of false news stories; people seem to trust less in government.

Cleaning Up Social Media: The Effect of Warning Labels on Likelihood of Sharing False News on Facebook

The flagging of false news has been one of the ways suggested to discourage people from sharing deceiving news stories on social media, and recent years have seen a growing number of initiatives focused on providing credibility labels for news online. Using an experimental design, this study assesses the effectiveness of such labels designed to reduce the sharing of false news. Specifically, it examines the impact of warning labels on Facebook users’ intentions to share false news stories, drawing on a sample of 501 participants from across the political spectrum. The study also explores users’ perceptions of the likelihood that other people would share false news even after seeing a warning label. We find that the flagging of false news may indeed have an effect on reducing false news sharing intentions by diminishing the credibility of misleading information. Furthermore, we find that users may be prone to believing that others are more likely to share false news than themselves, confirming the third‐person effect. This study shows that flagging of false news on social media platforms like Facebook may indeed help the current efforts to combat sharing of deceiving information on social media.

Prior exposure increases perceived accuracy of fake news.

The 2016 U.S. presidential election brought considerable attention to the phenomenon of "fake news": entirely fabricated and often partisan content that is presented as factual. Here we demonstrate one mechanism that contributes to the believability of fake news: fluency via prior exposure. Using actual fake-news headlines presented as they were seen on Facebook, we show that even a single exposure increases subsequent perceptions of accuracy, both within the same session and after a week. Moreover, this "illusory truth effect" for fake-news headlines occurs despite a low level of overall believability and even when the stories are labeled as contested by fact checkers or are inconsistent with the reader's political ideology. These results suggest that social media platforms help to incubate belief in blatantly false news stories and that tagging such stories as disputed is not an effective solution to this problem. It is interesting, however, that we also found that prior exposure does not impact entirely implausible statements (e.g., "The earth is a perfect square"). These observations indicate that although extreme implausibility is a boundary condition of the illusory truth effect, only a small degree of potential plausibility is sufficient for repetition to increase perceived accuracy. As a consequence, the scope and impact of repetition on beliefs is greater than has been previously assumed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

News Sites and Fake News in the Egyptian Political Transformation 2013–2014: Aljazeera.net case study

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Sex Trafficking, Russian Infiltration, Birth Certificates, and Pedophilia: A Survey Experiment Correcting Fake News

Following the 2016 U.S. election, researchers and policymakers have become intensely concerned about the dissemination of “fake news,” or false news stories in circulation (Lazer et al., 2017). Research indicates that fake news is shared widely and has a pro-Republican tilt (Allcott and Gentzkow, 2017). Facebook now flags dubious stories as disputed and tries to block fake news publishers (Mosseri, 2016). While the typical misstatements of politicians can be corrected (Nyhan et al., 2017), the sheer depth of fake news’s conspiracizing may preclude correction. Can fake news be corrected?

Sex Trafficking, Russian Infiltration, Birth Certificates, and Pedophilia: A Survey Experiment Correcting Fake News

The spread of false news stories, or “fake news,” is of pressing concern to social scientists and policymakers. Here, we summarize the results of one of the first attempts to correct fake news. Subjects were presented with multiple real-world examples of fake news, drawn from across the political spectrum. We find strong evidence that citizens are willing to accept corrections to fake news, regardless of their ideology and the content of the fake stories.

Prior Exposure Increases Perceived Accuracy of Fake News

The 2016 US Presidential Election brought considerable attention to the phenomenon of “fake news”: entirely fabricated and often partisan content that is presented as factual. Here we demonstrate one mechanism that contributes to the believability of fake news: fluency via prior exposure. Using actual fake news headlines presented as they were seen on Facebook, we show that even a single exposure increases subsequent perceptions of accuracy, both within the same session and after a week. Moreover, this “illusory truth effect” for fake news headlines occurs despite a low level of overall believability, and even when the stories are labeled as contested by fact-checkers or are inconsistent with the reader’s political ideology. These results suggest that social media platforms help to incubate belief in blatantly false news stories, and that tagging such stories as disputed is not an effective solution to this problem. Interestingly, however, we also find that prior exposure does not impact entirely implausible statements (e.g., “The Earth is a perfect square”). These observations indicate that although extreme implausibility is a boundary condition of the illusory truth effect, only a small degree of potential plausibility is sufficient for repetition to increase perceived accuracy. As a consequence, the scope and impact of repetition on beliefs is greater than previously assumed.