Fake news detection: a survey of evaluation datasets.
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ABSTRACT:
SUBMITTER: D'Ulizia A
PROVIDER: S-EPMC8237334 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.
| S-EPMC10389754 | biostudies-literature
Project description:Fake news is a real problem in today’s world, and it has become more extensive and harder to identify. A major challenge in fake news detection is to detect it in the early phase. Another challenge in fake news detection is the unavailability or the shortage of labelled data for training the detection models. We propose a novel fake news detection framework that can address these challenges. Our proposed framework exploits the information from the news articles and the social contexts to detect fake news. The proposed model is based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behaviour based on past observations. We also incorporate many features from the news content and social contexts into our model to help us classify the news better. In addition, we propose an effective labelling technique to address the label shortage problem. Experimental results on real-world data show that our model can detect fake news with higher accuracy within a few minutes after it propagates (early detection) than the baselines.
| S-EPMC8800852 | biostudies-literature
Project description:In this article, we pursue the automatic detection of fake news reporting on the Syrian war using machine learning and meta-learning. The proposed approach is based on a suite of features that include a given article's linguistic style; its level of subjectivity, sensationalism, and sectarianism; the strength of its attribution; and its consistency with other news articles from the same "media camp". To train our models, we use FA-KES, a fake news dataset about the Syrian war. A suite of basic machine learning models is explored, as well as the model-agnostic meta-learning algorithm (MAML) suitable for few-shot learning, using datasets of a modest size. Feature-importance analysis confirms that the collected features specific to the Syrian war are indeed very important predictors for the output label. The meta-learning model achieves the best performance, improving upon the baseline approaches that are trained exclusively on text features in FA-KES.
| S-EPMC8600244 | biostudies-literature
Project description:To investigate how people assess whether politically consistent news is real or fake, two studies (N = 1,008; N = 1,397) with adult American participants conducted in 2020 and 2022 utilized a within-subjects experimental design to investigate perceptions of news accuracy. When a mock Facebook post with either fake (Study 1) or real (Study 2) news content was attributed to an alternative (vs. a mainstream) news outlet, it was, on average, perceived to be less accurate. Those with beliefs reflecting News Media Literacy demonstrated greater sensitivity to the outlet's status. This relationship was itself contingent on the strength of the participant's partisan identity. Strong partisans high in News Media Literacy defended the accuracy of politically consistent content, even while recognizing that an outlet was unfamiliar. These results highlight the fundamental importance of looking at the interaction between user-traits and features of social media news posts when examining learning from political news on social media.
| S-EPMC10562704 | biostudies-literature
Project description:Highlights • Social media, fake news, and COVID-19.• Misinformation on social media has fuelled panic regarding the COVID-19.• Altruism is the strongest predictor of fake news sharing on COVID-19.• Socialization, information seeking and pass time predict fake news sharing.• Entertainment is not associated with sharing fake news on COVID-19. Fake news dissemination on COVID-19 has increased in recent months, and the factors that lead to the sharing of this misinformation is less well studied. Therefore, this paper describes the result of a Nigerian sample (n = 385) regarding the proliferation of fake news on COVID-19. The fake news phenomenon was studied using the Uses and Gratification framework, which was extended by an “altruism” motivation. The data were analysed with Partial Least Squares (PLS) to determine the effects of six variables on the outcome of fake news sharing. Our results showed that altruism was the most significant factor that predicted fake news sharing of COVID-19. We also found that social media users’ motivations for information sharing, socialisation, information seeking and pass time predicted the sharing of false information about COVID-19. In contrast, no significant association was found for entertainment motivation. We concluded with some theoretical and practical implications.
| S-EPMC7390799 | biostudies-literature
Project description:BackgroundProliferation of misinformation in digital news environments can harm society in a number of ways, but its dangers are most acute when citizens believe that false news is factually accurate. A recent wave of empirical research focuses on factors that explain why people fall for the so-called fake news. In this scoping review, we summarize the results of experimental studies that test different predictors of individuals' belief in misinformation.MethodsThe review is based on a synthetic analysis of 26 scholarly articles. The authors developed and applied a search protocol to two academic databases, Scopus and Web of Science. The sample included experimental studies that test factors influencing users' ability to recognize fake news, their likelihood to trust it or intention to engage with such content. Relying on scoping review methodology, the authors then collated and summarized the available evidence.ResultsThe study identifies three broad groups of factors contributing to individuals' belief in fake news. Firstly, message characteristics-such as belief consistency and presentation cues-can drive people's belief in misinformation. Secondly, susceptibility to fake news can be determined by individual factors including people's cognitive styles, predispositions, and differences in news and information literacy. Finally, accuracy-promoting interventions such as warnings or nudges priming individuals to think about information veracity can impact judgements about fake news credibility. Evidence suggests that inoculation-type interventions can be both scalable and effective. We note that study results could be partly driven by design choices such as selection of stimuli and outcome measurement.ConclusionsWe call for expanding the scope and diversifying designs of empirical investigations of people's susceptibility to false information online. We recommend examining digital platforms beyond Facebook, using more diverse formats of stimulus material and adding a comparative angle to fake news research.
| S-EPMC8224890 | biostudies-literature
Project description:Analytical thinking safeguards us against believing or spreading fake news. In various forms, this common assumption has been reported, investigated, or implemented in fake news education programs. Some have associated this assumption with the inverse claim, that distractions from analytical thinking may render us vulnerable to believing or spreading fake news. This paper surveys the research done between 2016 and 2022 on psychological factors influencing one's susceptibility to believing or spreading fake news, considers which of the psychological factors are plausible distractors to one's exercise of analytical thinking, and discusses some implications of considering them as distractors to analytical thinking. From these, the paper draws five conclusions: (1) It is not analytical thinking per se, but analytical thinking directed to evaluating the truth that safeguards us from believing or spreading fake news. (2) While psychological factors can distract us from exercising analytical thinking and they can also distract us in exercising analytical thinking. (3) Whether a psychological factor functions as a distractor from analytical thinking or in analytical thinking may depend on contextual factors. (4) Measurements of analytical thinking may not indicate vulnerability to believing or spreading fake news. (5) The relevance of motivated reasoning to our tendency to believe fake news should not yet be dismissed. These findings may be useful to guide future research in the intersection of analytical thinking and susceptibility to believing or spreading fake news.
| S-EPMC10259813 | biostudies-literature
Project description:Countering misinformation can reduce belief in the moment, but corrective messages quickly fade from memory. We tested whether the longer-term impact of fact-checks depends on when people receive them. In two experiments (total N = 2,683), participants read true and false headlines taken from social media. In the treatment conditions, "true" and "false" tags appeared before, during, or after participants read each headline. Participants in a control condition received no information about veracity. One week later, participants in all conditions rated the same headlines' accuracy. Providing fact-checks after headlines (debunking) improved subsequent truth discernment more than providing the same information during (labeling) or before (prebunking) exposure. This finding informs the cognitive science of belief revision and has practical implications for social media platform designers.
| S-EPMC7865139 | biostudies-literature
Project description: Not available
| S-EPMC11023451 | biostudies-literature
Project description:The philosophical concept of informal fallacies-arguments that fail to provide sufficient support for a claim-is introduced and connected to the topic of fake news detection. We assumed that the ability to identify informal fallacies can be trained and that this ability enables individuals to better distinguish between fake news and real news. We tested these assumptions in a two-group between-participants experiment (N = 116). The two groups participated in a 30-minute-long text-based learning intervention: either about informal fallacies or about fake news. Learning about informal fallacies enhanced participants' ability to identify fallacious arguments one week later. Furthermore, the ability to identify fallacious arguments was associated with a better discernment between real news and fake news. Participants in the informal fallacy intervention group and the fake news intervention group performed equally well on the news discernment task. The contribution of (identifying) informal fallacies for research and practice is discussed.
| S-EPMC10057814 | biostudies-literature