Fake news spreader detection using trust-based strategies in social networks with bot filtration.
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ABSTRACT: Supplementary information
The online version contains supplementary material available at 10.1007/s13278-022-00890-z.
SUBMITTER: Rath B
PROVIDER: S-EPMC9244065 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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Project description:IntroductionMisinformation surrounding COVID-19 poses a global public health problem that adversely affects governments' abilities to mitigate the disease and causes accidental deaths and self-harm due to false beliefs about the virus, prevention measures, vaccines and cures. We aim to examine the relationship between exposure to and trust in COVID-19 news (from Television, social media, interpersonal communication) and information sources (healthcare experts, government, clerics) and belief in COVID-19 myths and false information, as well as critical verification practices before posting on social media.MethodsWe use a cross-sectional researcher-administered phone survey of adults living in Lebanon between March 27 and April 23, 2020.ResultsThe sample included 56.1% men and 43.9% women, 37.9% with a university degree, 63.0% older than 30, and 7% with media literacy training. Those who trust COVID-19 news from social media [95%CI:(1.05-1.52)] and interpersonal communication [95%CI:(1.25-1.82)], and those who trust information from clerics [95%CI:(1.25-1.82)] were more likely to believe in COVID-19 myths and false information. University graduates [95%CI:(0.25-0.51)] and those who trust information from government [95%CI:(0.65-0.89] were less likely to believe in myths and false information. Those who believe in COVID-19 myths and false information [95%CI:(0.25-0.70)] were less likely to engage in critical social media posting practices. Only those who underwent media literacy training [95%CI:(1.24-6.55)] were more likely to engage in critical social media posting practices.ConclusionHigher education and trust in information from government contributed to decreasing belief in COVID-19 myths and false information. Trust in news from social media, interpersonal communication and clerics contributed to increasing belief in COVID-19 myths and false information, which in turn contributed to less critical social media posting practices, thereby exacerbated the infodemic. Media literacy training contributed to increasing critical social media posting practices, thereby played a role in mitigating the infodemic.
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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.
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Project description:Fake news detection has gained increasing importance among the research community due to the widespread diffusion of fake news through media platforms. Many dataset have been released in the last few years, aiming to assess the performance of fake news detection methods. In this survey, we systematically review twenty-seven popular datasets for fake news detection by providing insights into the characteristics of each dataset and comparative analysis among them. A fake news detection datasets characterization composed of eleven characteristics extracted from the surveyed datasets is provided, along with a set of requirements for comparing and building new datasets. Due to the ongoing interest in this research topic, the results of the analysis are valuable to many researchers to guide the selection or definition of suitable datasets for evaluating their fake news detection methods.
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Project description:Individual differences in cognitive abilities and personality help to understand individual differences in various human behaviors. Previous work investigated individual characteristics in light of believing (i.e., misclassifying) fake news. However, only little is known about the misclassification of true news as fake, although it appears equally important to correctly identify fake and true news for unbiased belief formation. An online study with N = 530 (n = 396 men) participants was conducted to investigate performance in a Fake and True News Test in association with i) performance in fluid and crystallized intelligence tests and the Big Five Inventory, and ii) news consumption as a mediating variable between individual characteristics and performance in the Fake and True News Test. Results showed that fluid intelligence was negatively correlated with believing fake news (the association did not remain significant in a regression model); crystallized intelligence was negatively linked to misclassifying true news. Extraversion was negatively and crystallized intelligence was positively associated with fake and true news discernment. The number of different news sources consumed correlated negatively with misclassifying true news and positively with fake and true news discernment. However, no meaningful mediation effect of news consumption was observed. Only interpersonal trust was negatively related to misclassifying both fake and true news as well as positively related to news discernment. The present findings reveal that underlying factors of believing fake news and misclassifying true news are mostly different. Strategies that might help to improve the abilities to identify both fake and true news based on the present findings are discussed.
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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.
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Project description:One of the challenges today is to face fake news (false information) in health due to its potential impact on people's lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.
| S-EPMC7177765 | 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.
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