Fake news and COVID-19: modelling the predictors of fake news sharing among social media users
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ABSTRACT:
SUBMITTER: Apuke O
PROVIDER: S-EPMC7390799 | biostudies-literature | 2020 Jul
REPOSITORIES: biostudies-literature
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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.
| S-EPMC8177444 | biostudies-literature
Project description:Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analysing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of 'creators', and a majority playing the role of 'consumers'. The relative proportion of these groups (approx. 14% creators-86% consumers) appears stable over time: consumers are mostly exposed to the opinions of a vocal minority of creators (which are the origin of 82% of fake content in our data), that could be mistakenly understood as representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.
| S-EPMC9597167 | biostudies-literature
Project description:Sharing of fake news on social media platforms is a global concern, with research offering little insight into the motives behind such sharing. This study adopts a mixed-method approach to explore fake-news sharing behaviour. To begin with, qualitative data from 58 open-ended essays was analysed to identify six behavioural manifestations associated with sharing fake news. Thereafter, research model hypothesizing the association between these behaviours was proposed using the honeycomb framework and the third-person effect hypothesis. Age and gender were the control variables. Two data sets obtained from cross-sectional surveys with 471 and 374 social media users were utilized to test the proposed model. The study results suggest that instantaneous sharing of news for creating awareness had positive effect on sharing fake news due to lack of time and religiosity. However, authenticating news before sharing had no effect on sharing fake news due to lack of time and religiosity. The study results also suggest that social media users who engage in active corrective action are unlikely to share fake news due to lack of time. These results have significant theoretical and practical implications.
| S-EPMC7340407 | biostudies-literature
Project description:Disinformation (fake news) is a major problem that affects modern populations, especially in an era when information can be spread from one corner of the world to another in just one click. The diffusion of misinformation becomes more problematic when it addresses issues related to health, as it can affect people at both the individual and population levels. Through the ideas proposed by cultural evolution theory, in this study, we seek to understand the dynamics of disseminating messages (cultural traits) with untrue content (maladaptive traits). For our investigation, we used the scenario caused by the Coronavirus Disease 2019 (COVID-19) pandemic as a model. The instability caused by the pandemic provides a good model for the study of adapted and maladaptive traits, as the information can directly affect individual and population fitness. Through data collected on the Twitter platform (259,176 tweets) and using machine learning techniques and web scraping, we built a predictive model to analyze the following questions: (1) Is false information more shared? (2) Is false information more adopted? (3) Do people with social prestige influence the dissemination of maladaptive traits of COVID-19? We observed that fake news features contained in messages with false information were shared and adopted as unblemished messages. We also observed that social prestige was not a determining factor for the diffusion of maladaptive traits. Even with the ability to allow connections between individuals participating in social media, some factors such as attachment to cultural traits and the formation of social bubbles can favor isolation and decrease connectivity between individuals. Consequently, in the scenario of isolation between groups and low connectivity between individuals, there is a reduction in cultural exchange between people, which interferes with the dynamics of the selection of cultural traits. Thus, maladaptive (harmful) traits are favored and maintained in the cultural system. We also argue that the local Brazilian cultural context can be a determining factor for maintaining maladaptive traits. We conclude that in an unstable (pandemic) scenario, the information transmitted on Twitter is not reliable in relation to the increase in fitness, which may occur because of the low cultural exchange promoted by the personalization of the social network and cultural context of the population.Supplementary informationThe online version contains supplementary material available at 10.1007/s42979-021-00836-w.
| S-EPMC8397611 | biostudies-literature
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:The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors' predictions to improve the fake news detection system's overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.
| S-EPMC7959660 | biostudies-literature
Project description:Why do we share fake news? Despite a growing body of freely-available knowledge and information fake news has managed to spread more widely and deeply than before. This paper seeks to understand why this is the case. More specifically, using an experimental setting we aim to quantify the effect of veracity and perception on reaction likelihood. To examine the nature of this relationship, we set up an experiment that mimics the mechanics of Twitter, allowing us to observe the user perception, their reaction in the face of shown claims and the factual veracity of those claims. We find that perceived veracity significantly predicts how likely a user is to react, with higher perceived veracity leading to higher reaction rates. Additionally, we confirm that fake news is inherently more likely to be shared than other types of news. Lastly, we identify an activist-type behavior, meaning that belief in fake news is associated with significantly disproportionate spreading (compared to belief in true news).
| S-EPMC9361856 | biostudies-literature
Project description:Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.
| S-EPMC8764372 | biostudies-literature
Project description:BackgroundSocial media is widely used as a source of news and information regarding COVID-19. However, the abundance of misinformation on social media platforms has raised concerns regarding the spreading infodemic. Accordingly, many have questioned the utility and impact of social media news use on users' engagement with (mis)information.ObjectiveThis study offers a conceptual framework for how social media news use influences COVID-19 misinformation engagement. More specifically, we examined how news consumption on social media leads to COVID-19 misinformation sharing by inducing belief in such misinformation. We further explored if the effects of social media news use on COVID-19 misinformation engagement depend on individual differences in cognition and personality traits.MethodsWe used data from an online survey panel administered by a survey agency (Qualtrics) in Singapore. The survey was conducted in March 2022, and 500 respondents answered the survey. All participants were older than 21 years and provided consent before taking part in the study. We used linear regression, mediation, and moderated mediation analyses to explore the proposed relationships between social media news use, cognitive ability, personality traits, and COVID-19 misinformation belief and sharing intentions.ResultsThe results suggested that those who frequently used social media for news consumption were more likely to believe COVID-19 misinformation and share it on social media. Further probing the mechanism suggested that social media news use translated into sharing intent via the perceived accuracy of misinformation. Simply put, social media news users shared COVID-19 misinformation because they believed it to be accurate. We also found that those with high levels of extraversion than those with low levels were more likely to perceive the misinformation to be accurate and share it. Those with high levels of neuroticism and openness than those with low levels were also likely to perceive the misinformation to be accurate. Finally, it was observed that personality traits did not significantly influence misinformation sharing at higher levels of cognitive ability, but low cognitive users largely drove misinformation sharing across personality traits.ConclusionsThe reliance on social media platforms for news consumption during the COVID-19 pandemic has amplified, with dire consequences for misinformation sharing. This study shows that increased social media news consumption is associated with believing and sharing COVID-19 misinformation, with low cognitive users being the most vulnerable. We offer recommendations to newsmakers, social media moderators, and policymakers toward efforts in limiting COVID-19 misinformation propagation and safeguarding citizens.
| S-EPMC9533200 | biostudies-literature
Project description:Accuracy prompts, nudges that make accuracy salient, typically decrease the sharing of fake news, while having little effect on real news. Here, we introduce a new accuracy prompt that is more effective than previous prompts, because it does not only reduce fake news sharing, but it also increases real news sharing. We report four preregistered studies showing that an "endorsing accuracy" prompt ("I think this news is accurate"), placed into the sharing button, decreases fake news sharing, increases real news sharing, and keeps overall engagement constant. We also explore the mechanism through which the intervention works. The key results are specific to endorsing accuracy, rather than accuracy salience, and endorsing accuracy does not simply make participants apply a "source heuristic." Finally, we use Pennycook et al.'s limited-attention model to argue that endorsing accuracy may work by making people more carefully consider their sharing decisions.
| S-EPMC10637098 | biostudies-literature