scholarly journals A New Application of Social Impact in Social Media for Overcoming Fake News in Health

2020 ◽  
Vol 17 (7) ◽  
pp. 2430 ◽  
Author(s):  
Cristina Pulido ◽  
Laura Ruiz-Eugenio ◽  
Gisela Redondo-Sama ◽  
Beatriz Villarejo-Carballido
Keyword(s):  
Social Media ◽  
Health Information ◽  
Social Impact ◽  
Fake News ◽  
False Information ◽  
The Social ◽  
Health Related ◽  
Potential Impact

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.

scholarly journals Spreading Fake News in the Virtual Realm in Bangladesh: Assessment of Impact

2020 ◽  
pp. 11-25
Author(s):  
Sharifa Umma Shirina ◽  
Md. Tabiur Rahman Prodhan
Keyword(s):  
Social Media ◽  
Social Impact ◽  
National Election ◽  
Technological Advancement ◽  
Fake News ◽  
Supersonic Speed ◽  
News Reporting ◽  
The Social ◽  
The World ◽  
Acute Crisis

Fake news is ‘false, often sensational, information disseminated under the guise of news reporting.’ The upsurge of technological advancement, especially social media, has paved the way for spreading fake news. The virtual realm spurs fake news as per the speed of air. Nowadays, fake news has been one of the social problems in the world along with Bangladesh. Self-seeker groups use fake news as an ‘atomic arsenal’ to disseminate their popular rhetoric with supersonic speed for fulfilling male purposes. Fake news is usually rampant during any crisis, elections, and even in campaigns. The hoaxers and fakers exploit the opportunity of the wavering psychology of the social media users, and fake news becomes ‘viral’ on social media, Facebook. Recently Bangladesh has faced an acute crisis of spreading fake news during the ‘Movement of Nirapod Sarak Chai, ‘National election in December 2018’ and very recent ‘need child’s head for Padma Bridge.’ This study titled “Spreading Fake News in the Virtual Realm in Bangladesh: Assessment of Impact” seeks the reasons for spreading fake news and its’ social impact in Bangladesh.

Fake News Finds an Audience

2019 ◽  
pp. 201-222
Author(s):  
Erik P. Bucy ◽  
John E. Newhagen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Information Processing ◽  
Educational Strategy ◽  
Fake News ◽  
False Information ◽  
User Experiences ◽  
The Social ◽  
Social Media Platforms ◽  
Media Systems

The vulnerabilities shown by media systems and individual users exposed to attacks on truth from fake news and computational propaganda in recent years should be considered in light of the characteristics and concerns surrounding big data, especially the volume and velocity of messages delivered over social media platforms that tax the average user’s capacity to determine their truth value in real time. For reasons explained by the psychology of information processing, a high percentage of fake news that reaches audiences is accepted as true, particularly when distractions and interruptions typify user experiences with technology. As explained in this essay, fake news thrives in environments lacking editorial policing and epistemological vigilance, making the social media milieu ideally suited for spreading false information. In response, we suggest the value of an educational strategy to combat the dilemma that digital disinformation poses to informed citizenship.

scholarly journals MISINFORMATION AND DISINFORMATION DURING COVID-19: THE EFFECTS AND THE RELEVANT LAWS IN MALAYSIA

2020 ◽  
Vol 5 (21) ◽  
pp. 202-209
Author(s):  
Hanis Wahed
Keyword(s):  
Social Media ◽  
Social Situation ◽  
Fake News ◽  
False Information ◽  
Legal Research ◽  
Conspiracy Theories ◽  
The Public ◽  
The Social ◽  
The Future ◽  
Use Of Social Media

Misinformation and disinformation are increasing as fast as the spreading of Coronavirus disease 2019 or Covid-19. Both happen as a result of the use of social media and technologies. The act of spreading fake news, rumors, and conspiracy theories or giving false information is considered an offence under the laws of Malaysia. However, the number of cases that relate to this offence has been increasing especially during the current pandemic. Thus, this article discusses the effects of the offence and the efforts taken in preventing it from happening. The focus is on the laws that are applicable in the situation. The methodology used is socio-legal research that involves analysing the laws that are applicable in the social situation. The article suggests that further research should be carried out on the applicable laws and amendments should be made to the relevant laws in order to combat the commission of the offence in the future. It is hoped that the suggestion will assist the authority to add more measures in combatting the pandemic and for the public to be more cautious of committing misinformation and disinformation.

Health-related fake news on social media platforms: A systematic literature review

2021 ◽  
pp. 146144482110387
Author(s):  
Cristiane Melchior ◽  
Mírian Oliveira
Keyword(s):  
Social Media ◽  
Health Information ◽  
Future Research ◽  
Fake News ◽  
Health Agencies ◽  
Health Related ◽  
Social Media Platforms ◽  
Future Work ◽  
News Sharing

This review aims to (a) investigate the characteristics of both the research community and the published research on health-related fake news on social media platforms, and (b) identify the challenges and provide recommendations for future research on the subject. We reviewed 69 journal articles found in the main academic databases up to April 2021. The studies extracted data mainly from Twitter, YouTube, and Facebook. Most articles aimed to investigate the public’s reaction to fake health information, concluding that health agencies and professionals should increase their online presence. The articles also suggest that future work should aim to improve the quality of health information on social media platforms, develop new tools and strategies to combat fake news sharing, and study the credibility of health information. Nonetheless, those in control of the platforms are the only ones which can take effective measures to ensure that their users receive reliable information.

scholarly journals A Framework for Finding Outspread News Pattern on Diverse Dataset using Time-Series

2020 ◽  
Vol 9 (3) ◽  
pp. 1102-1106
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Time Series ◽  
Pattern Analysis ◽  
Fake News ◽  
False Information ◽  
The People ◽  
The Social ◽  
Future Direction ◽  
The Individual

Expressing feeling or opinion is an inherent property of the individual and Now a day’s social media becomes an integral part of everyone’s life. It is a great medium to analyze the feeling of mass, but sometimes it flows the false feeling in the form of fake news or contents posted on social media. These fake content affects the people in the form of sentiments or companies in the business loss/profit, because most of the people make opinions based on what they read on social media. In fact, fake news or false information can create the damage among the individual, so it should be identified as early as possible. The interest in finding the pattern of fake news has been growing very rapidly in the last few years. In this article we proposed a comprehensive pattern analysis of viral contents, real or fake news on twitter using time series analysis. The proposed technique is simple but effective for detecting and analysis fake contents on the social networks. Experiments results shows that our proposed technique outperformed for differentiating real vs fake news on twitter. Finally, we identify and discuss future direction.

scholarly journals Detecting Fake Users on Social Media with a Graph Database

2021 ◽  
Vol 12 (1) ◽  
pp. 49-56
Author(s):  
Yichun Zhao ◽  
Jens Weber
Keyword(s):  
Social Media ◽  
Centrality Measures ◽  
Graph Database ◽  
Fake News ◽  
False Information ◽  
Daily Lives ◽  
The Social ◽  
Efficient Data ◽  
Personal Content ◽  
Efficient Data Analysis

Social media has become a major part of people’s daily lives as it provides users with the convenience to connect with people, interact with friends, share personal content with others, and gather information. However, it also creates opportunities for fake users. Fake users on social media may be perceived as popular and influential if not detected. They might spread false information or fake news by making it look real, manipulating real users into making  certain decisions. In computer science, a social network can be treated as a graph, which is a data structure consisting of nodes being the social media users, and edges being the connections between users. Graph data can be stored in a graph database for efficient data analysis. In this paper, we propose using a graph database to achieve an increased scalability to accommodate larger graphs. Centrality measures as features were extracted for the random forest classifier to successfully detect fake users with high precision, recall, and accuracy. We have achieved promising results especially when compared with previous studies.   

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 248
Author(s):  
Simone Leonardi ◽  
Giuseppe Rizzo ◽  
Maurizio Morisio
Keyword(s):  
Social Media ◽  
Network Architecture ◽  
Diffusion Mechanism ◽  
Automated Classification ◽  
Fake News ◽  
Neural Network Architecture ◽  
User Classification ◽  
The Social ◽  
Network Topologies

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

scholarly journals Advancing children’s news media literacy: learning from the practices and experiences of young Australians

2018 ◽  
Vol 41 (5) ◽  
pp. 689-707
Author(s):  
Tanya Notley ◽  
Michael Dezuanni
Keyword(s):  
Social Media ◽  
Media Literacy ◽  
News Media ◽  
Social Impact ◽  
Literacy Learning ◽  
Literacy Education ◽  
Fake News ◽  
News Stories ◽  
Media Literacy Education ◽  
Nationally Representative

Social media use has redefined the production, experience and consumption of news media. These changes have made verifying and trusting news content more complicated and this has led to a number of recent flashpoints for claims and counter-claims of ‘fake news’ at critical moments during elections, natural disasters and acts of terrorism. Concerns regarding the actual and potential social impact of fake news led us to carry out the first nationally representative survey of young Australians’ news practices and experiences. Our analysis finds that while social media is one of young people’s preferred sources of news, they are not confident about spotting fake news online and many rarely or never check the source of news stories. Our findings raise important questions regarding the need for news media literacy education – both in schools and in the home. Therefore, we consider the historical development of news media literacy education and critique the relevance of dominant frameworks and pedagogies currently in use. We find that news media has become neglected in media literacy education in Australia over the past three decades, and we propose that current media literacy frameworks and pedagogies in use need to be rethought for the digital age.

Sign in / Sign up

Export Citation Format

Share Document