Fake News In Social Media Research Articles

OptNet-Fake: Fake News Detection in Socio-Cyber Platforms Using Grasshopper Optimization and Deep Neural Network

Exposure to half-truths or lies has the potential to undermine democracies, polarize public opinion, and promote violent extremism. Identifying the veracity of fake news is a challenging task in distributed and disparate cyber-socio platforms. To enhance the trustworthiness of news on these platforms, in this article, we put forward a fake news detection model, OptNet-Fake. The proposed model is architecturally a hybrid that uses a meta-heuristic algorithm to select features based on usefulness and trains a deep neural network to detect fake news in social media. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$</tex-math> </inline-formula> -D feature vectors for the textual data are initially extracted using the term frequency inverse document frequency (TF-IDF) weighting technique. The extracted features are then directed to a modified grasshopper optimization (MGO) algorithm, which selects the most salient features in the text. The selected features are then fed to various convolutional neural networks (CNNs) with different filter sizes to process them and obtain the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram features from the text. These extracted features are finally concatenated for the detection of fake news. The results are evaluated for four real-world fake news datasets using standard evaluation metrics. A comparison with different meta-heuristic algorithms and recent fake news detection methods is also done. The results distinctly endorse the superior performance of the proposed OptNet-Fake model over contemporary models across various datasets.

A Gamified Platform to Support Educational Activities About Fake News in Social Media

A Gamified Platform to Support Educational Activities About Fake News in Social Media

Social media users’ attitudes toward pervasiveness of fake news in Arab countries and its negative effects: Kuwait as a case study

PurposeThe purpose of this study is to examine the respondents’ evaluation of the pervasiveness of fake news through various SM platforms in Kuwait. The authors also examined the respondents’ attitudes toward most fake news on SM. A total of 1,539 Kuwaitis were selected.Design/methodology/approachThe questionnaire was the major tool for this study. The respondents, from whom demographic information was obtained, were asked about which SM platforms most frequently spread fake news, their attitudes toward the subjects most frequently involved in spreading fake news, their degree of use of the six SM platforms and interest in various subjects, and the attitudes toward the negative nature of SM news. SPSS was used for the data analysis.FindingsThe results showed that WhatsApp was the most likely to be used to disseminate fake news; Twitter and Instagram ranked second. The younger subjects were affected more by text and voice clips than the older ones.Originality/valueThe study, hopefully, produces new knowledge on the subject of fake news in social media, especially in the Arab world, since there are few studies conducted in the region. The study showed that WhatsApp was the SM tool most likely to be responsible for disseminating fake news in Kuwait, which may shed light on the usage of this application to be a news tool, rather than merely an interpersonal communication medium.

FNR: a similarity and transformer-based approach to detect multi-modal fake news in social media

FNR: a similarity and transformer-based approach to detect multi-modal fake news in social media

COMMUNICATION TECHNOLOGY IN FIGHTING HOAX ON SOCIAL MEDIA FACEBOOK

Hoax is fake news, or misleading news that has been around for a long time, but during the Covid-19 pandemic, there are lots of new fake news that are increasing. In this context Facebook as a social media becomes a place for spreading Hoaks or fake news in social media. During this pandemic, this is evidenced by the existence of more than 800 Hoax cases regarding Covid-19 in Indonesia, according to the Ministry of the Republic of Indonesia, seeing this MAFINDO (Indonesian Anti-slander Society) has made an application to prevent hoaxes that are spread in Indonesia with the aim of analyzing data or information. news that is read with the aim of preventing fake news in Indonesia.

Classification and Analysis on Fake News in Social Media Using Machine Learning

Classification and Analysis on Fake News in Social Media Using Machine Learning

Emotional Regulation and Institutional Maintenance: Debunking Fake News with Emotions

How to fight fake news? Through the study of the social entrepreneur Laura Brennan, in the case of the HPV vaccination in Ireland from 2015-19 we show the importance emotional work in social media to fight fake news. We propose a model of emotional regulation in which social entrepreneurs use mirroring, distancing and enactment strategies to maintain institutional frames and debunk fake news. We claim about the importance of the transformation of negative emotions in positive emotional energy through identification and about the embodiment of the truth in creating a legitimizing frame. By that, we contribute to the understanding social entrepreneurship and emotion work and the embodiment of emotions in fighting against fake news in social media.

Building a National Commitment Against Hoax Threats for Indonesian Sovereignty

This research initiated to offer a strong commitment to defend the sovereignty of Indonesia. The purpose of this research to advise and create awareness of each people to commit and must be wise in receiving and disseminating information, especially hoax. This research used the literature study method by analyzing the impact that has been caused by hoaxes. In collected the data, the author’s team searched, listed, and displayed the main data and the supported data as the most fake news in social media. The author classified the data into two, are the main data was accessed from the Minister of Communication and Informatics news, even was completed by the supported data that were listed from the news posted on declared hoaxes as a common enemy that destroys the order of life in Indonesia. The data have been collected by the case studies were easily influencing people’s opinions and even could be potentially disrupt the national security of our nation. The result of the research in order to produce an important reference that educate the public to deal with hoaxes. This research initiated to offer a wise solution for face hoax through collected the data have been listed by literature study and analyzed the impacts for our society in Indonesia. In digital literacy, people need to know the characteristics of hoaxes so as not to get caught up with fake news, or slander that is circulating. This fake news will certainly harm many parties, including you. For that, be wise in reading, analyzing, and trusting information, before you disseminate it.

An Experimental Study of Fake News Detection on Covid-19 Twitter data and a Conceptual framework for the Early Detection and Prevention of Fake News in Social Media

An Experimental Study of Fake News Detection on Covid-19 Twitter data and a Conceptual framework for the Early Detection and Prevention of Fake News in Social Media

IDENTIFICATION OF FAKE NEWS PUBLISHED IN THE PAPER EDITION OF A PROVINCIAL NEWSPAPER IN THE ERA OF TRUMP'S DIGITAL DISINFORMATION AND THE START OF COVID. FROM GLOBAL TO LOCAL

Fake news has become a problem in the general information society and owes its preeminence, to a large extent, to the democratisation of social networks and the polarisation of different kinds of forces. But beyond the digital channels, there is a public, in this case of a local-provincial nature, which follows the news as it has traditionally done: mainly through its newspaper of reference, in print. This paper analyses, quantitatively and qualitatively, the contents related to fake news appearing in the printed version of Diario Informacion, between 3 February 2020, the start of the US election campaign and the appearance of the first case of coronavirus in Spain, two clear events that lead directly to fake news in origin or destination, until 21 January 2021, the latter day following the start of the Joe Biden era. The aim is to try to find out whether the acerbic debate and the prominence of fake news in digital channels have a proportional transfer to the pages of this newspaper and whether they are priority issues for the reader. The study makes it clear that, compared to the bombardment and noise surrounding fake news in social media, in the print press there is a predominance of calm reflection, analysis of the problem and a clear and endorsed denunciation of this type of message. And what is more important: it is almost no news for the paper.

Covid-19 Fake News Detection: A Survey

The increase of fake news in social media, especially about Covid-19, poses a real threat to the mental and physical health of people. It is an important task to detect such news and to stop it spreading. In this article we describe the main approaches for fake news about Covid-19 detection, including Classical Machine Learning models, models based on Neural Networks and models, which were created based on the other approaches and preprocessing steps. We analyze the results of the challenge “Constraint@AAAI2021 - COVID19 Fake News Detection”, the main goal of which was the binary classification of news collected from social media for fake and real news. We analyze the best approaches which were proposed by researchers during the challenge. Also, we describe datasets of fake news related to Covid-19, which could be useful for the detection and classification of such news.

Optimization and improvement of fake news detection using deep learning approaches for societal benefit

Fake news is a topic that has been discussed for quite some time. Prior to the internet era, it was mostly distributed through yellow journalism, with a focus on sensational news such as crime, rumours, accidents, and amusing news. To rescue the life of people from these fake news propagation, detection of fake news at an early stage becomes the most crucial step. People unknowingly propagate fake news and become a part of fake news propagation. While the original fake news propagators are the one with their aim to target innocent people for spreading the fake news. To stop this series of events, fake news detection and its pattern of propagation becomes very essential to society and the government. Various techniques exist to detect fake news in social media, among which neural networks have shown effective results. For this research, a deep learning based approach has been used to differentiate false news from the original ones. A LSTM neural network has been used to build the proposed model. Besides the neural network, a gloVe word embedding has been used for vector representation of textual words. Also, for feature extraction or vectorization, tokenization technique has been used. N-grams concept is used to enhance the proposed model. The comparative analysis of multiple fake news detection techniques is analysed. The results of proposed model have been evaluated using accuracy metrics. The model outperformed by achieving 99.88% of accuracy.

Improving fake news detection with domain-adversarial and graph-attention neural network

With the widespread use of online social media, we have witnessed that fake news causes enormous distress and inconvenience to people's social life. Although previous studies have proposed rich machine learning methods for identifying fake news in social media, the task of detecting fake news in emerging news events/domains remains a challenging problem due to the wide range of news topics on social media as well as the evolution and variation of fake news contents in the web. In this study, we propose an approach which we term “domain-adversarial and graph-attention neural network” (DAGA-NN) model to address the challenge. Its main advantage is that, in a text environment with multiple events/domains, only partial domain sample data are needed to train the model to achieve accurate cross-domain fake news detection in those domains with few (or even no) samples, which makes up for the limitations of traditional machine learning in fake news detection tasks due to news content evolution or cross-domain identification (where there is no sample data). Extensive experiments were conducted on two multimedia datasets of Twitter and Weibo, and the results showed that the proposed model was very effective in detecting fake news across events/domains.

Introduction: Towards a sociology of pandemics

With SARS-CoV-2 a new coronavirus is spreading around the world that challenges governments and triggers unprecedented social responses. Worldwide people have had to manage the experience of an uncertain new threat under very different conditions. A growing body of research and theoretical approaches tries to make sense of the social responses to the pandemic. This monograph issue contributes to the research on the first wave of the pandemic from the perspective of the sociology of risk and uncertainty. This includes a number of key topics such as care workers’ experiences in the Netherlands, stigmatisation and Othering in India, the multidimensionality of social inequalities in the experience of confinement in Argentina, mourning practices in Iran, discourses of legitimacy in Sweden, distrust in government in Hong Kong, risk communication in the UK, and fake news in social media. This introduction sets these contributions in the broader context of key debates in the sociology of risk and uncertainty such as rational, in-between and non-rational approaches to risk in everyday life, the Othering of social groups, the multidimensionality of risk and inequality, the debate about methodological cosmopolitanism, the discursive construction of legitimacy, the significance of (dis)trust for public engagement with risk, shortcomings in risk communication, and framing of fake news and conspiracy theories. The monograph concludes with reflections on perspectives for social learning, the importance of the society–nature–technology nexus for the understanding of the crisis, and finally, with envisioning possible pathways towards a new normal.

Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision

The problem of automatic detection of fake news in social media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded as a straight-forward, binary classification problem, the major challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor, and recent approaches utilizing distributional semantics require large training corpora. In this paper, we introduce an alternative approach for creating a large-scale dataset for tweet classification with minimal user intervention. The approach relies on weak supervision and automatically collects a large-scale, but very noisy, training dataset comprising hundreds of thousands of tweets. As a weak supervision signal, we label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean, inaccurate dataset, the results are comparable to those achieved using a manually labeled set of tweets. Moreover, we show that the combination of the large-scale noisy dataset with a human labeled one yields more advantageous results than either of the two alone.

Fake News Sharing

Fake news has become a growing problem for societies, spreading virally and transforming into harmful impacts in social networks. The problem of fake news is even more troubling in the healthcare context. In the healthcare literature, it has been well established that threat situations and coping responses facilitate information sharing and seeking among the public. Along a similar vein, we argue that threat and coping related cues are important indicators of shareworthiness of fake news in social media. We address the following research questions associated with fake news sharing in the context of Zika virus: How do threat- and coping-related cues influence fake news sharing? We characterize threat situations that have threat and severity cues and coping responses that are based on reaction to protection and fear cues. The results indicate the significant positive effect of threat cues and protection cues on fake news sharing. Such an investigation can allow the monitoring of viral fake messages in a timely manner.

Digital Images on Social Media and Proliferation of Fake News on Covid-19 in Kano, Nigeria

Misinformation becomes rampant in the digital age and social media provide people with the opportunities for engaging more actively in society. The objectives of the study are: (i) to ascertain the extent to which residents of Kano have been exposed to digital images on Covid-19 that often accompany fake news in social media; (ii) to establish the extent to which residents of Kano are influenced by fake news on Covid-19 with images; (iii) to find out the factors that often lead to the influence of fake news with digital images on Covid-19 among social media users in Kano; (iv) to ascertain the social media platforms mostly used in spreading fake news about Covid-19 in the state. Theoretically, Technological Determinism and Perception theories were adopted to analyze these issues. A Positivist approach to data generation and analysis was adopted using the survey method. Two local governments were selected for the study: Tarauni and Kano Municipal. Tarauni local government area had the highest number of the Covid-19 cases, while Kano Municipal had the lowest number. The population of the study consist of 593,087 with a sample size of 400 respondents derived from Taro Yamane’s sample size prediction table. The respondents were reached through cluster sampling. A total of 400 copies of questionnaires were administered to respondents in Tarauni and Kano Municipal. However, only 385 copies, which represent (96%), were retrieved and found usable for the study as the remaining 15 were not returned. The study found that Kano residents were significantly exposed to digital images that often accompany fake news in social media. They read news online every day, prefer news accompanied by images, share and like news online. The study also found that Kano residents are influenced by fake news with digital images on Covid-19 to a very great extent, especially on Facebook. Factors responsible for proliferation of fake news on social media include: perception or instinct, eagerness to be the first to share images and lack of knowledge about image verification tools. The study concludes that ignorance and the old belief that pictures do not lie were responsible for this.

Detecting Fake News in Social Media Using Voting Classifier

The availability of social media, blogs, and websites to everyone creates a lot of problems. False news is a critical issue that can affect individuals or entire countries. Fake news can be created and shared all over the world. The 2016 presidential election in the United States illustrates that problem. As a result, controlling social media is essential. Machine learning (ML) algorithms help to detect fake news automatically. This article proposes a framework for detecting fake news based on feature extraction and feature selection algorithms and a set of voting classifiers. The proposed system distinguishes fake news from real news. First, we preprocessed the data taking unnecessary characters and numbers and reducing the words in the dictionary (lemmatization). Second, we extracted some important features using two feature extraction types: the term frequency-inverse document frequency (TF-IDF) technique and the DOC2VEC algorithm, a word embedding technique. Third, the extracted characteristics were reduced with the help of the chi-square algorithm and the analysis of variance (ANOVA) algorithm. We used three data sets that are published online: Media-Eval, Fake-or-Real-News, and ISOT. To evaluate the proposed framework, we used five different performance metrics: accuracy (ACC), the area under the curve (AUC), precision, recall, and f1-score. Our system achieved 94.6% of ACC for the Fake-or-Real dataset. For the Media-Eval dataset, the system achieved 92.3% of ACC. For the ISOT dataset, the system achieved 100% of ACC. We contrast the proposed framework with several other classification algorithms. The experimental results show that the proposed framework outperforms the existing works in terms of ACC by 0.2% for the ISOT dataset.

Lightweight Chain for Detection of Rumors and Fake News in Social Media

Social media has become one of the most important sources of news in our lives, but the process of validating news and limiting rumors remains an open research issue. Many researchers have suggested using Blockchain to solve this problem, but it has traditionally failed due to the large volume of data and users in such environments. In this paper, we propose to modify the structure of the Blockchain while preserving its main characteristics. We achieve this by integrating customize blockchain with the Text Mining (TM) algorithm to create a modified Light Weight chain (LWC). LWC will speed up the verification process, which is carried out through proof of good history where the nodes will have the weights according to their previous posts. Moreover, the LWC will be compatible with different applications such as verifying the authenticity of news or legal religious ruling (fatwas). In this research, we have implemented a simple model to simulate the proposed LWC for the detection of fake news and preserving the characteristics and features of the traditional Blockchain. The results on experimental data reflect the effectiveness of the proposed algorithm in establishing the chain.

Linguistic-Based Detection of Fake News in Social Media

The tremendous growth and impact of fake news as a hot research field gained the public&amp;rsquo;s attention and threatened their safety in recent years. However, there is a wide range of developed fashions to detect fake contents, either those human-based approaches or machine-based approaches; both have shown inadequacy and limitations, especially those fully automatic approaches. The purpose of this analytic study of media news language is to investigate and identify the linguistic features and their contribution in analyzing data to detect, filter, and differentiate between fake and authentic news texts. This study outlines promising uses of linguistic indicators and adds a rather unconventional outlook to prior literature. It utilizes qualitative and quantitative data analysis as an analytic method to identify systematic nuances between fake and factual news in terms of detecting and comparing 16 attributes under three main linguistic features categories (lexical, grammatical, and syntactic features) assigned manually to news texts. The obtained datasets consist of publicly available right documents on the Politi-fact website and the raw (test) data set collected randomly from news posts on Facebook pages. The results show that linguistic features, especially grammatical features, help determine untrustworthy texts and demonstrate that most of the test news tends to be unreliable articles.