scholarly journals Fake news identification

2021 ◽  
Author(s):  
Peter Racsko
Keyword(s):  
Information Technology ◽  
Everyday Life ◽  
Economic Status ◽  
Social Aspects ◽  
Fake News ◽  
False Information ◽  
Conspiracy Theories ◽  
Electronic Publications ◽  
Valid Information

Abstract Fake news, deceptive information, and conspiracy theories are part of our everyday life. It is really hard to distinguish between false and valid information. As contemporary people receive the majority of information from electronic publications, in many cases fake information can seriously harm people’s health or economic status. This article will analyze the question of how up-to-date information technology can help detect false information. Our proposition is that today we do not have a perfect solution to identify fake news. There are quite a few methods employed for the discrimination of fake and valid information, but none of them is perfect. In our opinion, the reason is not in the weaknesses of the algorithms, but in the underlying human and social aspects.

scholarly journals False news around COVID-19 circulated less on Sina Weibo than on Twitter. How to overcome false information?

2020 ◽  
Author(s):  
Cristina Pulido Rodríguez ◽  
Beatriz Villarejo Carballido ◽  
Gisela Redondo-Sama ◽  
Mengna Guo ◽  
Mimar Ramis ◽  
...  
Keyword(s):  
Social Media ◽  
Evidence Based ◽  
World Economic Forum ◽  
Fake News ◽  
Best Interest ◽  
False Information ◽  
Conspiracy Theories ◽  
Sina Weibo ◽  
World Economic ◽  
False News

Since the Coronavirus health emergency was declared, many are the fake news that have circulated around this topic, including rumours, conspiracy theories and myths. According to the World Economic Forum, fake news is one of the threats in today's societies, since this type of information circulates fast and is often inaccurate and misleading. Moreover, fake-news are far more shared than evidence-based news among social media users and thus, this can potentially lead to decisions that do not consider the individual’s best interest. Drawing from this evidence, the present study aims at comparing the type of Tweets and Sina Weibo posts regarding COVID-19 that contain either false or scientific veracious information. To that end 1923 messages from each social media were retrieved, classified and compared. Results show that there is more false news published and shared on Twitter than in Sina Weibo, at the same time science-based evidence is more shared on Twitter than in Weibo but less than false news. This stresses the need to find effective practices to limit the circulation of false information.

Fake News

2018 ◽  
Author(s):  
Bente Kalsnes
Keyword(s):  
Communication Technologies ◽  
Social Motivation ◽  
Research Literature ◽  
Fake News ◽  
False Information ◽  
Conspiracy Theories ◽  
Different Types ◽  
Fact Checking ◽  
Definition Of ◽  
Different Levels

Fake news is not new, but the American presidential election in 2016 placed the phenomenon squarely onto the international agenda. Manipulation, disinformation, falseness, rumors, conspiracy theories—actions and behaviors that are frequently associated with the term—have existed as long as humans have communicated. Nevertheless, new communication technologies have allowed for new ways to produce, distribute, and consume fake news, which makes it harder to differentiate what information to trust. Fake news has typically been studied along four lines: Characterization, creation, circulation, and countering. How to characterize fake news has been a major concern in the research literature, as the definition of the term is disputed. By differentiating between intention and facticity, researchers have attempted to study different types of false information. Creation concerns the production of fake news, often produced with either a financial, political, or social motivation. The circulation of fake news refers to the different ways false information has been disseminated and amplified, often through communication technologies such as social media and search engines. Lastly, countering fake news addresses the multitude of approaches to detect and combat fake news on different levels, from legal, financial, and technical aspects to individuals’ media and information literacy and new fact-checking services.

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.

scholarly journals Fighting the COVID-19 Infodemic with Neonet: A Text-Based Supervised Machine Learning Algorithm

2021 ◽  
Author(s):  
Mohammad AR Abdeen ◽  
Ahmed Abdeen Hamed ◽  
Xindong Wu
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Network Science ◽  
Learning Algorithm ◽  
Noun Phrases ◽  
Supervised Machine Learning ◽  
News Article ◽  
Fake News ◽  
False Information ◽  
Conspiracy Theories

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety and follow proper procedures to mitigate the risks. Here, we present a novel supervised machine learning text mining algorithm that analyzes the content of a given news article and assign a label to it. The NeoNet algorithm is trained by noun-phrases features which contributes a network model. The algorithm was tested on a real-world dataset and predicted the label of never-seem articles and flags ones that are suspicious or disputed. In five different fold comparisons, NeoNet surpassed prominent contemporary algorithm such as Neural Networks, SVM, and Random Forests. The analysis shows that the NeoNet algorithm predicts a label of an article with a 100% precision using a non-pruned model. This highlights the promise of detecting disputed online contents that may contribute negatively to the COVID-19 pandemic. Indeed, using machine learning combined with powerful text mining and network science provide the necessary tools to counter the spread of misinformation, disinformation, fake news, rumors, and conspiracy theories that is associated with the COVID19 Infodemic.

scholarly journals Monetary incentives do not reduce the repetition-induced truth effect

2021 ◽  
Author(s):  
Felix Speckmann ◽  
Christian Unkelbach
Keyword(s):  
Response Times ◽  
Repetition Effect ◽  
Monetary Incentives ◽  
Fake News ◽  
False Information ◽  
Conspiracy Theories ◽  
Truth Effect

AbstractPeople rate and judge repeated information more true than novel information. This truth-by-repetition effect is of relevance for explaining belief in fake news, conspiracy theories, or misinformation effects. To ascertain whether increased motivation could reduce this effect, we tested the influence of monetary incentives on participants’ truth judgments. We used a standard truth paradigm, consisting of a presentation and judgment phase with factually true and false information, and incentivized every truth judgment. Monetary incentives may influence truth judgments in two ways. First, participants may rely more on relevant knowledge, leading to better discrimination between true and false statements. Second, participants may rely less on repetition, leading to a lower bias to respond “true.” We tested these predictions in a preregistered and high-powered experiment. However, incentives did not influence the percentage of “true” judgments or correct responses in general, despite participants’ longer response times in the incentivized conditions and evidence for knowledge about the statements. Our findings show that even monetary consequences do not protect against the truth-by-repetition effect, further substantiating its robustness and relevance and highlighting its potential hazardous effects when used in purposeful misinformation.

Political psychology in the digital (mis)information age: A model of news belief and sharing

2020 ◽  
Author(s):  
Jay Joseph Van Bavel ◽  
Elizabeth Ann Harris ◽  
Philip Pärnamets ◽  
Steve Rathje ◽  
Kimberly Doell ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Psychology ◽  
Information Age ◽  
Policy Research ◽  
Integrative Model ◽  
Future Research ◽  
Fake News ◽  
Policy Makers ◽  
Conspiracy Theories ◽  
The Social

The spread of misinformation, including “fake news,” propaganda, and conspiracy theories, represents a serious threat to society, as it has the potential to alter beliefs, behavior, and policy. Research is beginning to disentangle how and why misinformation is spread and identify processes that contribute to this social problem. We propose an integrative model to understand the social, political, and cognitive psychology risk factors that underlie the spread of misinformation and highlight strategies that might be effective in mitigating this problem. However, the spread of misinformation is a rapidly growing and evolving problem; thus scholars need to identify and test novel solutions, and work with policy makers to evaluate and deploy these solutions. Hence, we provide a roadmap for future research to identify where scholars should invest their energy in order to have the greatest overall impact.

Jyotismati Taila and Ashtamangal Ghrita Nasya in the management of Mental Retardation - A Comparative Study

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
M. R. Maniar ◽  
K. S. Patel ◽  
I. U. Mistry
Keyword(s):  
Mental Retardation ◽  
Comparative Study ◽  
Everyday Life ◽  
Mental Status ◽  
Study Design ◽  
Comparative Effectiveness ◽  
Social Aspects ◽  
Management Approach ◽  
Classical Text ◽  
Mental Functions

Mental retardation is still elusive to researchers due to multidimensionality of psychological, medical, educational and social aspects, which alters mental functions and capability. Mental sub capability divided in 4 categories, Mild, Moderate, Severe and Profound. Chief aim of management of mental retardation is to make child more capable of performing common activities of everyday life by positive improvement in mental sub-capability. Mental retardation required multidimensional management approach. Present study focused on medicinal intervention, particularly analysis of comparative effectiveness of selected drug formulations (Astamangalghrita and Jyotismatitaila) from classical text of Ayurveda. Study design with the aims to compare the effectiveness of Jyotismatitaila and Astamangal Ghrita Nasya on Mental retardation. Assessment were based on Mental Status Score and IQ score taken before starting of treatment and after completion of treatment in both group. Obtained data was analyzed statistically. In this study, from result we conclude that both drugs are effective to improve Mental Status parameter and in IQ, but higher percentage and significance wise Jyotismati Taila had better result than Astamangal Ghrita Nasya.

Due Process: A Conversation with Maria Augusta Ramos

2019 ◽  
Vol 72 (3) ◽  
pp. 52-58
Author(s):  
Nilo Couret
Keyword(s):  
Criminal Justice ◽  
Everyday Life ◽  
Criminal Justice System ◽  
Justice System ◽  
Due Process ◽  
Democratic Institutions ◽  
Fake News ◽  
Truth Telling

Nilo Couret interviews Brazilian documentary filmmaker Maria Augusta Ramos. Her recent documentary, O Processo (The Trial, 2018), chronicles the “parliamentary coup” against Dilma Rousseff, delving into the impeachment process and the former president's trial in the Senate. In O Processo, Ramos engages with enduring themes and subjects from her twenty-year career, particularly her well-known Justice Trilogy, which examined the Brazilian criminal justice system. For Ramos, documentary shares an affinity with forensic discourse when its purpose is truth-telling in the service of justice. Rousseff's trial and impeachment, however, find the filmmaker probing how justice has been sundered from the truth in a contemporary moment when corruption scandals and fake news compromise our democratic institutions. Her films combine an observational approach with institutional analyses in order to reveal the workings of power behind the surfaces of everyday life.

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.

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