Real News Research Articles

Verification of News Video Content: Findings from a Study of Journalism Students

ABSTRACT The rapid spread of misinformation online has been deemed as a growing problem in the current digital media environment with significant impact both on journalism and on society at large. As news practitioners are increasingly challenged by information overload and the need to process huge volumes of unstructured and unfiltered data within a very short time span, the veracity of online and User-Generated Content (UGC) and its spread through social media and video platforms has been identified as a key concern. This study aims to extend the body of knowledge on the verification of news video content, derived from social media platforms. Using a web-based tool that analyses the online context around a video post and presents a number of verification signals, a set of fake and real news videos coming from YouTube and Facebook were examined by a sample of journalism students (N = 90) regarding their veracity. A sample of professional journalists (N = 17) was also used for qualitative comparison purposes and additional results. The results of the study highlight the significance of online context as well as the efficacy of a semi-automated process in the verification of video content and journalism practice, in conjunction with useful verification features and practices.

A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

creasing number of social media platforms, emerging new technologies, and population growth which results in the rate of using social media has increased rapidly. With an increasing number of users on online platforms comes to a variety of problems like fake news. The extensive growth of fake news on social media can have a serious impact on the real world and became a cause of concern for net users and governments all over the world. Distinguishing between real news and fake news becoming more challenging. The amount of fake news has become a disguise. In this paper, we have done a survey on detection techniques for fake news using Algorithms and Deep learning techniques. We have compared machine learning algorithms like Naïve-Bayes, Decision tree, SVM, Adaboost, etc. Comparing the accuracy

Diffusion of real versus misinformation during a crisis event: A big data-driven approach

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misinformation on social news.

Poisoning the information well?

Abstract This study examines the impact of fake news discourse on perceptions of news media credibility. If participants are told they have been exposed to fake news, does this lead them to trust information institutions less, including the news media? Study 1 (n = 188) found that news media credibility decreased when participants were told they saw fake news, while news credibility did not change when participants were told they saw real news. Study 2 (n = 400) found that those who saw fake news – and were told they saw a fake news post – decreased their trust in the news media while those who saw fake news and were not debriefed did not change their perceptions of the news media. This shows that the social impact of fake news is not limited to its direct consequences of misinforming individuals, but also includes the potentially adverse effects of discussing fake news.

A Signal Detection Approach to Understanding the Identification of Fake News

Researchers across many disciplines seek to understand how misinformation spreads with a view toward limiting its impact. One important question in this research is how people determine whether a given piece of news is real or fake. In the current article, we discuss the value of signal detection theory (SDT) in disentangling two distinct aspects in the identification of fake news: (a) ability to accurately distinguish between real news and fake news and (b) response biases to judge news as real or fake regardless of news veracity. The value of SDT for understanding the determinants of fake-news beliefs is illustrated with reanalyses of existing data sets, providing more nuanced insights into how partisan bias, cognitive reflection, and prior exposure influence the identification of fake news. Implications of SDT for the use of source-related information in the identification of fake news, interventions to improve people’s skills in detecting fake news, and the debunking of misinformation are discussed.

Analysing the behavioural finance impact of 'fake news' phenomena on financial markets: a representative agent model and empirical validation

This paper proposes an original behavioural finance representative agent model, to explain how fake news’ empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis. The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news, and predicts a novel secondary impact of fake news: that fake news in a security amplifies underreactions to subsequent real news for the security. Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event, this paper finds strong qualitative validation for its model’s dynamics and predictions.

An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

Inauthentic Newsfeeds and Agenda Setting in a Coordinated Inauthentic Information Operation

The 2015–2017 Russian Internet Research Agency (IRA)’s coordinated information operation is one of the earliest and most studied of the social media age. A set of 38 city-specific inauthentic “newsfeeds” made up a large, underanalyzed part of its English-language output. We label 1,000 tweets from the IRA newsfeeds and a matched set of real news sources from those same cities with up to five labels indicating the tweet represents a world in unrest and, if so, of what sort. We train a natural language classifier to extend these labels to 268 k IRA tweets and 1.13 million control tweets. Compared to the controls, tweets from the IRA were 34% more likely to represent unrest, especially crime and identity danger, and this difference jumped to about twice as likely in the months immediately before the election. Agenda setting by media is well-known and well-studied, but this weaponization by a coordinated information operation is novel.

Visual cognition of fake news: the effects of consumer brand engagement

ABSTRACT Consumers’ engagement with fake news has impacted the way consumers process information about brands on the Internet. The purpose of this article is to analyze the factors that affect the amount of cognitive processing of fake news. Two experimental studies were carried out. Study 1 participants were invited to look at web pages, while those in Study 2 had access to information on social networks (Facebook and Twitter). In both studies, eye movements were measured to examine cognitive processing tasks. The participants’ gaze behavior was used to assess the amount of cognitive processing and the reduction of intentional control, for different levels of consumer brand engagement. The results demonstrate that the presence of fake news increases the amount of cognitive processing of the viewer. Furthermore, the reduction in intentional control is less with fake news than with real news. Finally, the findings indicate that high consumer engagement with the brand results in a greater amount of cognitive processing of fake news. The results of this study demonstrate that the exposure of fake news involving certain brands may have a greater effect on sustaining visual cognition than real news.

Tuned bidirectional encoder representations from transformers for fake news detection

Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccurate news from real news can be done with natural language processing (NLP) technologies. In this paper, we proposed bidirectional encoder representations from transformers (BERT) for inaccurate news identification. BERT is a language model based on deep learning technologies and it has found effective for many NLP tasks. In this study, we use transfer learning and fine-tuning to adapt BERT for inaccurate news identification. The experiments show that our method could achieve accuracy of 99.23%, recall 99.46%, precision 98.86%, and F-Score of 99.15%. It is largely better than traditional method for the same tasks.

The Influence of Political Ideology on Fake News Belief: The Portuguese Case

The relationship between a subject’s ideological persuasion with the belief and spread of fake news is the object of our study. Departing from a left- vs. right-wing framework, a questionnaire sought to position subjects on this political-ideological spectrum and demanded them to evaluate five pro-left and pro-right fake and real news, totaling 20 informational products. The results show the belief and dissemination of (fake) news are related to the political ideology of the participants, with right-wing subjects exhibiting a greater tendency to accept fake news, regardless of whether it is pro-left or pro-right fake news. These findings contradict the confirmation bias and may suggest that a greater influence of factors such as age, the level of digital news literacy and psychological aspects in the judgment of fake news are at play. Older and less educated respondents indicated they believed and would disseminate fake news at greater rates. Regardless of the ideology they favor, the Portuguese attributed higher credibility to the sample’s real news, a fact that can be meaningful regarding the fight against disinformation in Portugal and elsewhere.

Infodemic: the effect of death-related thoughts on news-sharing

Research on the sharing of fake news has primarily focused on the manner in which fake news spreads and the literary style of fake news. These studies, however, do not explain how characteristics of fake news could affect people’s inclination toward sharing these news articles. Drawing on the Terror Management Theory, we proposed that fake news is more likely to elicit death-related thoughts than real news. Consequently, to manage the existential anxiety that had been produced, people share the news articles to feel connected to close others as a way of resolving the existential anxiety. Across three experimental studies (total N = 416), we found that it was not news type per se (i.e., real versus fake news) that influenced news-sharing intentions; instead, it was the increased accessibility to death-related thoughts elicited from the content of news articles that motivated news-sharing. The findings support the Terror Management framework and contribute to the existing literature by providing an empirical examination of the underlying psychological motive behind fake news-sharing tendencies.

Fake News Detection on Social Media Using Machine Learning

The project examines the techniques behind detecting “fake news”, i.e. misleading news stories from trustworthy sources. The invention of the World Wide Web and the wide embracing of social media networks (such as Facebook, Twitter) opened the way for information sharing that has never before been seen in human history. The use of social media sites is becoming more independent as a forum for information creation and sharing than ever before. Some of the users are confusing and impractical. It’s no easy task to design a system that automatically highlights an article as dishonest or even misleading. You can tell if an email is reliable even if you are not an expert. And an authority in a particular area needs to go through the process of finding out the facts before reaching a judgement of what is true or not. Reviews suggest the use of the classifier algorithm to plump-up the detection of news stories via machine learning. But these models don’t represent the basic quality of language, such as word order and importance of words. It is very likely that two articles would have the same word count, but would have total differences in their content. The information technology group (information science) wasting in response to the matter. Fiercely each combatting the fake news may be a classical text classification effort that offers a simple proposal. There is a good deal of evidence that may be used for constructing a model of false news or real news. One suggestion is a kind of a Naive Bayes grouping, where articles that are deceptive, speculative, imaginative, or fresh or without researched supporting information may be considered one thing, while those that are measured as fully supported true information may be considered the other.

Can We Stop the Spread of False Information on Vaccination? How Online Comments on Vaccination News Affect Readers’ Credibility Assessments and Sharing Behaviors

This study used a 2 (type of news: fake vs. real) × 2 (presence of negative user comments: yes vs. no) × 2 (presence of positive user comments: yes vs. no) between-subjects experimental design to examine the differences in perceived news credibility and sharing intention between fake news and real news on vaccination. Fake news was found to generate a lower level of perceived credibility than real news, which subsequently decreased news sharing intention. Furthermore, negative user comments significantly lowered perceived news credibility, and this was especially true for real news. However, this adverse effect was found to be mitigated by the presence of positive user comments. The experimental results have important theoretical and practical implications for future research on fake news about health and science.

Mutual Influence of Users Credibility and News Spreading in Online Social Networks

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

The role of analytical reasoning and source credibility on the evaluation of real and fake full-length news articles

AimPrevious research has focused on accuracy associated with real and fake news presented in the form of news headlines only, which does not capture the rich context news is frequently encountered in real life. Additionally, while previous studies on evaluation of real and fake news have mostly focused on characteristics of the evaluator (i.e., analytical reasoning), characteristics of the news stimuli (i.e., news source credibility) and the interplay between the two have been largely ignored. To address these research gaps, this project examined the role of analytical reasoning and news source credibility on evaluation of real and fake full-length news story articles. The project considered both accuracy and perceived credibility ratings as outcome variables, thus qualifying previous work focused solely on news detection accuracy.MethodWe conducted two independent but parallel studies, with Study 2 as a direct replication of Study 1, employing the same design but in a larger sample (Study 1: N = 292 vs. Study 2: N = 357). In both studies, participants viewed 12 full-length news articles (6 real, 6 fake), followed by prompts to evaluate each article’s veracity and credibility. Participants were randomly assigned to view articles with a credible or non-credible source and completed the Cognitive Reflection Test as well as short demographic questions.FindingsConsistent across both studies, higher analytical reasoning was associated with greater fake news accuracy, while analytical reasoning was not associated with real news accuracy. In addition, in both studies, higher analytical reasoning was associated with lower perceived credibility for fake news, while analytical reasoning was not associated with perceived credibility for real news. Furthermore, lower analytical reasoning was associated with greater accuracy for real (but not fake) news from credible compared to non-credible sources, with this effect only detected in Study 2.ConclusionsThe novel results generated in this research are discussed in light of classical vs. naturalistic accounts of decision-making as well as cognitive processes underlying news articles evaluation. The results extend previous findings that analytical reasoning contributes to fake news detection to full-length news articles. Furthermore, news-related cues such as the credibility of the news source systematically affected discrimination ability between real and fake news.

Deceptive accusations and concealed identities as misinformation campaign strategies

This paper presents a new social media phenomenon that sees users lying about their deceptive motivations by either dishonestly claiming that they are not bots or by asserting that real news is actually fake news. We analyzed the use of the #FakeNews and #NotABot hashtags in Twitter data collected on the 2019 Canadian federal elections. Our findings show that the #FakeNews hashtag was most likely to be connected to an established news source rather than an actual fake news site and that users of the #NotABot hashtag were no more likely to be human than other users in our data set. This phenomenon of lying about lying has been used to discredit well-known news organizations and amplify political misinformation, showing how online influence campaigns continue to evolve to manipulate social media users even as people have become more aware of the dangers of online misinformation.

Types (Disinformation)

Types (Disinformation)

Analiza postrzegania prawdziwości informacji wśród studentów – podobieństwo super fake newsów do prawdziwych wiadomości

Cel i hipoteza: przedmiotem badań prezentowanych w artykule jest postrzeganie prawdziwości fake newsów w zależności od sposobu ich definiowania: jako dezinformacji intencjonalnej (wąska definicja) lub niezamierzonej dezinformacji (szeroka definicja). U podstaw analizy leży hipoteza, zgodnie z którą fake newsy definiowane wąsko będą skuteczniej udawały prawdziwe wiadomości niż fake newsy definiowane szeroko, a zatem użytkownicy będą obie te grupy fake newsów postrzegać w różny sposób. Metody badań: metoda sondażu diagnostycznego, zawierającego skalę fake newsów, oraz psychologiczny pomiar poziomu analitycznego myślenia i aktywnie otwartego myślenia. Wyniki i wnioski: analiza udowadnia, że użytkownicy postrzegają prawdziwość fake newsów w dwojaki sposób: twarde fake newsy (fake newsy definiowane szeroko) są postrzegane jako mniej prawdziwe, a super fake newsy (fake newsy definiowane wąsko) są postrzegane jako bardziej prawdziwe. Ponadto, podczas gdy analityczne myślenie wpływa korzystnie jedynie na rozpoznawanie twardych fake newsów, to aktywnie otwarte myślenie chroni przed uwierzeniem zarówno w twarde, jak i super fake newsy. Wartość poznawcza: w artykule przedstawiono medioznawczo-psychologiczną analizę postrzegania prawdziwości różnych grup fake newsów i dzięki temu wskazano różne sposoby projektowania działań edukacyjnych w tym obszarze.

Social Bubbles, Fake News, and Profits: Can Real News Survive?

We develop a game-theoretic model of strategic interaction between news providers and news consumers. News providers are profit maximizers while consumers maximize utility. News providers can provide true or fake news. Providing real news is costlier than providing fake news. Consumers can be biased, i.e., live in a social bubble, or unbiased. Real News that does not fit their world view is costly for biased consumers, while fake news imposes a negative externality on unbiased consumers because they have to spend time fact-checking. Heterogeneity in costs for both news purveyors and consumers drives Nash equilibria in static and dynamic game formulations. We conclude that market based rational choice systems can help understand the spread of fake news and biased people. Consequently, it is not enough to say that greedy profit-maximizing media companies drive fake news. Consumers drive the choices made by media companies, while media companies drive the consumer choice to be biased. Fetters on a free press may exacerbate the increase of biased consumers. Last, there may be a trade-off between two equally important democratic goals: the right information to make good decisions and freedom of thought.