scholarly journals A family of falsehoods: Deception, media hoaxes and fake news

2018 ◽  
Vol 39 (3) ◽  
pp. 350-361 ◽  
Author(s):  
Teri Finneman ◽  
Ryan J. Thomas
Keyword(s):  
Social Media ◽  
Fake News ◽  
Public Officials ◽  
False Information ◽  
Media Environment ◽  
News Organizations ◽  
Information Spread ◽  
History Of

“Fake news” became a concern for journalists in 2017 as news organizations sought to differentiate themselves from false information spread via social media, websites and public officials. This essay examines the history of media hoaxing and fake news to help provide context for the current U.S. media environment. In addition, definitions of the concepts are proposed to provide clarity for researchers and journalists trying to explain these phenomena.

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.

What the fake? Assessing the extent of networked political spamming and bots in the propagation of #fakenews on Twitter

2019 ◽  
Vol 43 (1) ◽  
pp. 53-71 ◽  
Author(s):  
Ahmed Al-Rawi ◽  
Jacob Groshek ◽  
Li Zhang
Keyword(s):  
Social Media ◽  
Data Sets ◽  
Fake News ◽  
Content Type ◽  
Mainstream Media ◽  
News Discourse ◽  
Twitter Data ◽  
News Organizations ◽  
Twitter Users ◽  
One Year

PurposeThe purpose of this paper is to examine one of the largest data sets on the hashtag use of #fakenews that comprises over 14m tweets sent by more than 2.4m users.Design/methodology/approachTweets referencing the hashtag (#fakenews) were collected for a period of over one year from January 3 to May 7 of 2018. Bot detection tools were employed, and the most retweeted posts, most mentions and most hashtags as well as the top 50 most active users in terms of the frequency of their tweets were analyzed.FindingsThe majority of the top 50 Twitter users are more likely to be automated bots, while certain users’ posts like that are sent by President Donald Trump dominate the most retweeted posts that always associate mainstream media with fake news. The most used words and hashtags show that major news organizations are frequently referenced with a focus on CNN that is often mentioned in negative ways.Research limitations/implicationsThe research study is limited to the examination of Twitter data, while ethnographic methods like interviews or surveys are further needed to complement these findings. Though the data reported here do not prove direct effects, the implications of the research provide a vital framework for assessing and diagnosing the networked spammers and main actors that have been pivotal in shaping discourses around fake news on social media. These discourses, which are sometimes assisted by bots, can create a potential influence on audiences and their trust in mainstream media and understanding of what fake news is.Originality/valueThis paper offers results on one of the first empirical research studies on the propagation of fake news discourse on social media by shedding light on the most active Twitter users who discuss and mention the term “#fakenews” in connection to other news organizations, parties and related figures.

scholarly journals Fake and Real News detection Using Python

2020 ◽  
pp. 423-428
Author(s):  
Fakhra Akhtar ◽  
Faizan Ahmed Khan
Keyword(s):  
Social Media ◽  
Fake News ◽  
False Information ◽  
False Negatives ◽  
Mainstream Media ◽  
Social Media Platforms ◽  
The Masses ◽  
Negative Attributes ◽  
Entire Dataset ◽  
F Measure

<p>In the digital age, fake news has become a well-known phenomenon. The spread of false evidence is often used to confuse mainstream media and political opponents, and can lead to social media wars, hatred arguments and debates.Fake news is blurring the distinction between real and false information, and is often spread on social media resulting in negative views and opinions. Earlier Research describe the fact that false propaganda is used to create false stories on mainstream media in order to cause a revolt and tension among the masses The digital rights foundation DRF report, which builds on the experiences of 152 journalists and activists in Pakistan, presents that more than 88 % of the participants find social media platforms as the worst source for information, with Facebook being the absolute worst. The dataset used in this paper relates to Real and fake news detection. The objective of this paper is to determine the Accuracy , precision , of the entire dataset .The results are visualized in the form of graphs and the analysis was done using python. The results showed the fact that the dataset holds 95% of the accuracy. The number of actual predicted cases were 296. Results of this paper reveals that The accuracy of the model dataset is 95.26 % the precision results 95.79 % whereas recall and F-Measure shows 94.56% and 95.17% accuracy respectively.Whereas in predicted models there are 296 positive attributes , 308 negative attributes 17 false positives and 13 false negatives. This research recommends that authenticity of news should be analysed first instead of drafting an opinion, sharing fake news or false information is considered unethical journalists and news consumers both should act responsibly while sharing any news.</p>

Fake Online News

2019 ◽  
pp. 152-166
Author(s):  
Janet Aver Adikpo
Keyword(s):  
Online News ◽  
Primary Reason ◽  
Fake News ◽  
Media Environment ◽  
Influence Model ◽  
Production And Consumption ◽  
Information Spread ◽  
The Media ◽  
Hierarchy Of Influence ◽  
Model Trajectory

Today, the media environment has traversed several phases of technological advancements and as a result, there is a shift in the production and consumption of news. This chapter conceived fake news within the milieu of influencing information spread in the society, especially on the cyberspace. Using the hierarchy of influence model trajectory with fake news, it was established that it has become almost impossible to sustain trust and credibility through individual influences on online news content. The primary reason is that journalists are constrained by professional ethics, organizational routines, and ownership influence. Rather than verify facts and offer supporting claims, online users without professional orientation engage in a reproducing information indiscreetly. The chapter recommends that ethics be reconsidered as a means to recreate and imbibe journalistic values that will contend with the fake news pandemic.

Canards, Fausses Nouvelles, Paranoid Style

2019 ◽  
pp. 119-132
Author(s):  
Peppino Ortoleva
Keyword(s):  
19Th Century ◽  
Honore De Balzac ◽  
Fake News ◽  
Conspiracy Theories ◽  
Media Environment ◽  
Gerard De Nerval ◽  
History Of ◽  
History Of Journalism ◽  
Paranoid Style ◽  
The Great War

Fake news has been cyclically surfacing in the history of journalism and public opinion. In the vein of some classic authors, the chapter identifies ideas that are surprisingly useful in the present media environment. It interweaves three historical threads relevant to today’s fake news: (1) the growth of canards in 19th-century Paris, observed by both Honoré de Balzac and Gérard de Nerval as the habitual invention of news when facts were not sufficiently attractive for readers; (2) the diffusion of fausses nouvelles during the Great War, described by Marc Bloch and propelled by the tendency, in times of crisis, to search for oracles more than information proper; (3) the propensity, suggested by Richard Hofstadter, to spread conspiracy theories, notably in the development of McCarthyism.

Exploring a Methodological Model for Social Media Gatekeeping on Contentious Topics

2020 ◽  
pp. 97-111
Author(s):  
Jacob Groshek
Keyword(s):  
Social Media ◽  
Media Coverage ◽  
Genetically Modified Organisms ◽  
Media Environment ◽  
Virtual Absence ◽  
Broadcast Networks ◽  
News Organizations ◽  
News Networks ◽  
Technological Affordances ◽  
Flow Of Information

The notion of news networks has changed from primarily one of print and broadcast networks to one of social networks and social media. This study examines the intersection of technological affordances, dialogic activity, and where traditional news gatekeepers are now situated in the contemporary multigated and networked media environment. Using genetically modified organisms (GMOs) as a topical issue, social data was collected from Twitter. The most connected (and connecting) users were algorithmically identified and then sorted into ‘community' groups. The resultant graphs visually and statistically identify which users were important gatekeepers and how the flow of information on this topic was being structured around and by certain users that acted as ‘hubs' of communication in the network. Results suggest that the ongoing evolution of networked gatekeeping has led to the virtual absence of journalists and news organizations from prominence in social media coverage on certain topics, in this instance GMOs. Normative implications are discussed.

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.

Bad News

2021 ◽  
pp. 240-260
Author(s):  
Debasish Roy Chowdhury ◽  
John Keane
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Communication Networks ◽  
Bad News ◽  
Fake News ◽  
Media Environment ◽  
Media Frames ◽  
Mainstream Media ◽  
Bharatiya Janata Party ◽  
Media Communications

This chapter examines Indian media. Communications scholars have long argued that media sets the agenda for public opinion, first by drawing the attention of citizens to a particular issue, and then by defining it by means of comprehensible media ‘frames’ that act as cognitive shortcuts to understand issues. As in other so-called democracies, journalists working within India’s mainstream media are engaged 24/7 in framing narratives, making them indispensable for any government. Anti-Muslim messaging, generally subtle, has been the default media frame ever since the Hindu nationalist Bharatiya Janata Party (BJP) came to power in 2014. This coincided with the coming of communicative abundance, the profusion of new communication networks and technologies, and rapidly changing media consumption habits. Secretive organizations frame sophisticated misinformation campaigns to spread fake news and false claims through social media. In such a media environment marked by features common to despotisms like Vietnam, Iran, and Russia, where independent journalism is all but dead, self-censorship and toad-eating are rife.

Fake News Detection

2020 ◽  
Vol 11 (12) ◽  
pp. 575-580
Author(s):  
Aniket Kumar ◽  
Saurabh Kumar Pal ◽  
Kumar Dhruv Roy ◽  
Mr. Ragunthar T
Keyword(s):  
Social Media ◽  
Fake News ◽  
False Information ◽  
Digital World ◽  
Use Of Social Media

Now-a-days it's exceedingly common in this digital world that someone for his or her benefit try to manipulate a mass with false information. With the massive use of social media by the population which is beneficial for the users most of the time, can also be used as a really good platform to spread a fake news and at worse try to create chaos in society. Fake death news of celebrities, fake news regarding wars and fake news related to politics are the day-to-day life examples.

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.

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