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>

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.

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 Moroccan Social Media Platforms and COVID-19 Misinformation

2021 ◽  
Vol 2 (4) ◽  
pp. 123-134
Author(s):  
Hanane Aboulghazi
Keyword(s):  
Social Media ◽  
Exploratory Research ◽  
Fake News ◽  
Structured Interviews ◽  
Health Crisis ◽  
Main Research ◽  
Mainstream Media ◽  
Social Media Platforms ◽  
The Government ◽  
Crisis Situations

COVID-19 pandemic has been accompanied by a massive ‘infodemic’ and an over-abundance of disinformation that makes it hard for people to find trustworthy sources and reliable guidance when they need it. Young Moroccan internet users resort to social media for their news, and easily fall prey to the misinformation and fake news they encounter online. When it concerns public health, disinformation can turn into a lethal weapon. This is further exacerbated at the time of COVID-19 pandemic. To tackle this, the present research paper answers the research questions using a qualitative method, particularly semi-structured interviews preferable  in exploratory  research where the purpose is to gain an understanding of spreading online misinformation in the age of COVID-19. Semi-structured Interviews are conducted via “Google Meet” and “Zoom” using video-conferencing among 12 young Moroccan social media activists and professionals. The main research findings have shown that young Moroccan social media users have been consuming fake news about the Coronavirus, which has been especially prevalent on the most popular platforms, Facebook, Whats App and YouTube. Other results have shown that the mainstream media failed to debunk misinformation by subjecting them to rigorous fact checking experiments, lack of Media Information Literacy research in the form of crisis audits and crisis planning, Moroccan social media are ill prepared for crisis manual and conducting crisis training. These ensure that media regulators are not better equipped to handle any  misinformation in health crisis situations. Therefore, media literacy is not only about how to use the computer and do an internet search, it also involves helping young Moroccan people to deal with disinformation in crisis situations, and realize that anyone anywhere can put up a very official-looking websites. These websites masquerade as high-credibility sources that have been spreading misinformation about COVID-19. Therefore, the government needs.

scholarly journals Level of Acceptance of News Stories on Social Media Platforms Among Youth in Nigeria

2021 ◽  
Vol 37 (2) ◽  
pp. 210-225
Author(s):  
Festus Prosper Olise ◽  
Keyword(s):  
Social Media ◽  
Sampling Technique ◽  
Fake News ◽  
Mainstream Media ◽  
News Stories ◽  
Multi Stage ◽  
States Data ◽  
Social Media Platforms ◽  
Media Platform ◽  
And Gender

This study investigates the level of acceptance of news stories on social media platforms among youth in Nigeria following the assumption that the proliferation of news stories on social media promotes the circulation of both factual and fake news. The sample consisted of 600 youth; however, 583 validly participated in the study. The participants were equitably selected in six States in Nigeria that represented the six geo-political zones of the country. The multi-stage sampling technique was employed to evenly select the youth from the major cities/towns in the States. Data generated were analysed and presented through descriptive and inferential statistics using SPSS version 20 software. Results show that the youth accepted entertainment news stories more than any other type of news stories on social media platforms. The majority of the youth considered Twitter as the most acceptable social media platform for receiving news stories in Nigeria. Findings also revealed that despite their love to read news stories on social media platforms, the youth’s level of acceptance of it was low. Furthermore, the age and gender of the youth were found to directly influence their level of acceptance of news stories on social media platforms. The study concluded that the multi-dimensional inter-play that characterised the low level of acceptance of news stories on social media platforms among the youth in Nigeria does not portend ominous signs. Keywords: Acceptance, mainstream media, news stories, social media platforms, youth.

scholarly journals COVID-19-Related Social Media Fake News in India

2021 ◽  
Vol 2 (1) ◽  
pp. 100-114 ◽  
Author(s):  
Md. Sayeed Al-Zaman
Keyword(s):  
Public Health ◽  
Social Media ◽  
Fake News ◽  
Future Health ◽  
Health Crisis ◽  
Healthcare Facilities ◽  
Mainstream Media ◽  
Health Related ◽  
Social Media Platforms ◽  
Audio Video

COVID-19-related online fake news poses a threat to Indian public health. In response, this study seeks to understand the five important features of COVID-19-related social media fake news by analyzing 125 Indian fake news. The analysis produces five major findings based on five research questions. First, the seven themes of fake news are health, religiopolitical, political, crime, entertainment, religious, and miscellaneous. Health-related fake news (67.2%) is on the top of the list that includes medicine, medical and healthcare facilities, viral infection, and doctor-patient issues. Second, the seven types of fake news contents are text, photo, audio, video, text and photo, text and video, and text and photo and video. More fake news takes the form of text and video (47.2%). Third, online media produces more fake news (94.4%) than mainstream media (5.6%). More interestingly, four social media platforms: Twitter, Facebook, WhatsApp, and YouTube, produce most of the fake news. Fourth, relatively more fake news has international connections (54.4%) as the COVID-19 pandemic is a global phenomenon. Fifth, most of the COVID-19-related fake news is negative (63.2%) which could be a real threat to public health. These results may contribute to the academic understanding of social media fake news during the present and future health-crisis period. This paper concludes by stating some limitations regarding the data source and results, as well as provides a few suggestions for further research.

scholarly journals Legitimating falsehood in social media: A discourse analysis of political fake news

2020 ◽  
pp. 175048132096165
Author(s):  
Ebuka Elias Igwebuike ◽  
Lily Chimuanya
Keyword(s):  
Social Media ◽  
Discourse Analysis ◽  
Role Model ◽  
Discourse Markers ◽  
Ethnic Violence ◽  
Sampled Data ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms ◽  
General Elections

Digital peddling of fake news is influential to persuasive political participation, with veritable social media platforms. Social media, with their instantaneous and widespread usage, have been exploited by ‘anonymous’ political influencers who fabricate and inundate internet community with unverified and false information. Using van Leeuwen’s Discourse Legitimation approach and insights from Discourse Analysis, this study analyses 120 purposively sampled fake news posts on Whatsapp, Facebook and Twitter, shared during the 2019 general elections in Nigeria. WhatsApp allows for the easy and fast sharing of fake news as it pulled the largest occurrence of legitimation strategies, followed by Facebook. Authorisation is the highest occurring legitimation strategy at 46.6% frequency; this is followed by Moralisation which has 27% and Rationalisation at 26.4%; while Mythopoesis did not feature at all in the sampled data, leaving it at 0%. In particular, expert and role model authority are most often deployed to validate fake news such as the demise and cloning of President Buhari, ruling party’s plan to rig and destabilise the 2019 election, massive corruption in the current administration and imminent ethnic violence. The study argues that these strategies are viable persuasive tools owing to their use of discourse markers like make-believe images, emotive language, appeal to emotions, rational conclusions, hateful comments, verbal indictment and coercive verbs.

Governing Fake News: The Regulation of Social Media and the Right to Freedom of Expression in the Era of Emergency

2021 ◽  
pp. 1-41
Author(s):  
Donato VESE
Keyword(s):  
Social Media ◽  
Freedom Of Expression ◽  
Self Regulation ◽  
Public Law ◽  
Fake News ◽  
Human Right ◽  
Social Media Platforms ◽  
Definition Of ◽  
The Right ◽  
Media Censorship

Governments around the world are strictly regulating information on social media in the interests of addressing fake news. There is, however, a risk that the uncontrolled spread of information could increase the adverse effects of the COVID-19 health emergency through the influence of false and misleading news. Yet governments may well use health emergency regulation as a pretext for implementing draconian restrictions on the right to freedom of expression, as well as increasing social media censorship (ie chilling effects). This article seeks to challenge the stringent legislative and administrative measures governments have recently put in place in order to analyse their negative implications for the right to freedom of expression and to suggest different regulatory approaches in the context of public law. These controversial government policies are discussed in order to clarify why freedom of expression cannot be allowed to be jeopardised in the process of trying to manage fake news. Firstly, an analysis of the legal definition of fake news in academia is presented in order to establish the essential characteristics of the phenomenon (Section II). Secondly, the legislative and administrative measures implemented by governments at both international (Section III) and European Union (EU) levels (Section IV) are assessed, showing how they may undermine a core human right by curtailing freedom of expression. Then, starting from the premise of social media as a “watchdog” of democracy and moving on to the contention that fake news is a phenomenon of “mature” democracy, the article argues that public law already protects freedom of expression and ensures its effectiveness at the international and EU levels through some fundamental rules (Section V). There follows a discussion of the key regulatory approaches, and, as alternatives to government intervention, self-regulation and especially empowering users are proposed as strategies to effectively manage fake news by mitigating the risks of undue interference by regulators in the right to freedom of expression (Section VI). The article concludes by offering some remarks on the proposed solution and in particular by recommending the implementation of reliability ratings on social media platforms (Section VII).

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 Prevalence and source analysis of COVID-19 misinformation of 138 countries

2021 ◽  
Author(s):  
Md. Sayeed Al-Zaman
Keyword(s):  
Social Media ◽  
Positive Association ◽  
The Philippines ◽  
Source Analysis ◽  
Mainstream Media ◽  
Content Analysis Method ◽  
The Us ◽  
Political Conservatism ◽  
Social Media Platforms ◽  
Internet Literacy

This study analyzed 9,657 pieces of misinformation that originated in 138 countries and fact-checked by 94 organizations. Collected from Poynter Institute's official website and following a quantitative content analysis method along with descriptive statistical analysis, this research produces some novel insights regarding COVID-19 misinformation. The findings show that India (15.94%), the US (9.74%), Brazil (8.57%), and Spain (8.03%) are the four most misinformation-affected countries. Based on the results, it is presumed that the prevalence of COVID-19 misinformation can have a positive association with the COVID-19 situation. Social media (84.94%) produces the highest amount of misinformation, and the internet (90.5%) as a whole is responsible for most of the COVID-19 misinformation. Moreover, Facebook alone produces 66.87% misinformation among all social media platforms. Of all countries, India (18.07%) produced the highest amount of social media misinformation, perhaps thanks to the country's higher internet penetration rate, increasing social media consumption, and users' lack of internet literacy. On the other hand, countries like Turkey, the US, Brazil, and the Philippines where either political control over media is intense or political conservatism is apparent, experienced a higher amount of misinformation from mainstream media, political figures, and celebrities. Although the prevalence of misinformation was the highest in March 2020, given the present trends, it may likely to increase slightly in 2021.

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