Major News Sources Research Articles

Social network size and endorsement of political violence in the US

BackgroundIn recent years, the United States (US) has witnessed a rise in political violence. Prior research has found that an individual’s social network is associated with their likelihood of engaging in various forms of violence, but research on social networks and political violence in the US context is limited. This study examined associations between social network size and endorsement of political violence in a recent nationally representative survey and explored how the relationship varied by use of social media as a major news source, perceptions of the government as an enemy, and membership in a marginalized or privileged racial or ethnic group.MethodsThis was a nationally representative cross-sectional survey study of adults aged 18 and older in the US, administered from May 13-June 2, 2022. The exposure was social network size, defined by the number of strong social connections. We examined three violence-related outcomes: support for non-political violence, support for political violence, and personal willingness to engage in political violence. We estimated prevalence ratios for associations using survey-weighted Poisson regression with robust standard errors, adjusting for hypothesized confounders and including interaction terms to examine effect measure modification.ResultsThe sample included 8,620 respondents. Median age was 48.4 years (95% CI = 47.9–48.8), 51.5% were female (95% CI = 50.4–52.7%), and 62.7% self-identified as non-Hispanic White (95% CI = 61.4–65.9%). In adjusted models, those with zero strong connections were more likely than those with 1–4 strong social connections to consider political violence usually/always justified in general (PR = 2.43, 95% CI = 1.47–4.01). Those with 50 + strong connections were more likely than those with 1–4 strong social connections to consider political violence usually/always justified in at least one situation (PR = 1.19, 95% CI = 1.03–1.37) and were more likely to report being willing to personally use political violence (PR = 1.52, 95% CI = 1.13–2.04). Associations varied somewhat by social media use, perceptions of the government as an enemy, and racialized identity.ConclusionsIndividuals who reported very few and very many strong social connections were more likely than others to support political violence or be personally willing to engage in it in one form or another. Findings point toward potential intervention and prevention opportunities.

Traditional News Media as Agents of Authenticity: Nigerian Audiences Weathering the Onslaught of New Media Streaming

The Nigerian media have, over the years, moved back and forth via all manners of experience of leadership, from military to democratic, until they finally settled into the present democratic governance. Experience from previous regimes exposed them to the vagaries of power and how to cope with them. These traditional news media, newspapers/magazines, radio, and television, were then the major and authentic news sources for Nigerians. The coming of the internet with its social media handles threw the journalists of these media into confusion as the citizens have taken the reigns of news/information peddling from under them, turning them into agents of confirmation and authenticity of information. Ironically, these traditional media, television, radio, and print, have adjusted to online media streaming, thus liberating themselves from the vagaries of temporal and spatial limitedness. Deploying a survey method (opinion poll, OPL) and relying on a purposive sampling technique, the authors purposively selected WhatsApp group platforms (WGP) as the most suitable of the new/social media with access to all manners of news sources. These were used to interrogate how online media/information/news streaming has taken the audience away from traditional media by being on the ‘spot’ at all hours with their avalanche of “Breaking News”. The findings revealed that audiences currently resort to traditional media to confirm the authenticity of news and information carried online in a sort-of-when-in-doubt-watch-the television manner or listen to radio or read the newspapers/magazines. It concludes that online news streaming has become the coveted bride of present-day information and news seekers, albeit an unreliable one.

Public perceptions of justification in the deadly police shootings of black men: a content analysis of social media posts in 2016 and 2020

The proliferation of information technologies has revolutionized how people around the world consume news. By 2021, an estimated 8.6 billion mobile subscriptions existed worldwide. In the United States, a majority (84%) of Americans access their news digitally. On social media platforms, Facebook is the most used network for obtaining news. The three major news sources in the United States, CNN, Fox News, and MSNC, have a strong presence on Facebook. The current study analyzes public comments posted on Facebook involving the deadly police shootings of four Black men (i.e., Alton Sterling, Philando Castile, Jonathan Price, Rayshard Brooks) to identify reactions at two points in time. Our work centers on public comments to these posts, not the posts themselves. Using a summative qualitative analysis, we analyzed the first 20 Facebook comments from CNN, Fox News, and MSNBC that are listed as most relevant (e.g., highest number of likes, reactions, replies) to the most recently posted news story regarding the incident. This resulted in a sample size of 223 posts. Public comments were categorized as justifying the killing, perceiving the killing as not justified, or expressing neutrality as to whether the killing was justified or not. We found that most comments, regardless of time period, did not engage in victim-blaming. Overall, the majority (N = 139 or 62%) of all comments perceived these deadly police shootings as not justified. Most (54%) posts on Fox News justified this violence, while the majority of posts on CNN or MSNBC felt the police had used excessive violence. There were more negative comments of Black men and victim-blaming after the Trump presidency (in 2020) than in 2016 (before Trump took office), which may be related to the racial rhetoric of the times.

An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection

Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology’s reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND.Graphical

FAKE NEWS DETECTOR USING DEEP LEARNING

In recent years, there has been an increase in worry about the presence of false news on the internet. Real and false news have becoming harder to identify as social media and other online platforms have rapidly replaced print media as many peoples major news sources. Techniques for natural language processing (NLP) have been developed to help identify and categorize bogus news in attempt to solve this problem. Examining the language used in news items is one method of spotting false information. Large datasets of news stories may be used to train NLP models to find linguistic patterns that are suggestive of false news. For instance, false news stories may employ dramatic or exaggerated language, or they may not cite reliable sources to back up their assertions. NLP models may be trained to recognize these patterns and spot news stories that are probably fraudulent by utilizing machine learning methods. One other method for spotting false news is to examine the social environment in which news pieces are circulated. The sharing and consumption of news stories by users is extensively documented by social media companies. NLP algorithms can spot stories that are probably false by looking for trends in social media activity. A sign that a news story is phony can be, for instance, if it is being shared quickly by a lot of bot accounts. Having access to good training data is crucial for developing an NLP-based false news detector. Real and false news stories should also be included in this data, along with information on how the pieces were shared and read on social media. Researchers may create algorithms that can precisely distinguish between real and false news stories by training NLP models on this data. Overall, the development of NLP algorithms for false news identification is a crucial milestone in the fight against the spread of untruth on the internet. We can create tools that assist individuals identify between true and false news and, in turn, foster a more informed and involved society by utilizing the power of machine learning and natural language processing.

Public Perception of Political Opinion Polls and Their Influence on People’s Voting Behavior: The Case of Mwanza City Residents

This study focuses on the influence of public perceptions of political opinion polls on people’s voting behavior in Mwanza city and the role of Tanzanian media in creating awareness about political opinion polls. The study uses a descriptive paradigm and a qualitative case study design. A purposive sampling technique was used to select participants for key informants and focus group discussions. The study concludes that, although polling is a new phenomenon in Tanzania, the level of awareness of polls is high among Mwanza City residents and they hold a variety of positions regarding the accuracy of the polls. The study highlights why journalists need to be trained on how to report poll-related stories to avoid false conclusions because they have become a major source of news.

Is Snapchat Discover really a news platform? News snacking from social media and users’ knowledge of current affairs

The role of the media in informing the public has long been a central topic in journalism studies. Given that social media platforms have become today’s major source of news, it is important to understand the impact of social media use on citizens’ knowledge of current affairs. While people get news from multiple platforms throughout the day, most research treats social media as a single entity or examines only one or two major platforms ignoring newer social media platforms. Drawing on news snacking framework, this study investigates how using some of today’s most popular social media platforms predicts users’ current affairs knowledge, with particular attention to Snapchat and its news section Discover. A survey conducted in the United States (N=417) demonstrated that each of the platforms is distinct: Twitter is a strongly positive predictor of knowledge, Facebook a marginally significant negative predictor, Reddit a significantly negative predictor and Instagram not a significant predictor. Overall Snapchat use has no significant association with users’ knowledge of current affairs, whereas Discover use has a negative relationship. Further analysis revealed that mere exposure to Snapchat is positively related to soft-news knowledge and attention to Discover is negatively related to hard-news knowledge.

The Gulf War as Simulacra: An Analysis of Vishwa Ghatana [World Affairs] on Nepal Television

Between early August 1990 and the end of February 1991, the coverage of the Persian Gulf War on Cable News Network (CNN) television showcases a comprehensive example of simulacra. Based on Jean Baudrillard's ideas of postmodern simulacra and simulation, this paper attempts to demonstrate how the simulacra of the Gulf War appeared before the Nepali audience through a domestic nationwide channel. The major source of news and perspectives of the contents of Nepal Television's world affairs program were the reports and images of CNN. The paper, with an approach to looking into the event retrospectively, demonstrates a case where CNN used a comparatively small television channel in a developing country to disseminate its cultural product to the local audience in their vernacular language. Taking Nepal Television (NTV) and its Vishwa Ghatana [World Affairs], a program dedicated to the world affairs as a case in point, this paper examines relationships between the rhetoric and reality in the theoretical and conceptual frame of simulacra and simulation based on Baudrillard’s ideas.

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

Achieving herd immunity through vaccination depends upon the public's acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative). Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19.

How Climate Movement Actors and News Media Frame Climate Change and Strike: Evidence from Analyzing Twitter and News Media Discourse from 2018 to 2021

Twitter enables an online public sphere for social movement actors, news organizations, and others to frame climate change and the climate movement. In this paper, we analyze five million English tweets posted from 2018 to 2021 demonstrating how peaks in Twitter activity relate to key events and how the framing of the climate strike discourse has evolved over the past three years. We also collected over 30,000 news articles from major news sources in English-speaking countries (Australia, Canada, United States, United Kingdom) to demonstrate how climate movement actors and media differ in their framing of this issue, attention to policy solutions, attribution of blame, and efforts to mobilize citizens to act on this issue. News outlets tend to report on global politicians’ (in)action toward climate policy, the consequences of climate change, and industry's response to the climate crisis. Differently, climate movement actors on Twitter advocate for political actions and policy changes as well as addressing the social justice issues surrounding climate change. We also revealed that conversations around the climate movement on Twitter are highly politicized, with a substantial number of tweets targeting politicians, partisans, and country actors. These findings contribute to our understanding of how people use social media to frame political issues and collective action, in comparison to the traditional mainstream news outlets.

The First Baby Born After Polygenic Embryo Screening

The First Baby Born After Polygenic Embryo Screening

Mc-DNN

With the advancement of technology, social media has become a major source of digital news due to its global exposure. This has led to an increase in spreading fake news and misinformation online. Humans cannot differentiate fake news from real news because they can be easily influenced. A lot of research work has been conducted for detecting fake news using Artificial Intelligence and Machine Learning. A large number of deep learning models and their architectural variants have been investigated and many websites are utilizing these models directly or indirectly to detect fake news. However, state-of-the-arts demonstrate the limited accuracy in distinguishing fake news from the original news. We propose a multi-channel deep learning model namely Mc-DNN, leveraging and processing the news headlines and news articles along different channels for differentiating fake or real news. We achieve the highest accuracy of 99.23% on ISOT Fake News Dataset and 94.68% on Fake News Data for Mc-DNN. Thus, we highly recommend the use of Mc-DNN for fake news detection.

Narratives and counter-narratives in religious responses to COVID-19: A computational text analysis.

Religious responses to COVID-19 as portrayed in a major news source raise the issue of conflict or cooperation between religious bodies and public health authorities. We compared articles in the New York Times relating to religion and COVID-19 with the COVID-19 statements posted on 63 faith-based organizations' web sites, and with the guidance documents published by the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) specifically for religious bodies. We used computational text analysis to identify and compare sentiments and topics in the three bodies of text. Sentiment analysis showed consistent positive values for faith-based organizations' texts throughout the period. The initial negative sentiment of religion-COVID-19 coverage in the New York Times rose over the period and eventually converged with the consistently positive sentiment of faith-based documents. In our topic modelling analysis, rank order and regression analysis showed that topic prevalence was similar in the faith-based and public health sources, and both showed statistically significant differences from the New York Times. We conclude that there is evidence of both narratives and counter-narratives, and that these showed demonstrable shifts over time. Text analysis of public documents shows alignment of the interests of public health and religious bodies, which can be discerned for the benefit of communities if parties are trusted and religious messages are consistent with public health communications.

Public health framing in local media coverage of crashes involving pedestrians or bicyclists in Hawai‘i, 2019: A content analysis

Media coverage of traffic crashes involving people walking and bicycling tends to frame crashes as isolated events rather than as a public health problem that disproportionately affects vulnerable populations. This study assessed public health framing in local media coverage of traffic crashes involving pedestrians and bicyclists in Hawai‘i. We systematically searched the websites of all major local news sources (newspapers and local television stations) to identify articles about Hawai‘i traffic crashes involving pedestrians/bicyclists that were published in 2019. Using content analysis, we extracted information on title and article attributes, crash details, language patterns, and public health framing elements. After screening 738 news articles for inclusion, 162 articles comprised the final sample. These articles reported on 164 crash events, representing 57 unique crash events. Most articles described the crashes factually as isolated events. Language patterns in article titles were non-agentive (77%) and focused on the pedestrian or bicyclist (77%) without mentioning the driver or vehicle (69%). When articles ascribed agents, vehicles (53%) were mentioned more often than drivers (13%). One-quarter of articles contained a counterfactual that subtly assigned responsibility for the crash to the person walking or bicycling. About 15% of articles described a traffic safety solution. These findings serve as a benchmark for local media coverage and can be used to inform state-level actions to improve local media reporting. Media coverage that considers the contextual and systematic factors contributing to the crash could better support local government policy change to protect pedestrians and bicyclists.

Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource.

BackgroundBefore the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place.ObjectiveHere we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies.MethodsWe collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19–related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations.ResultsWe found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis.ConclusionsThe goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.

Role of media surveillance function during COVID-19 breakout

IntroductionIt is a digital era and people always turn on television to gain knowledge of what’s happening around, same is the case with COVID-19 breakout. Whole world relied on media to understand the scenario, as media has always played substantial role in providing information regarding precaution and treatment of the disease.ObjectivesTo assess influence of media surveillance function on society during COVID-19 breakout.MethodsParticipants included in the study are 11 and above who can utilize abstract thinking as per Piaget’s theory of cognitive development. Questionnaire used in the study was Media surveillance questionnaire, it was previously used in the study done during Ebola Virus Disease (EVD) spread in Nigeria. In current study the questionnaire was used by replacing disease name i.e. EVD to COVID-19. Questionnaire was distributed online on social media groups.ResultsAccording to data analysis majority agrees that major source of COVID-19 news is television and Radio, messages on media help in avoiding sick people, media provides helpful information regarding disease prevention and control and messages on media needs more clear directions.ConclusionsMedia played positive role during COVID-19 breakout and it also spread awareness about the myths, false home remedies and precautionary measures. Media also informed society about the health emergencies in case if anyone is infected with the virus. Still media messages need more clarity and direction as people rely more on media than social media. It is media’s responsibility to provide accurate information and more clear messages after inquiring matter properly especially when it’s about human lifeDisclosureNo significant relationships.

Assessment of Nigerian Newspapers’ Reportage of Violence against Children: Case Study of Daily Sun and Punch National Newspapers

Traditionally, child rearing in Nigeria closely reflects the “spare the rod and spoil the child” maxim and as such spanking, flogging, slapping, beating and evening starving a child as a form of punishment for wrong doing and as a method of behavior modification are common. These are not necessarily considered as maltreatment or abuse of the child. Despite the adoption and implementation of the Child Rights Act in Nigeria, violence against children seems to be on a steady increase. Stories of sexual molestation, rape, child labour, infliction of physical injuries and use of children for rituals by parents, guardians and other members of the society abound. Violence against children is considered as those acts by other persons especially adults that undermine and threaten the healthy life and existence of children or those that violet their rights as humans. In Nigeria newspapers are a major source of News, second only to radio and television in coverage, currency and content. National dailies are newspapers with daily publications and national spread or coverage. This study analyzed the frequency, length, prominence level, direction and sources of information reported on violence against children in the selected national daily newspapers. It then provided information on the role of the newspapers in Nigeria in the fight against child violence and public awareness of the impact of violence against children on development of the nation and the attempts to curtail such violence. The composite week sampling technique in which the four weeks of the month are reduced to one and a sample is randomly selected from each day of the week was used. As such 168 editions of Daily Sun and Punch newspapers published from January to December of 2016 were selected. Data were collected using code sheet and analyzed via content analysis. The result showed that the frequency of the newspapers’ reportage of violence against children in Nigeria was low. Again, it was found that the length or space given to reports on violence against children was inadequate, the direction of the few reports on violence against children was in favor of the course or fight against child violence and these newspapers gave no prominence to reports on violence against children. Finally, it was found that major source of news about violence against children was through journalism; government and individual sources provided only minimal information.
 Adeline Nkwam-Uwaoma and Mishack Ndukwu
 
 Keywords – children, Newspapers Reportage, Nigeria, Violence
 
 Nkwam-Uwaoma is with the Department of Mass Communication , Imo State University, P.M.B. 200 Owerri , Nigeria (corresponding author, phone +2348035414973; email: nkwamuwaomaadline@yahoo.com )
 Ndukwu is with Imo State University, P.M.B. 2020 Owerri, Nigeria (email: mishack.cj@gmail.com ).
 
 
 

Integrating location and textual information for detecting affected people in a crisis

Social networks have become a major source of news with millions of people writing and trading news daily. At the occurrence of any event, news spread faster on social networks compared to other news sources, which makes them a good source for event identification. The most common approach for event detection from social networks is text analysis.In the case of a crisis, social media plays an important role in understanding the situation at the location of the crisis as the information is received directly from the affected people. If the collected information is used effectively this can minimize casualties and help in providing basic needs and medical attention. In this paper, we propose a hybrid approach that combines text analysis techniques and location identification to efficiently detect affected people. The addition of location information is done for the purpose of filtering out users writing about the crisis without being affected. The experimental results on Twitter data showed that the combination of text analysis and location yielded an accuracy of 96% as compared to 87% when using text analysis only.

Compound words on the Jakarta post newspaper and Việtnam news newspaper

A newspaper has become the major news source around the world, either the printed or online newspaper. A newspaper provides many kinds of news and from different perspectives. Words in the newspaper play a significant role to deliver the information to the readers. The morpho-semantic study is needed to find out the uncommon words and their meaning in the newspaper. News in The Jakarta Post newspaper and Việt Nam News newspaper were analyzed, applying Hamawand’s (2001) compound categorization. Endocentric compound and exocentric compound by O’Grady et al (2016) are also being used in this study. The data were taken in a purposive sampling technique by observing eight news in the business column by The Jakarta Post and nine news in the economy column by Việt Nam News, taken from August 28, 2020, until September 4, 2020. Both newspapers contain distinctive compound words related to the economy that is still rarely known by people. From the total seventeen news, only a noun compound and adjective compound are found in the news, while the verb compound does not appear at all. The total of compound words found in the news is 25; there are 18 noun compounds and 7 adjective compounds found in the news. There are 16 endocentric compounds and 9 exocentric compounds found. The result shows that noun compounds and endocentric compounds appear more often than adjective compounds and exocentric compounds.

WITHDRAWN: Performance of bernoulli’s naive bayes classifier in the detection of fake news

Abstract Social media has abolished the conventional news sources such as newspaper and TV news channels and became the major source of news. According to Pew Research Centre, which is an Internet research company, 62 percent of adults in the united states gather their news from social media [1] . Social media has become an open platform to spread any type of fake news or propaganda’s all over the world with a single click. Social media platforms like Facebook and Google are using preventive measures to tackle this fake news by using reporting and flagging tools. However, these measures are user-based and only users can report the news as fake or genuine. Machine learning and artificial intelligence may help to create certain powerful algorithms that can detect and remove fake news broadcasting itself. This paper explores a machine learning algorithm named Bernoulli’s Naive Bayes Classifier, which is the extended version of Multinomial Naive Bayes with predictors as Boolean variables i.e. 0 and 1 to detect fake news. Previous studies applied Gaussian Naive Bayes [2] . Our proposed methodology classifies the input data into two classes 0 or 1. ’0′ stands for Fake and ’1′ stands for Genuine news article. Further, it is observed that the results are enhanced as compared to Gaussian Naive Bayes. From the experiments, we observe that the classification results improve by the use of Bernoulli’s Naive Bayes Classifier as compared to Gaussian Naive Bayes. The comparison is done in terms of accuracy, precision, recall, and F1 measure. The accuracy is improved by 10%, precision by 15%, and F1 measure by 6%.