scholarly journals Fake News Detection System Using machine Learning

2021 ◽  
Vol 10 (6) ◽  
pp. 12-15
Keyword(s):  
Information Quality ◽  
Detection System ◽  
Fake News ◽  
Web Based ◽  
News Stories ◽  
News Sources ◽  
Quality Check ◽  
Media Channels ◽  
Highlight Extraction

Expansion of deluding data in ordinary access news sources, for example, web-based media channels, news web journals, and online papers have made it testing to distinguish reliable news sources, hence expanding the requirement for computational apparatusesready to give bits of knowledge into the unwavering quality of online substance. In this paper, every person center around the programmed ID of phony substance in the news stories. In the first place, all of us present a dataset for the undertaking of phony news identification. All and sundry depict the pre-preparing, highlight extraction, characterization and forecast measure in detail. We've utilized Logistic Regression language handling strategies to order counterfeit news. The prepreparing capacities play out certain tasks like tokenizing, stemming and exploratory information examination like reaction variable conveyance and information quality check (for example invalid or missing qualities). Straightforward pack of-words, n-grams, TF-IDF is utilized as highlight extraction strategies. Strategic relapse model is utilized as classifier for counterfeit news identification with likelihood of truth.

Fake News Detection Using Deep Learning

2021 ◽  
pp. 440-451
Author(s):  
Varalakshmi Konagala ◽  
Shahana Bano
Keyword(s):  
Deep Learning ◽  
Uncertain Data ◽  
Online News ◽  
Sufficient Information ◽  
Fake News ◽  
Web Based ◽  
News Sources ◽  
N Gram

The engendering of uncertain data in ordinary access news sources, for example, news sites, web-based life channels, and online papers, have made it trying to recognize capable news sources, along these lines expanding the requirement for computational instruments ready to give into the unwavering quality of online substance. For instance, counterfeit news outlets were observed to be bound to utilize language that is abstract and enthusiastic. At the point when specialists are chipping away at building up an AI-based apparatus for identifying counterfeit news, there wasn't sufficient information to prepare their calculations; they did the main balanced thing. In this chapter, two novel datasets for the undertaking of phony news locations, covering distinctive news areas, distinguishing proof of phony substance in online news has been considered. N-gram model will distinguish phony substance consequently with an emphasis on phony audits and phony news. This was pursued by a lot of learning analyses to fabricate precise phony news identifiers and showed correctness of up to 80%.

Fake News Detection Using Deep Learning

2020 ◽  
pp. 166-177
Author(s):  
Varalakshmi Konagala ◽  
Shahana Bano
Keyword(s):  
Deep Learning ◽  
Uncertain Data ◽  
Online News ◽  
Sufficient Information ◽  
Fake News ◽  
Web Based ◽  
News Sources ◽  
N Gram

The engendering of uncertain data in ordinary access news sources, for example, news sites, web-based life channels, and online papers, have made it trying to recognize capable news sources, along these lines expanding the requirement for computational instruments ready to give into the unwavering quality of online substance. For instance, counterfeit news outlets were observed to be bound to utilize language that is abstract and enthusiastic. At the point when specialists are chipping away at building up an AI-based apparatus for identifying counterfeit news, there wasn't sufficient information to prepare their calculations; they did the main balanced thing. In this chapter, two novel datasets for the undertaking of phony news locations, covering distinctive news areas, distinguishing proof of phony substance in online news has been considered. N-gram model will distinguish phony substance consequently with an emphasis on phony audits and phony news. This was pursued by a lot of learning analyses to fabricate precise phony news identifiers and showed correctness of up to 80%.

scholarly journals A Global Online Content Quality Check Solution

2020 ◽  
Author(s):  
Uyiosa Omoregie
Keyword(s):  
Information Quality ◽  
Online Content ◽  
Web Based ◽  
Intrinsic Nature ◽  
Content Quality ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Check System ◽  
Quality Check

A global online analytical quality check system (and method) for online content analysis is presented. Web-based information (articles, commentary etc.) is analysed then scored based on criteria designed to evaluate the quality of analytical content. Content is then categorised as ‘analytical’ or ‘non-analytical’. Further labelling of the intrinsic nature of the content (e.g. ‘satire’ ‘political’ ‘scientific’) and users’ (content consumers) ratings completes the process. Applied to Web browsers and online social media platforms: the rating produced by the quality check can help users discern quality content, avoid being misinformed and engage more analytically with other users. This system can also be viewed as a theory of information quality.

scholarly journals Reducing Fraudulent News Proliferation using Classification Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 3362-3365
Keyword(s):  
Natural Language ◽  
Fake News ◽  
Inverse Document Frequency ◽  
Classification Techniques ◽  
News Stories ◽  
News Sources ◽  
Document Frequency ◽  
Knowledge Set ◽  
Media Channels ◽  
Natural Language Process

The expansion of dishonorable information in normal get entry to social access media retailers like internet based media channels, news web journals, and online papers have made it hard to identify dependable news sources, subsequently growing the need for technique tools able to deliver insights into the reliability of online content substances.. This paper comes up with the applications of Natural language process techniques for detective work the dishonest news, that is, dishonorable news stories that return from the non-reputable sources. Solely by building a model supported mistreatment word tallies or a Term Frequency-Inverse Document Frequency matrix, will solely get you to date. Is it potential for you to make a model which will differentiate between “Real “news and “Fake” news? Thus our planned work is going to be on grouping a knowledge set of each pretend and real news and uses a Naïve mathematician classifier so as to make a model to classify an editorial into pretend or really supported its words and phrases.

scholarly journals Automatic Identification of Information Quality Metrics in Health News Stories

2020 ◽  
Vol 8 ◽  
Author(s):  
Majed Al-Jefri ◽  
Roger Evans ◽  
Joon Lee ◽  
Pietro Ghezzi
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Quality ◽  
Evaluation Process ◽  
Health News ◽  
News Stories

Objective: Many online and printed media publish health news of questionable trustworthiness and it may be difficult for laypersons to determine the information quality of such articles. The purpose of this work was to propose a methodology for the automatic assessment of the quality of health-related news stories using natural language processing and machine learning.Materials and Methods: We used a database from the website HealthNewsReview.org that aims to improve the public dialogue about health care. HealthNewsReview.org developed a set of criteria to critically analyze health care interventions' claims. In this work, we attempt to automate the evaluation process by identifying the indicators of those criteria using natural language processing-based machine learning on a corpus of more than 1,300 news stories. We explored features ranging from simple n-grams to more advanced linguistic features and optimized the feature selection for each task. Additionally, we experimented with the use of pre-trained natural language model BERT.Results: For some criteria, such as mention of costs, benefits, harms, and “disease-mongering,” the evaluation results were promising with an F1 measure reaching 81.94%, while for others the results were less satisfactory due to the dataset size, the need of external knowledge, or the subjectivity in the evaluation process.Conclusion: These used criteria are more challenging than those addressed by previous work, and our aim was to investigate how much more difficult the machine learning task was, and how and why it varied between criteria. For some criteria, the obtained results were promising; however, automated evaluation of the other criteria may not yet replace the manual evaluation process where human experts interpret text senses and make use of external knowledge in their assessment.

scholarly journals Information Re-Sharing on Social Network Sites in the Age of Fake News

10.28945/3871 ◽  
2017 ◽  
Vol 20 ◽  
pp. 215-235 ◽  
Author(s):  
Mehrdad Koohikamali ◽  
Anna Sidorova
Keyword(s):  
Social Media ◽  
Social Network ◽  
Information Quality ◽  
Social Network Sites ◽  
Research Model ◽  
Fake News ◽  
Quality Of Information ◽  
Sharing Behavior

Aim/Purpose: In the light of the recent attention to the role of social media in the dissemination of fake news, it is important to understand the relationship between the characteristics of the social media content and re-sharing behavior. This study seeks to examine individual level antecedents of information re-sharing behavior including individual beliefs about the quality of information available on social network sites (SNSs), attitude towards SNS use and risk perceptions and attitudes. Methodology: Testing the research model by data collected through surveys that were adminis-tered to test the research model. Data was collected from undergraduate students in a public university in the US. Contribution: This study contributes to theory in Information Systems by addressing the issue of information quality in the context of information re-sharing on social media. This study has important practical implications for SNS users and providers alike. Ensuring that information available on SNS is of high quality is critical to maintaining a healthy user base. Findings: Results indicate that attitude toward using SNSs and intention to re-share infor-mation on SNSs is influenced by perceived information quality (enjoyment, rele-vance, and reliability). Also, risk-taking propensity and enjoyment influence the intention to re-share information on SNSs in a positive direction. Future Research: In the dynamic context of SNSs, the role played by quality of information is changing. Understanding changes in quality of information by conducting longitudinal studies and experiments and including the role of habits is necessary.

scholarly journals Building a Dataset for Detecting Fake News in Amharic Language

2021 ◽  
pp. 76-83
Author(s):  
Tewodros Tazeze ◽  
Raghavendra R
Keyword(s):  
Social Media ◽  
Information Exchange ◽  
Detection System ◽  
Fake News ◽  
News Sources ◽  
Vernacular Language ◽  
Semitic Language ◽  
Media Platform ◽  
Tremendous Challenge ◽  
High Range

The rapid growth and expansion of social media platform has filled the gap of information exchange in the day to day life. Apparently, social media is the main arena for disseminating manipulated information in a high range and exponential rate. The fabrication of twisted information is not limited to ones language, society and domain, this is particularly observed in the ongoing COVID-19 pandemic situation. The creation and propagation of fabricated news creates an urgent demand for automatically classification and detecting such distorted news articles. Manually detecting fake news is a laborious and tiresome task and the dearth of annotated fake news dataset to automate fake news detection system is still a tremendous challenge for low-resourced Amharic language (after Arabic, the second largely spoken Semitic language group). In this study, Amharic fake news dataset are crafted from verified news sources and various social media pages and six different machine learning classifiers Naïve bays, SVM, Logistic Regression, SGD, Random Forest and Passive aggressive Classifier model are built. The experimental results show that Naïve bays and Passive Aggressive Classifier surpass the remaining models with accuracy above 96% and F1- score of 99%. The study has a significant contribution to turn down the rate of disinformation in vernacular language.

scholarly journals COMBATING INFODEMIC COVID-19: GOVERNMENT RESPONSE AGAINST FAKE NEWS ON SOCIAL MEDIA

2021 ◽  
Vol 14 (2) ◽  
pp. 255
Author(s):  
Andi Luhur Prianto ◽  
Abdillah Abdillah ◽  
Syukri Firdaus ◽  
Muhammad Arifeen Yamad
Keyword(s):  
Social Media ◽  
Media Literacy ◽  
Phenomenological Approach ◽  
Public Trust ◽  
Fake News ◽  
Communication Performance ◽  
Government Response ◽  
The Social ◽  
Media Channels

The global commitment to fighting the pandemic is not only about medical and epidemiological work, but also about how information about the disease is disseminated. The threat of the Covid-19 infodemic is no less dangerous than the pandemic itself. The phenomenon of infodemic has distorted the work of science and reduced public trust in state authorities. This research has identified, mapped, and analyzed official government responses to fake news attacks on social media. This study uses an interpretive-phenomenological approach, related to the spread and belief of fake news about Covid-19 in Indonesia. Data analysis uses the Nvivo-12 Pro application, as an artificial intelligence tool to support data exploration from various sources. The results show that the quality of media literacy, public communication performance, and the effectiveness of government regulations have become part of the challenges in mitigating infodemic. The level of public trust in information from social media contributes to the decline in trust in fake news about Covid-19. Stimulation from the social media news that does not control the belief in myths and false information about Covid-19. Content creators who have produced, posted, and shared on social media channels that are less critical, have an impact on the infodemic situation. The solution is to increase media literacy education and the effectiveness of law enforcement in mitigating the infodemic in Indonesia.

scholarly journals Fake News and Rumour Detection on Social Media

2019 ◽  
Vol 8 (11S) ◽  
pp. 1172-1175
Keyword(s):  
Mass Scale ◽  
Fake News ◽  
Quality Of Data ◽  
Mediated Communication ◽  
Web Based ◽  
Online Journals ◽  
The People ◽  
Network Propagation ◽  
The Continuum

PC Mediated Communication (CMC) advances like, for example, online journals, Twitter, Reddit, Facebook and other web based life presently have such a large number of dynamic clients that they have turned into an ideal stage for news conveyance on a mass scale. Such a mass scale news conveyance framework accompanies a proviso of faulty veracity. Building up the unwavering quality of data online is a strenuous and an overwhelming test yet it is basically essential particularly amid the time-touchy circumstances, for example, genuine crises which can have destructive impact on people and society. 2016 US Presidential race is an encapsulation of the previously mentioned crisis. In a study , it was concluded that the public's engagement with phoney news through Facebook was higher than through standard sources. So as to battle the spread of malevolent and unplanned falsehood in online networking we built up a model to recognise counterfeit news. Counterfeit news recognition is a procedure of classifying news and estimating it on the continuum of veracity. Detection is done by classifying and clustering assertions made about the event followed by veracity assessment methods emerging from linguistic cue, characteristics of the people involved and network propagation dynamics..

scholarly journals Cancer In The News: Bias And Quality In Media Reporting Of Cancer Research

2018 ◽  
Author(s):  
Amanda Amberg ◽  
Darren N. Saunders
Keyword(s):  
Cancer Research ◽  
Reporting Quality ◽  
Online News ◽  
News Stories ◽  
Research Reporting ◽  
News Reports ◽  
News Sources ◽  
Study Selection ◽  
Meta Analyses

AbstractCancer research in the news is often associated with sensationalising and inaccurate reporting, giving rise to false hopes and expectations. The role of study selection for cancer-related news stories is an important but less commonly acknowledged issue, as the outcomes of primary research are generally less reliable than those of meta-analyses and systematic reviews. Few studies have investigated the quality of research that makes the news and no previous analyses of the proportions of primary and secondary research in the news have been found in the literature. The main aim of this study was to investigate the nature and quality of cancer research covered in online news reports by four major news sources from USA, UK and Australia. We measured significant variation in reporting quality, and observed biases in many aspects of cancer research reporting, including the types of study selected for coverage, and in the spectrum of cancer types, gender of scientists, and geographical source of research represented. We discuss the implications of these finding for guiding accurate, contextual reporting of cancer research, which is critical in helping the public understand complex science and appreciate the outcomes of publicly funded research, avoid undermining trust in science, and assist informed decision-making.

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