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 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.

Whose News Can You Trust?

2014 ◽  
pp. 324-352
Author(s):  
Rick Malleus
Keyword(s):  
Peer Review ◽  
Theoretical Framework ◽  
Online News ◽  
News Sources ◽  
Definition Of ◽  
Online News Sites

This chapter proposes a framework for analyzing the credibility of online news sites, allowing diaspora populations to evaluate the credibility of online news about their home countries. A definition of credibility is established as a theoretical framework for analysis, and a framework of seven elements is developed based on the following elements: accuracy, authority, believability, quality of message construction, peer review, comparison, and corroboration. Later, those elements are applied to a variety of online news sources available to the Zimbabwean diaspora that serves as a case study for explaining the framework. The chapter concludes with a discussion of the framework in relation to some contextual circumstances of diaspora populations and presents some limitations of the framework as diaspora populations might actually apply the different elements.

scholarly journals Fake News Detection using RNN-LSTM

2021 ◽  
Vol 9 (10) ◽  
pp. 1731-1737
Author(s):  
Samarth Mengji
Keyword(s):  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Principal Factor ◽  
Online Media ◽  
Fake News ◽  
Short Term ◽  
Web Based ◽  
Personal Level ◽  
Long Short Term Memory

Abstract: Fake news distribution is a social phenomenon that can't be avoided on a personal level or through web-based social media like Facebook and Twitter. We're interested in counterfeit news because it's one of many sorts of double dealing in online media, but it's a more severe one because it's designed to deceive people. We're concerned about this now that we've seen what's going on. We are concerned about this issue because we have seen how, through the usage of social correspondence, this marvel has recently caused a shift in the direction of society and people groupings, as well as their opinions. Along these lines, we chose to confront and decrease this wonder, which is as yet the principal factor to pick a large portion of our choices. Our objective in this study is to develop a detector that can predict if a piece of news is false based just on its content, and then attack the problem using RNN method models LSTMs and Bi-LSTMs to tackle the problem from a basic deep learning viewpoint. Keywords: RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), Fake news detection, Deep learning

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..

Whose News Can You Trust?

2012 ◽  
pp. 186-214
Author(s):  
Rick Malleus
Keyword(s):  
Peer Review ◽  
Theoretical Framework ◽  
Online News ◽  
News Sources ◽  
Definition Of ◽  
Online News Sites

This chapter proposes a framework for analyzing the credibility of online news sites, allowing diaspora populations to evaluate the credibility of online news about their home countries. A definition of credibility is established as a theoretical framework for analysis, and a framework of seven elements is developed based on the following elements: accuracy, authority, believability, quality of message construction, peer review, comparison, and corroboration. Later, those elements are applied to a variety of online news sources available to the Zimbabwean diaspora that serves as a case study for explaining the framework. The chapter concludes with a discussion of the framework in relation to some contextual circumstances of diaspora populations and presents some limitations of the framework as diaspora populations might actually apply the different elements.

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.

scholarly journals Fake News Detection on Social Media

2020 ◽  
pp. 1132-1137
Author(s):  
Dipti Chaudhari ◽  
Krina Rana ◽  
Radhika Tannu ◽  
Snehal Yadav
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Smart Phone ◽  
Online News ◽  
Support Vector ◽  
Fake News ◽  
News Sources ◽  
Application Architecture ◽  
News Websites ◽  
Facebook Pages

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and the articles which are circulated among the social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs and social networking sites. It can be considered that social media has replaced the traditional media and become one of the main platforms for spreading news. News on social media trends to travel faster and easier than traditional news sources due to the internet accessibility and convenience. It is harmful for the society to believe on the rumors and pretend to be a news. The basic need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and methodology for fake news detection. With the help of Machine Learning, we tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. Even we have presented the mechanism to identify the significant Tweet's attribute and application architecture to systematically automate the classification of the online news.

scholarly journals Rumor Detection on Twitter

2021 ◽  
Vol 9 (8) ◽  
pp. 2543-2546
Author(s):  
Shilpa Singhal
Keyword(s):  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Fake News ◽  
Web Based ◽  
News Sources ◽  
The People ◽  
Media Interaction ◽  
Rumor Detection ◽  
The Given ◽  
Source Of Information

Abstract: Social media interaction such as news spreading around the network is a great source of information nowadays. From one’s perspective, its negligible exertion, straightforward access, and quick dispersing of information that lead people to look out and eat up news from internet-based life. Twitter is among the most well-known ongoing news sources that ends up a standout amongst the most dominant news spreading mediums. It is known to cause extensive harm by spreading bits of fake news among the people. Online clients are normally vulnerable and are reliable on web-based networking media as their source of information without checking the veracity of the information being spread. This research contributes to develops a system for detection of rumors about real- world events that propagate on Twitter and to design a prediction algorithm that will train the machine to predict whether the given data is information or a rumor. The work finds all the useful features of a Tweet. The dataset used is the pheme dataset of known Rumors and Non Rumors. Afterwards, we make a comparison between various known Machine learning algorithms such as Decision tree, SVM, Random Tree.

Sign in / Sign up

Export Citation Format

Share Document