Fake News Detection Using ML

Udit Sharma

doi:10.22214/ijraset.2021.37209

Fake News Detection Using ML

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37209 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 3668-3674

Author(s):

Udit Sharma

Keyword(s):

Deep Learning ◽

Breaking Point ◽

Fake News ◽

Mechanical Assembly

Fake news is depicted as a story that is made up with an aim to mislead or to swindle the peruser. We have introduced a reaction for the undertaking of phony news disclosure by utilizing Deep Learning structures. Because of various number of instances of phony news the outcome has been an augmentation in the in the spread of phony news. Due to the wide impacts of the immense onsets of phony news, people are conflicting if not by huge helpless finders of phony news. The most liked of such exercises consolidate "boycotts" of sources and producers that are not trustworthy. While these instruments are used to make an inexorably unique complete beginning to end plan, we need to address continuously inconvenient situations where logically strong sources and makers discharge fake news. As, the objective of this endeavor was to make a mechanical assembly for perceiving the language designs that portray phony and confirmed news using AI, AI and customary language getting ready techniques. The consequences of this undertaking exhibit the breaking point with respect to AI and AI to be huge. We have developed a model that gets numerous no of normal indications of veritable and phony news and additionally an application that aides in the portrayal of the order decision.

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BerConvoNet: A deep learning framework for fake news classification

Applied Soft Computing ◽

10.1016/j.asoc.2021.107614 ◽

2021 ◽

pp. 107614

Author(s):

Monika Choudhary ◽

Satyendra Singh Chouhan ◽

Emmanuel S. Pilli ◽

Santosh Kumar Vipparthi

Keyword(s):

Deep Learning ◽

Fake News ◽

Learning Framework

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Optimization and improvement of fake news detection using deep learning approaches for societal benefit

International Journal of Information Management Data Insights ◽

10.1016/j.jjimei.2021.100051 ◽

2021 ◽

Vol 1 (2) ◽

pp. 100051

Author(s):

Tavishee Chauhan ◽

Hemant Palivela

Keyword(s):

Deep Learning ◽

Learning Approaches ◽

Fake News ◽

Societal Benefit

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Fake news and misinformation detection on headlines of COVID-19 using deep learning algorithms

International Journal of Data Science ◽

10.1504/ijds.2020.115873 ◽

2020 ◽

Vol 5 (4) ◽

pp. 316

Author(s):

Xin Wang ◽

Peng Zhao ◽

Xi Chen

Keyword(s):

Deep Learning ◽

Learning Algorithms ◽

Fake News

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The Explore of Using Deep Learning Models for Fake News Classification

Lecture Notes in Electrical Engineering - Frontier Computing ◽

10.1007/978-981-15-3250-4_46 ◽

2020 ◽

pp. 389-393

Author(s):

Ting-Hao Chang ◽

Wei-Hung Tu ◽

Jia-Wei Chang ◽

Tien-Chi Huang ◽

Yi-Xiang Luo

Keyword(s):

Deep Learning ◽

Fake News ◽

Learning Models

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Fake News Detection Using Deep Learning

Research Anthology on Fake News, Political Warfare, and Combatting the Spread of Misinformation ◽

10.4018/978-1-7998-7291-7.ch025 ◽

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%.

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Detecting Fake News Using Deep Learning and NLP

Advances in Digital Crime, Forensics, and Cyber Terrorism - Confluence of AI, Machine, and Deep Learning in Cyber Forensics ◽

10.4018/978-1-7998-4900-1.ch007 ◽

2021 ◽

pp. 117-133

Author(s):

Uma Maheswari Sadasivam ◽

Nitin Ganesan

Keyword(s):

Deep Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Reliable Information ◽

Citizen Journalism ◽

Fake News ◽

Social Unrest ◽

Safe Place ◽

The Social

Fake news is the word making more talk these days be it election, COVID 19 pandemic, or any social unrest. Many social websites have started to fact check the news or articles posted on their websites. The reason being these fake news creates confusion, chaos, misleading the community and society. In this cyber era, citizen journalism is happening more where citizens do the collection, reporting, dissemination, and analyse news or information. This means anyone can publish news on the social websites and lead to unreliable information from the readers' points of view as well. In order to make every nation or country safe place to live by holding a fair and square election, to stop spreading hatred on race, religion, caste, creed, also to have reliable information about COVID 19, and finally from any social unrest, we need to keep a tab on fake news. This chapter presents a way to detect fake news using deep learning technique and natural language processing.

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