Survey on Fake News or Truth - Rumours Detection using Machine Learning

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Kalyani Deore ◽  
Leena Gaikwad ◽  
Rohit Dhamne ◽  
Vishal Agale ◽  
T. Bhaskar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Modern World ◽  
Fake News ◽  
Misleading Information ◽  
Original Source ◽  
News Stories ◽  
False News ◽  
Communication Methods

This study is to help readers to understand detection of fake news using machine learning. The main purpose of the planned system is to build an application which identifies fake news stories from a bunch of news stories to make people aware of fake news rumours. With the help of machine learning algorithms, we can detect and separate out the fake news. Nowadays, it is become harder to identify the original source of news stories, like looking for a needle in a haystack. In the modern world, news is a kind of communication that keeps us up to date on the latest events, topics, and people in the wider globe. A society relies on news for a variety of reasons, the most important of which is informing its members about events taking on in and around them that might influence them. Oral and traditional media, as well as digital communication methods, altered videos, memes, unconfirmed marketing, and social media have all contributed to the spread of rumors. As nowadays many people use social media in many cases people get wrong and misleading information and people share those stories without verifying whether it is real or fake news stories. Spreading false information on social media has become a major problem these days. That is why we need a system that can tell us whether something is false news or not. Applications are: 1. Fake news may be detected on social media using this approach. 2. The system can be used to help news channels to broadcast only real and classified news. 3. Users can easily detect and eliminate fake articles that contain misinformation intended to mislead readers.

scholarly journals Prediction of Fake Tweets Using Machine Learning Algorithms

2021 ◽  
Author(s):  
M. Sreedevi ◽  
G. Vijay Kumar ◽  
K. Kiran Kumar ◽  
B. Aruna ◽  
Arvind Yadav
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Internet Media ◽  
Learning Techniques ◽  
False News

Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.

Spiritualism and the Resurgence of Fake News

2020 ◽  
pp. 222-237
Author(s):  
Kristy A. Hesketh
Keyword(s):  
Social Media ◽  
Mass Media ◽  
Fake News ◽  
News Stories ◽  
News Reports ◽  
The World ◽  
Spirit World ◽  
Social Media Platforms ◽  
Media Platform ◽  
False News

This chapter explores the Spiritualist movement and its rapid growth due to the formation of mass media and compares these events with the current rise of fake news in the mass media. The technology of cheaper publications created a media platform that featured stories about Spiritualist mediums and communications with the spirit world. These articles were published in newspapers next to regular news creating a blurred line between real and hoax news stories. Laws were later created to address instances of fraud that occurred in the medium industry. Today, social media platforms provide a similar vessel for the spread of fake news. Online fake news is published alongside legitimate news reports leaving readers unable to differentiate between real and fake articles. Around the world countries are actioning initiatives to address the proliferation of false news to prevent the spread of misinformation. This chapter compares the parallels between these events, how hoaxes and fake news begin and spread, and examines the measures governments are taking to curb the growth of misinformation.

scholarly journals Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 64
Author(s):  
Panagiotis Kantartopoulos ◽  
Nikolaos Pitropakis ◽  
Alexios Mylonas ◽  
Nicolas Kylilis
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Test Phase ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Specific Subject ◽  
Learning Techniques ◽  
Twitter Account

Social media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual creation of fake users. Their goal is to deceive other users employing various methods, or even create a stream of fake news and opinions in order to influence an idea upon a specific subject, thus impairing the platform’s integrity. As such, machine learning techniques have been widely used in social networks to address this type of threat by automatically identifying fake accounts. Nonetheless, threat actors update their arsenal and launch a range of sophisticated attacks to undermine this detection procedure, either during the training or test phase, rendering machine learning algorithms vulnerable to adversarial attacks. Our work examines the propagation of adversarial attacks in machine learning based detection for fake Twitter accounts, which is based on AdaBoost. Moreover, we propose and evaluate the use of k-NN as a countermeasure to remedy the effects of the adversarial attacks that we have implemented.

scholarly journals Intentional or inadvertent fake news sharing? Fact-checking warnings and users’ interaction with social media content

2020 ◽  
Author(s):  
Alberto Ardèvol-Abreu ◽  
Patricia Delponti ◽  
Carmen Rodríguez-Wangüemert
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Media Content ◽  
Fake News ◽  
Negative Effects ◽  
News Stories ◽  
Social Media Platforms ◽  
Fact Checking ◽  
False News ◽  
News Sharing

The main social media platforms have been implementing strategies to minimize fake news dissemination. These include identifying, labeling, and penalizing –via news feed ranking algorithms– fake publications. Part of the rationale behind this approach is that the negative effects of fake content arise only when social media users are deceived. Once debunked, fake posts and news stories should therefore become harmless. Unfortunately, the literature shows that the effects of misinformation are more complex and tend to persist and even backfire after correction. Furthermore, we still do not know much about how social media users evaluate content that has been fact-checked and flagged as false. More worryingly, previous findings suggest that some people may intentionally share made up news on social media, although their motivations are not fully explained. To better understand users’ interaction with social media content identified or recognized as false, we analyze qualitative and quantitative data from five focus groups and a sub-national online survey (N = 350). Findings suggest that the label of ‘false news’ plays a role –although not necessarily central– in social media users’ evaluation of the content and their decision (not) to share it. Some participants showed distrust in fact-checkers and lack of knowledge about the fact-checking process. We also found that fake news sharing is a two-dimensional phenomenon that includes intentional and unintentional behaviors. We discuss some of the reasons why some of social media users may choose to distribute fake news content intentionally.

scholarly journals Doubters are more convincing than advocates. The impact of user comments and ratings on credibility perceptions of false news stories on social media

2019 ◽  
Vol 8 (4) ◽  
pp. 446-470
Author(s):  
Jan P . Kluck ◽  
Leonie Schaewitz ◽  
Nicole C . Krämer
Keyword(s):  
Social Media ◽  
Misleading Information ◽  
News Stories ◽  
User Comments ◽  
News Credibility ◽  
Credibility Rating ◽  
Heuristic Information Processing ◽  
False News ◽  
The Impact ◽  
Sharing Behavior

False information on social media poses a crucial threat to our society, and calls for interventions to combat this problem are becoming louder. Users themselves may have the potential to diminish the impact of misleading information. In an online experiment with a 3 x 3 between-subjects design (credibility evaluation in user comments: positive vs. negative vs. none) x (numerical credibility rating: positive vs. negative vs. none), we tested the influence of bandwagon cues on the impact of a false news post on Facebook (N = 240). Contrary to prevalent assumptions regarding heuristic information processing, numerical credibility ratings had no influence on participants’ credibility appraisals and intended sharing behavior. However, negative user comments diminished the believability of false news. Moreover, participants’ willingness to share the news post publicly and privately was indirectly reduced by the effect of negative user comments on perceived news credibility.

scholarly journals Social Media and Fake News in the 2016 Election

2017 ◽  
Vol 31 (2) ◽  
pp. 211-236 ◽  
Author(s):  
Hunt Allcott ◽  
Matthew Gentzkow
Keyword(s):  
Social Media ◽  
Online Survey ◽  
Fake News ◽  
News Stories ◽  
Social Media Networks ◽  
Data Archives ◽  
American Adult ◽  
2016 Election ◽  
False News ◽  
Election News

Following the 2016 US presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: 1) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; 2) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; 3) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and 4) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

scholarly journals Fake News Detection: A Survey of Techniques

2020 ◽  
Vol 9 (9) ◽  
pp. 383-386
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Fake News ◽  
False Information ◽  
News Stories ◽  
Learning Techniques ◽  
Negative Impacts ◽  
To Come

The extensive spread of fake news (low quality news with intentionally false information) has the potential for extremely negative impacts on individuals, society and particular in the political world. Therefore, fake news detection on social media has recently become an emerging research which is attracting tremendous attention. Detection of false information is technically challenging for several reasons. Use of various social media tools, content is easily generated and quickly spread, which lead to a large volume of content to analyze. Online information is very wide spread, which cover a large number of subjects, which contributes complexity to this task. The application of machine learning techniques are explored for the detection of ‘fake news’ that come from non-reputable sources which mislead real news stories. The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information. This paper performs survey of Machine learning techniques which is mainly used for false detection and provides easier way to generate results.

scholarly journals Fake News Detection on Social Media using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 940-943
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Huge Number ◽  
Web Based ◽  
Distributed Information ◽  
Learning Techniques ◽  
Day By Day

: Web based life administrations, as Facebook and Twitter, Renren, Instagram, and linkedin have recently become an enormous and persistent supply of day by day news. These stages give a huge number of clients and give numerous administrations, for example, content arrangement and distributing. Not all distributed information via internet based medium is dependable and exact. Numerous individuals attempt to distribute fake and mistaken news so as to control general conclusion. Counterfeit news might be intentionally made to advance money related, political and public premiums, and can lead to unsafe effects on people convictions and choices.. In this paper we examine different systems for recognizing counterfeit information via internet based networking medium. Our point is to locate a dependable and right model that arranges a given article as fake or genuine. For identification of fake articles we use machine learning algorithms.

scholarly journals A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

2021 ◽  
Vol 9 (8) ◽  
pp. 1046-1050
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
New Technologies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Fake News ◽  
Detection Techniques ◽  
Social Media Platforms ◽  
Extensive Growth

creasing number of social media platforms, emerging new technologies, and population growth which results in the rate of using social media has increased rapidly. With an increasing number of users on online platforms comes to a variety of problems like fake news. The extensive growth of fake news on social media can have a serious impact on the real world and became a cause of concern for net users and governments all over the world. Distinguishing between real news and fake news becoming more challenging. The amount of fake news has become a disguise. In this paper, we have done a survey on detection techniques for fake news using Algorithms and Deep learning techniques. We have compared machine learning algorithms like Naïve-Bayes, Decision tree, SVM, Adaboost, etc. Comparing the accuracy

scholarly journals An Effecient Fake News Detection System Using Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 3125-3129 ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Negative Impact ◽  
Detection System ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Easy Access ◽  
Fake News ◽  
K Nearest Neighbors

Social media plays a major role in several things in our life. Social media helps all of us to find some important news with low price. It also provides easy access in less time. But sometimes social media gives a chance for the fast-spreading of fake news. So there is a possibility that less quality news with false information is spread through the social media. This shows a negative impact on the number of people. Sometimes it may impact society also. So, detection of fake news has vast importance. Machine learning algorithms play a vital role in fake news detection; Especially NLP (Natural Language Processing) algorithms are very useful for detecting the fake news. In this paper, we employed machine learning classifiers SVM, K-Nearest Neighbors, Decision tree, Random forest. By using these classifiers we successfully build a model to detect fake news from the given dataset. Python language was used for experiments.

Sign in / Sign up

Export Citation Format

Share Document