scholarly journals Influencing Opinions through False Online Information : A Study

2019 ◽  
pp. 443-449
Author(s):  
Baldev Singh
Keyword(s):  
Social Media ◽  
News Media ◽  
Online Information ◽  
Fake News ◽  
False Information ◽  
Detection Algorithms ◽  
Online Social Media ◽  
The People ◽  
Information Detection ◽  
Fake Reviews

Online Social media generates lot of information now-a-days. It is not legitimate information so there are the chances of fake and false information produced using social media. It is very alarming that majority of the people getting news from social media which is very much prone to false information in comparison to traditional news media which is very dangerous to the society. One of the primary reasons to influence opinion through false information is to earn money, name or fame. In this study, the focus is on to highlight false information generated through fake reviews, fake news and hoaxes based on web & social media. It summarized various False information spreading Mechanisms, False Information Detection Algorithms, Mining Techniques for Online False Information to detect and prevent false online information.

scholarly journals Analysis on Fake News Detection Methodologies

2020 ◽  
Vol 9 (1) ◽  
pp. 1572-1575
Keyword(s):  
Social Media ◽  
News Media ◽  
Low Cost ◽  
Past Research ◽  
Online News ◽  
Easy Access ◽  
Accurate Information ◽  
Fake News ◽  
The Real ◽  
Online Social Media

Fake news is a coinage often used to refer to fabricated news that uses eye-catching headlines for increased sales rather than legitimate well-researched news, spread via online social media. Emergence of fake news has been increased with the immense use of online news media and social media. Low cost, easy access and rapid dissemination of information lead people to consume news from social media. Since the spread rate of these contents are faster it becomes difficult to identify the fake news from the accurate information. People can download articles from sites, share the content, re-share from others and by the end of the day the false information has gone far from its original site that it becomes very difficult to compare with the real news. It is a long standing problem that affects the digital social media due to its serious threats of misleading information, it creates an immense impact on the society. Hence the identification of such news are relevant and so certain measures needs to be taken in order to reduce or distinguish between the real and fake news. This paper provides a survey on recent past research papers done on this domain and provides an idea on different techniques on machine learning and deep learning that could help in the identification of fake and real news.

How Fake News Differs From Personal Lies

2020 ◽  
pp. 000276422091024
Author(s):  
Ming Ming Chiu ◽  
Yu Won Oh
Keyword(s):  
Social Media ◽  
News Media ◽  
Intimate Relationship ◽  
Detection Methods ◽  
News Article ◽  
Fake News ◽  
Media Companies ◽  
The People ◽  
School Curricula

Personal lies (girl on date lying to dad) and fake news ( Obama Bans Pledge of Allegiance) both deceive but in different ways, so they require different detection methods. People in long-term relationships try to tell undetectable lies to encourage, often, audience inaction. In contrast, unattached fake news welcome attention and try to ignite audience action. Thus, they differ in six ways: (a) speaker–audience relationship, (b) goal, (c) emotion, (d) information, (e) number of participants, and (f) citation of sources. To detect personal lies, a person can use their intimate relationship to heighten emotions, raise the stakes, and ask for more information, participants, or sources. In contrast, a person evaluates the legitimacy of potential fake news by examining the websites of its author, the people in the news article, and/or reputable media sources. Large social media companies have suitable expertise, data, and resources to reduce fake news. Search tools, rival news media links to one another’s articles, encrypted signature links, and improved school curricula might also help users detect fake news.

scholarly journals Spread of health-related fake news in Tamil social media - A pilot study

2019 ◽  
Vol 1 ◽  
pp. 21-24
Author(s):  
R Dharshanram ◽  
P D Madan Kumar ◽  
P Iyapparaj
Keyword(s):  
Social Media ◽  
Oral Cancer ◽  
News Media ◽  
Social Media Analytics ◽  
Fake News ◽  
Enterprise Application ◽  
Online Social Media ◽  
Health Related ◽  
Educational Campaign ◽  
Gum Disease

Introduction: Fake news is a type of propaganda that consists of deliberate misinformation or hoaxes spread through traditional print and broadcast news media or online social media. Fake news concerning health subject is not a new phenomenon - its roots are probably as old as healthcare itself. Aim: This study aims to measure the volume of shares concerning health-related fake news in Tamil language social media. Materials and Methods: Analysis was performed employing the BuzzFeed Enterprise Application available through its website. BuzzFeed is a social media analytics and curation tool for content marketers. The data were obtained for 15 most commonly shared pages concerning four keywords, namely vaccinations, oral cancer, gum disease, and dental caries from May 1, 2018, to May 15, 2018, in Tamil language, the local vernacular. Results: The topic most contaminated with fake news was vaccinations (80%) followed by oral cancer and gum disease (both in 60%). Altogether, links containing fake news were shared 272 times in 15 days and accounted for 40% of the studied material. Conclusion: Action could be taken to scientifically evaluate sources of the most frequently shared medical myths. As shown above, some topics were generally free of fake news, whereas others were extremely biased and filled with fallacies. Thus, an extensive educational campaign (not only in social media) for the latter should be implemented.

scholarly journals Infodemia – an Analysis of Fake News in Polish News Portals and Traditional Media During the Coronavirus Pandemic

2021 ◽  
Vol 34 (4) ◽  
pp. 81-98
Author(s):  
Malwina Popiołek ◽  
Monika Hapek ◽  
Marzena Barańska
Keyword(s):  
Social Media ◽  
Qualitative Analysis ◽  
Work Environment ◽  
News Media ◽  
News Item ◽  
Fake News ◽  
Sources Of Information ◽  
False Information ◽  
Traditional Media ◽  
The Media

The article addresses the issue of the presence of false information on coronavirus in the Polish news media between January and September 2020. The research aimed to check the extent to which traditional media participate in disinformation processes during the pandemic. An attempt has also been made at explaining the reasons for the publication of fake news in these media. Sources of information that Poles use most often were examined: popular information portals, traditional media websites, and social media (Facebook and Twitter). The article analyses false information in both quantitative and qualitative terms. A total of 101 pieces of false information made available online were diagnosed, of which every fourth news item (25.74%) appeared in opinion-forming media (three most popular news portals and all traditional media were taken into account). The qualitative analysis shows that publishing false information in the opinion-forming media is the result of changes in the journalistic work environment (especially declining standards of work, a desire to attract the attention of the media audience and the pursuit of the media organisations’ own interests). However, this issue requires further research in editorial offices and among journalists.

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 Detecting Suspicious News over Social Media using Ant Colony Optimization

2019 ◽  
Vol 8 (12) ◽  
pp. 1679-1684
Keyword(s):  
Social Media ◽  
Ant Colony Optimization ◽  
Research Work ◽  
Ant Colony ◽  
Free Access ◽  
Fake News ◽  
Online Social Media ◽  
The People ◽  
Other Information ◽  
Social Media Platforms

The online social media platforms has become the trending as it provide the convenient and free access to users to share their day to day activities and other information. Despite the significance of these online social media platforms, there are also the people that can mislead the other by posting the fake news. These kinds of news are termed as suspicious news. Such kind of misleading news can badly affect the society. It is the way too hard to completely restrict such people from posting anything on social media. But after the detection of such activities, these posts can be removed from social media and users can be restricted from the respective platform. From the years, researchers are continuously working to detect the suspicious activities using machine learning and data mining techniques. This research work addresses the problem of suspicious news detection using the ant colony optimization based ant miner plus technique. This proposed approach is termed as ACODSN (Ant Colony Optimization for the Detection of Suspicious News). The experimentation is conducted on the dataset of FakenewsNet. The system performance is analyzed in terms of evaluation metrics of recall, precision, and f-measure.

scholarly journals A Framework for Finding Outspread News Pattern on Diverse Dataset using Time-Series

2020 ◽  
Vol 9 (3) ◽  
pp. 1102-1106
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Time Series ◽  
Pattern Analysis ◽  
Fake News ◽  
False Information ◽  
The People ◽  
The Social ◽  
Future Direction ◽  
The Individual

Expressing feeling or opinion is an inherent property of the individual and Now a day’s social media becomes an integral part of everyone’s life. It is a great medium to analyze the feeling of mass, but sometimes it flows the false feeling in the form of fake news or contents posted on social media. These fake content affects the people in the form of sentiments or companies in the business loss/profit, because most of the people make opinions based on what they read on social media. In fact, fake news or false information can create the damage among the individual, so it should be identified as early as possible. The interest in finding the pattern of fake news has been growing very rapidly in the last few years. In this article we proposed a comprehensive pattern analysis of viral contents, real or fake news on twitter using time series analysis. The proposed technique is simple but effective for detecting and analysis fake contents on the social networks. Experiments results shows that our proposed technique outperformed for differentiating real vs fake news on twitter. Finally, we identify and discuss future direction.

scholarly journals Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 556
Author(s):  
Thaer Thaher ◽  
Mahmoud Saheb ◽  
Hamza Turabieh ◽  
Hamouda Chantar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Selection ◽  
Language Processing ◽  
User Profile ◽  
Vital Role ◽  
Classification Model ◽  
Fake News ◽  
False Information ◽  
Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

scholarly journals Advancing children’s news media literacy: learning from the practices and experiences of young Australians

2018 ◽  
Vol 41 (5) ◽  
pp. 689-707
Author(s):  
Tanya Notley ◽  
Michael Dezuanni
Keyword(s):  
Social Media ◽  
Media Literacy ◽  
News Media ◽  
Social Impact ◽  
Literacy Learning ◽  
Literacy Education ◽  
Fake News ◽  
News Stories ◽  
Media Literacy Education ◽  
Nationally Representative

Social media use has redefined the production, experience and consumption of news media. These changes have made verifying and trusting news content more complicated and this has led to a number of recent flashpoints for claims and counter-claims of ‘fake news’ at critical moments during elections, natural disasters and acts of terrorism. Concerns regarding the actual and potential social impact of fake news led us to carry out the first nationally representative survey of young Australians’ news practices and experiences. Our analysis finds that while social media is one of young people’s preferred sources of news, they are not confident about spotting fake news online and many rarely or never check the source of news stories. Our findings raise important questions regarding the need for news media literacy education – both in schools and in the home. Therefore, we consider the historical development of news media literacy education and critique the relevance of dominant frameworks and pedagogies currently in use. We find that news media has become neglected in media literacy education in Australia over the past three decades, and we propose that current media literacy frameworks and pedagogies in use need to be rethought for the digital age.

scholarly journals Hate in a Tweet: Exploring Internet-Based Islamophobic Discourses

Religions ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 307 ◽  
Author(s):  
Giulia Evolvi
Keyword(s):  
Social Media ◽  
European Union ◽  
Qualitative Analysis ◽  
Union Membership ◽  
News Media ◽  
Fake News ◽  
Global Networks ◽  
Western Values

Islamophobia is the unfounded hostility against Muslims. While anti-Muslim feelings have been explored from many perspectives and in different settings, Internet-based Islamophobia remains under-researched. What are the characteristics of online Islamophobia? What are the differences (if any) between online and offline anti-Muslim narratives? This article seeks to answer these questions through a qualitative analysis of tweets written in the aftermath of the 2016 British referendum on European Union membership (also known as “Brexit”), which was followed by a surge of Islamophobic episodes. The analysis of the tweets suggests that online Islamophobia largely enhances offline anti-Islam discourses, involving narratives that frame Muslims as violent, backward, and unable to adapt to Western values. Islamophobic tweets also have some peculiar characteristics: they foster global networks, contain messages written by so-called “trolls” and “bots,” and contribute to the spreading of “fake news.” The article suggests that, in order to counteract online Islamophobia, it is important to take into account the networked connections among social media, news media platforms, and offline spaces.

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