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 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.

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 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.

scholarly journals Fake News Detection on Social Media Using A Natural Language Inference Approach

2020 ◽  
Author(s):  
Amir Bidgoly ◽  
Hossein Amirkhani ◽  
Fariba Sadeghi
Keyword(s):  
Social Media ◽  
Natural Language ◽  
News Item ◽  
Support Vector ◽  
Statistical Features ◽  
Fake News ◽  
K Nearest Neighbors ◽  
News Sources ◽  
Online Social Media ◽  
Embedding Methods

Abstract Fake news detection is a challenging problem in online social media, with considerable social and political impacts. Several methods have already been proposed for the automatic detection of fake news, which are often based on the statistical features of the content or context of news. In this paper, we propose a novel fake news detection method based on Natural Language Inference (NLI) approach. Instead of using only statistical features of the content or context of the news, the proposed method exploits a human-like approach, which is based on inferring veracity using a set of reliable news. In this method, the related and similar news published in reputable news sources are used as auxiliary knowledge to infer the veracity of a given news item. We also collect and publish the first inference-based fake news detection dataset, called FNID, in two formats: the two-class version (FNID-FakeNewsNet) and the six-class version (FNID-LIAR). We use the NLI approach to boost several classical and deep machine learning models including Decision Tree, Naïve Bayes, Random Forest, Logistic Regression, k-Nearest Neighbors, Support Vector Machine, BiGRU, and BiLSTM along with different word embedding methods including Word2vec, GloVe, fastText, and BERT. The experiments show that the proposed method achieves 85.58% and 41.31% accuracies in the FNID-FakeNewsNet and FNID-LIAR datasets, respectively, which are 10.44% and 13.19% respective absolute improvements.

scholarly journals A new approach of ranking prevalent news topics from social media using unsupervised techniques

2019 ◽  
Vol 8 (07) ◽  
pp. 24683-24789
Author(s):  
Dr. D. Murali ◽  
Vinutha BA
Keyword(s):  
Social Media ◽  
News Media ◽  
User Interaction ◽  
Common People ◽  
New Approach ◽  
Online Social Media ◽  
The Social ◽  
User Attention ◽  
The Common ◽  
Filter Noise

The precious data from online origin has developed into a extended research. The mass media and news media provides the daily events to the common people. Huge amount of information is been achieved by an online social media suchlike Twitter, which contains more information about news-associated content. It is necessary to find a way to filter noise, for these resources to be useful and grab the content that is depend on the similarity to news media. Despite after the noise is eliminated the excessive data still remain in the data so it is essential to prioritize it for utilization. We are introducing three factors for prioritization. The unsupervised technique finds the news topics that are common in the pair of social media and news media, and then ranks them by the applicability factors such as MF, UA and UI. Initially the temporal prevalence of the appropriate topic in news media focus (MF). Secondary the temporal prevalence of the appropriate topic in social media illustrates the user attention (UA). Finally the interconnection among the social media users who specify this topic demonstrates the power of the society who is discussing; it is termed as the user interaction (UI).  

scholarly journals Detecting Fake News Over Job Posts via Bi-Directional Long Short-Term Memory (BIDLSTM)

2021 ◽  
Vol 16 (6) ◽  
pp. 1-18
Author(s):  
T. V. Divya ◽  
Barnali Gupta Banik
Keyword(s):  
Social Media ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Word Embedding ◽  
Support Vector ◽  
Fake News ◽  
Short Term ◽  
Term Memory ◽  
Online Social Media ◽  
Long Short Term Memory

Fake news detection on job advertisements has grabbed the attention of many researchers over past decade. Various classifiers such as Support Vector Machine (SVM), XGBoost Classifier and Random Forest (RF) methods are greatly utilized for fake and real news detection pertaining to job advertisement posts in social media. Bi-Directional Long Short-Term Memory (Bi-LSTM) classifier is greatly utilized for learning word representations in lower-dimensional vector space and learning significant words word embedding or terms revealed through Word embedding algorithm. The fake news detection is greatly achieved along with real news on job post from online social media is achieved by Bi-LSTM classifier and thereby evaluating corresponding performance. The performance metrics such as Precision, Recall, F1-score, and Accuracy are assessed for effectiveness by fraudulency based on job posts. The outcome infers the effectiveness and prominence of features for detecting false news. .

scholarly journals Social Media Infodemic During COVID-19 Outbreak

2020 ◽  
Vol 15 (4) ◽  
pp. 95-97
Author(s):  
Jeevan Bhatta ◽  
Sharmistha Sharma ◽  
Shashi Kandel ◽  
Roshan Nepal
Keyword(s):  
Public Health ◽  
Social Media ◽  
Current Situation ◽  
Fake News ◽  
Related Information ◽  
Personal Experiences ◽  
Health Related ◽  
Social Media Platforms ◽  
To Receive ◽  
Common Platform

Social media is a common platform that enables its users to share opinions, personal experiences, perspectives with one another instantaneously, globally. It has played a paramount role during pandemics such as COVID-19 and unveiled itself as a crucial means to communicate between the sources and the individuals. However, it also has become a place to disseminate misinformation and fake news rapidly. Infodemic, a plethora of information, some authentic some not makes it even harder to general people to receive factual and trustworthy information when required, has grown to be a major risk to public health and social media is developing as a trendy platform for this infodemic. This commentary aims to explore how social media has affected the current situation. We also aim to share our insight to control this misinformation.  This commentary contributes to evolving knowledge to counter fake news or health-related information shared over various social media platforms.

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