scholarly journals Unsupervised Fake News Detection on Social Media: A Generative Approach

2019 ◽  
Vol 33 ◽  
pp. 5644-5651 ◽  
Author(s):  
Shuo Yang ◽  
Kai Shu ◽  
Suhang Wang ◽  
Renjie Gu ◽  
Fan Wu ◽  
...  
Keyword(s):  
Social Media ◽  
Research Effort ◽  
Low Cost ◽  
Fake News ◽  
Bayesian Network Model ◽  
Inference Problem ◽  
Crucial Problem ◽  
Negative Impacts ◽  
Collapsed Gibbs Sampling ◽  
Generative Approach

Social media has become one of the main channels for people to access and consume news, due to the rapidness and low cost of news dissemination on it. However, such properties of social media also make it a hotbed of fake news dissemination, bringing negative impacts on both individuals and society. Therefore, detecting fake news has become a crucial problem attracting tremendous research effort. Most existing methods of fake news detection are supervised, which require an extensive amount of time and labor to build a reliably annotated dataset. In search of an alternative, in this paper, we investigate if we could detect fake news in an unsupervised manner. We treat truths of news and users’ credibility as latent random variables, and exploit users’ engagements on social media to identify their opinions towards the authenticity of news. We leverage a Bayesian network model to capture the conditional dependencies among the truths of news, the users’ opinions, and the users’ credibility. To solve the inference problem, we propose an efficient collapsed Gibbs sampling approach to infer the truths of news and the users’ credibility without any labelled data. Experiment results on two datasets show that the proposed method significantly outperforms the compared unsupervised methods.

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.

A Deep Learning Based Approach for Fake News Detection

2021 ◽  
pp. 388-394
Author(s):  
Srishti Sharma ◽  
Vaishali Kalra
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Language Processing ◽  
Research Effort ◽  
Political Polarization ◽  
Fake News ◽  
User Characteristics ◽  
Mainstream Media ◽  
Boosting Method ◽  
Partisan Conflict

Owing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problemOwing to the rapid explosion of social media platforms in the past decade, we spread and consume information via the internet at an expeditious rate. It has caused an alarming proliferation of fake news on social networks. The global nature of social networks has facilitated international blowout of fake news. Fake news has proven to increase political polarization and partisan conflict. Fake news is also found to be more rampant on social media than mainstream media. The evil of fake news is garnering a lot of attention and research effort. In this work, we have tried to handle the spread of fake news via tweets. We have performed fake news classification by employing user characteristics as well as tweet text. Thus, trying to provide a holistic solution for fake news detection. For classifying user characteristics, we have used the XGBoost algorithm which is an ensemble of decision trees utilising the boosting method. Further to correctly classify the tweet text we used various natural language processing techniques to preprocess the tweets and then applied a sequential neural network and state-of-the-art BERT transformer to classify the tweets. The models have then been evaluated and compared with various baseline models to show that our approach effectively tackles this problem

MEDIA LITERACY FOR DISSEMINATION ANTICIPATED FAKE NEWS ON SOCIAL MEDIA

2020 ◽  
Vol 13 (2) ◽  
pp. 278-289
Author(s):  
Dedeh Fardiah ◽  
Rini Rinawati ◽  
Ferry Darmawan ◽  
Rifqi Abdul ◽  
Kurnia Lucky
Keyword(s):  
Social Media ◽  
Legal Protection ◽  
Provincial Government ◽  
Fake News ◽  
The West ◽  
West Java ◽  
The Public ◽  
The Government ◽  
Negative Impacts ◽  
Depth Interviews

Information technology nowadays results in spreading information rapidly. Everyone can easily produce information quickly through several social media, such as Facebook, Twitter, Instagram, or mobile phone messages, such as WhatsApp, Telegram, etc. It is alarming if the information conveyed is inaccurate such as a hoax with a highly provocative title, leading the reader and recipient to obtain a negative opinion. For fighting hoaxes and preventing their negative impacts, the government has adequate legal protection named ITE Law. Apart from the legal product, the government also forms the National Cyber Institution. For example, in West Java, the government has formed West Java Clean Sweep Team (Saber) for Hoaxes, in charge of verifying information distribution in public. The team is built as proactive efforts of the West Java Provincial Government to secure the residents of West Java from disseminating fake news. This article examines how the West Java Saber Hoaxes Team carried out a strategy to minimize the dissemination of fake news (hoaxes) on social media. The research used descriptive studies through in-depth interviews on West Java Saber Hoaxes Team. The result of the research showed that strategies conducted by this team are monitoring, receiving complaints, and educating the public.

scholarly journals SOCIAL MEDIA IN INFORMATION WARFARE. ASSAULT WEAPON WITH HIGH RECOIL

2021 ◽  
Vol 17 (1) ◽  
pp. 258-264
Author(s):  
Alin PREDA
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Web 2.0 ◽  
Low Cost ◽  
The Internet ◽  
Information Warfare ◽  
Information Propagation ◽  
Fake News ◽  
Institutional Communication ◽  
Assault Weapon

Beyond the benefits or risks of individual or institutional communication through social media, we must note that it is the perfect environment for fake news and propaganda because of the speed of information propagation, the unfriendly environment for checking sources, algorithms behind social networks and, last but not least, the extremely low cost. In other words, the Internet and web 2.0 have created the favorable framework for the conduct of the war "for minds and hearts", as it can be called the information war waged through social media. Beyond these considerations, the non-regulation of the online domain - the lack of rules, be they deontological, make social media a powerful weapon of attack in this type of war. At the same time, the use of this space by state actors should be done with caution because it involves risks that could result in the loss of the most important action capacity: credibility. This article aims to analyze social media as a tool in information warfare

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 Stance detection with BERT embeddings for credibility analysis of information on social media

2021 ◽  
Vol 7 ◽  
pp. e467
Author(s):  
Hema Karande ◽  
Rahee Walambe ◽  
Victor Benjamin ◽  
Ketan Kotecha ◽  
TS Raghu
Keyword(s):  
Social Media ◽  
Online Social Networks ◽  
Early Stage ◽  
Low Cost ◽  
Easy Access ◽  
Fake News ◽  
Cable News ◽  
Automatic Feature Extraction ◽  
People Search ◽  
Mixed Blessing

The evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers the societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e., when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.

scholarly journals Bias Konfirmasi terhadap Perilaku Berbohong

2020 ◽  
Vol 7 (1) ◽  
pp. 21-40
Author(s):  
Fiqhiyatun Naja ◽  
Nanik Kholifah
Keyword(s):  
Social Media ◽  
Regression Analysis ◽  
National Security ◽  
Linear Regression Analysis ◽  
Confirmation Bias ◽  
Sampling Technique ◽  
Fake News ◽  
R Value ◽  
Behavior Scale ◽  
Negative Impacts

The spread of fake news in Indonesia is now increasingly widespread, especially through social media, many negative impacts have been caused from the spread of fake information. Fake information can defame the reputation of others, cruel slander, fighting between groups, and disrupt national disintegration and even disrupt national security stability. Confirmation bias is one of the reasons why someone conducts or disseminates fake information, where individuals tend to only seek and receive information that is in accordance with their thoughts and ignores different opinions that might be true facts. This study aims to measure the effect of confirmation bias on lying behavior that is prevalent around us. The sample in this study was the millennial generation of social media users who are members of the PMII Pasuruan organization of 80 members, the samples were taken by purposive sampling technique. Data collection used a lying behavior scale and a confirmation bias scale compiled by the researchers using Likert answer method. The data were then analyzed using One Predictor Linear Regression Analysis. The results of data analysis resulted r value of 0.102228 with a significance value of 0.286. This shows that there is no significant correlation between confirmation bias and lying behavior.

scholarly journals STUDY OF THE DISTRIBUTION OF FAKE NEWS (HOAX) THROUGH SOCIAL MEDIA RELATED TO THE COVID-19 VACCINE AND PREVENTION EFFORTS TO SUPPORT MASS EXECUTION VACCINE IN INDONESIA

2021 ◽  
Vol 6 (24) ◽  
pp. 18-29
Author(s):  
Gideon Satria Putra Sugiyanto ◽  
Annisa Sabrina Nur Arrasy ◽  
Sweeta Melanie
Keyword(s):  
Social Media ◽  
Fake News ◽  
Group Discussions ◽  
The Public ◽  
Counter Narrative ◽  
Social Media Platforms ◽  
The Government ◽  
Negative Impacts ◽  
Media Channels ◽  
Depth Interviews

The COVID-19 pandemic has been going on in Indonesia for more than a year since the beginning of 2020. This pandemic has certainly had many negative impacts, both macro, and micro. The Indonesian government has made a lot of efforts to tackle this pandemic both operationally and in socialization to reduce the further spread of vaccine efforts throughout Indonesia. But unfortunately, there is the challenge of spreading fake news related to the COVID-19 vaccine that is troubling the public. The spread of fake news happened quite quickly with digital communication using social media. Research using qualitative methods examines the condition of socialization communication related to the COVID-19 vaccine, fake news, and efforts to overcome it through in-depth interviews and focus group discussions. The results of the study show that there has been a lot of communication and socialization carried out by the government regarding the COVID-19 vaccine but it has not been structured in one source and there is still minimal anticipation of fake news. As a result, a lot of fake news has spread widely in the community regarding COVID-19, starting from issues related to health and beyond health or safety. Efforts that can be made to minimize the spread of fake news and its dangers include collaboration with various stakeholders, mass and periodic socialization and education on various social media channels, strict penalties for spreading fake news, providing social media platforms or channels to file complaints, create educational content and creative counter-narrative.

Says Who? The Effects of Presentation Format and Source Rating on Fake News in Social Media

MIS Quarterly ◽  
2019 ◽  
Vol 43 (3) ◽  
pp. 1025-1039 ◽  
Author(s):  
Antino Kim ◽  
◽  
Alan R. Dennis ◽  
Keyword(s):  
Social Media ◽  
Presentation Format ◽  
Fake News

Identity concerns drive belief: The impact of partisan identity on the belief and dissemination of true and false news

2018 ◽  
Author(s):  
Andrea Pereira ◽  
Jay Joseph Van Bavel ◽  
Elizabeth Ann Harris
Keyword(s):  
Social Media ◽  
Political System ◽  
Political Identity ◽  
Social Goals ◽  
Fake News ◽  
Political Beliefs ◽  
System P ◽  
False News ◽  
Democrats And Republicans ◽  
The Impact

Political misinformation, often called “fake news”, represents a threat to our democracies because it impedes citizens from being appropriately informed. Evidence suggests that fake news spreads more rapidly than real news—especially when it contains political content. The present article tests three competing theoretical accounts that have been proposed to explain the rise and spread of political (fake) news: (1) the ideology hypothesis— people prefer news that bolsters their values and worldviews; (2) the confirmation bias hypothesis—people prefer news that fits their pre-existing stereotypical knowledge; and (3) the political identity hypothesis—people prefer news that allows their political in-group to fulfill certain social goals. We conducted three experiments in which American participants read news that concerned behaviors perpetrated by their political in-group or out-group and measured the extent to which they believed the news (Exp. 1, Exp. 2, Exp. 3), and were willing to share the news on social media (Exp. 2 and 3). Results revealed that Democrats and Republicans were both more likely to believe news about the value-upholding behavior of their in-group or the value-undermining behavior of their out-group, supporting a political identity hypothesis. However, although belief was positively correlated with willingness to share on social media in all conditions, we also found that Republicans were more likely to believe and want to share apolitical fake new. We discuss the implications for theoretical explanations of political beliefs and application of these concepts in in polarized political system.

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