scholarly journals Spreading Disinformation on Facebook: Do Trust in Message Source, Risk Propensity, or Personality Affect the Organic Reach of “Fake News”?

2019 ◽  
Vol 5 (4) ◽  
pp. 205630511988865 ◽  
Author(s):  
Tom Buchanan ◽  
Vladlena Benson
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Preliminary Evidence ◽  
Fake News ◽  
Risk Propensity ◽  
Online Study ◽  
Message Source ◽  
Considerable Concern ◽  
Source Message

There is considerable concern about the propagation of disinformation through social media, particularly for political purposes. “Organic reach” has been found to be important in the propagation of disinformation on social networks. This is the phenomenon whereby social media users extend the audience for a piece of information: interacting with it, or sharing it with their wider networks, greatly increases the number of people the information reaches. This project evaluated the extent to which characteristics of the message source (how trustworthy they were) and the recipient (risk propensity and personality) influenced the organic reach of a potentially false message. In an online study, 357 Facebook users completed personality and risk propensity scales and rated their likelihood of interacting in various ways with a message posted by either a trustworthy or untrustworthy source. Message source impacted on overall organic reach, with messages from trusted sources being more likely to be propagated. Risk propensity did not influence reach. However, low scores on trait agreeableness predicted greater likelihood of interacting with a message. The findings provide preliminary evidence that both message source and recipient characteristics can potentially influence the spread of disinformation.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

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 Social Media and Fake News in the Post-Truth Era: The Manipulation of Politics in the Election Process

2020 ◽  
pp. 177-196
Author(s):  
Turgay Yerlikaya ◽  
Seca Toker
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Presidential Elections ◽  
Political Life ◽  
Main Theme ◽  
Fake News ◽  
Election Process ◽  
Use Of Social Media ◽  
The Uk ◽  
Positive Effect

This article focuses on how virtual social networks affect socio-political life. The main theme of the article is how social networks such as Facebook and Twitter can direct voters’ electoral preferences, especially during election time, through the dissemination of manipulative content and fake news. The use of social media, which was initially thought to have a positive effect on democratization, has been extensively discussed in recent years as threat to democracy. Examples from the 2016 U.S. presidential elections, France, Brexit, Germany, the UK and Turkey will be used to illustrate the risks that social networks pose to democracy, especially during election periods.

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

Poland

2018 ◽  
Author(s):  
Robert Gorwa
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Hate Speech ◽  
Public Life ◽  
Digital Tools ◽  
Social Media Marketing ◽  
Fake News ◽  
Political Campaign ◽  
Marketing Firms ◽  
Digital Politics

This chapter provides the first overview of political bots, fake accounts, and other false amplifiers in Poland. Based on extensive interviews with political campaign managers, journalists, activists, employees of social media marketing firms, and civil society groups, the chapter outlines the emergence of Polish digital politics, covering the energetic and hyper-partisan “troll wars,” the interaction of hate speech with modern platform algorithms, and the recent effects of “fake news” and various sources of apparent Russian disinformation. The chapter then explores the production and management of artificial identities on Facebook, Twitter, and other social networks—an industry confirmed to be active in Poland—and assesses how they can be deployed for both political and commercial purposes. Overall, the chapter provides evidence for a rich array of digital tools that are increasingly being used by various actors to exert influence over Polish politics and public life.

scholarly journals Citizen Debates in Social Networks about Didactic Resources for Mathematics

2021 ◽  
Vol 18 (21) ◽  
pp. 11686
Author(s):  
Rosa Valls-Carol ◽  
Garazi Álvarez-Guerrero ◽  
Garazi López de Aguileta ◽  
Álvaro Alonso ◽  
Marta Soler-Gallart
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Scientific Evidence ◽  
Social Media Analytics ◽  
Fake News ◽  
Or Education

Citizens are increasingly turning to social media to open up debates on issues of utmost importance, such as health or education. When analyzing citizens’ social media interactions on COVID-19, research has underlined the importance of sharing and spreading information based on scientific evidence rather than on fake news. However, whether and how citizens’ interactions in the field of education, particularly in mathematics, are based on scientific evidence remains underexplored. To contribute to filling this gap, this article presents an analysis of citizen debates in social networks about didactic resources for mathematics. Through social media analytics, 136,964 posts were extracted from Reddit, Instagram, Twitter and Facebook, of which 1755 were analyzed. Results show that out of the 213 posts of citizen debates on didactic resources for mathematics, only two contained scientific evidence and eight claimed to contain scientific evidence. These findings highlight the importance of promoting actions to encourage citizen debates around didactic resources for mathematics based on scientific evidence.

Role of Social Media Use in Democracy

2020 ◽  
Vol 36 (2) ◽  
pp. 55-59
Author(s):  
Neelam Kumari ◽  
Joginder Singh Malik ◽  
Dangi Pooja Arun
Keyword(s):  
Social Networks ◽  
Social Media ◽  
New Media ◽  
Fake News ◽  
Social Lives ◽  
Way Of Thinking ◽  
The Government ◽  
Democratic Systems ◽  
Use Of Social Media

Social networks such as Twitter, Facebook, and Google hold the potential to alter civic engagement, thus essentially hijacking democracy, by influencing individuals towards a particular way of thinking. Today, social media plays a crucial role in facilitating and transmitting content related to all the matters that have larger effect on public opinions and democracy. Due to higher use of social media among new generations, they are exposed to politics more frequently, and in a way that is integrated into their social lives. New media hailed as vehicles for providing a voice to the voiceless. But the restrictions imposed by the government on social media sites and internet services, while allowing only that content that are government friendly amounts to compelled speech. This paper explores how social media have become a platform for fake news and propaganda to influence certain audiences towards a particular way of thinking. When it comes to healthy democratic networks, it is important that the news remains true so it doesn’t affect people levels of trust. A certain amount of trust is very crucial for healthy and well-functioning democratic systems.

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 New technologies, Social Media and Democracy

2021 ◽  
Vol 20 (41) ◽  
pp. 253-274
Author(s):  
Edilene Lôbo ◽  
José Luis Bolzan de Morais
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Political Parties ◽  
New Technologies ◽  
Fake News ◽  
Human Right ◽  
Traditional Media ◽  
Free Thought ◽  
The Impact

This article aims to consider the impact of new technologies in the Brazilian elections of 2018, questioning about the possibilities of its transformation with the prominent use of social networks to directly connect citizens and candidates, without the customary intervention of political parties and traditional media. It also aims to discuss the role of fake news in the electoral process and the means to fight it, so it does not denature the free thought formation as a human right essential to the practice of citizenship in the new digital age.

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.

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