scholarly journals Fact-checking on Twitter: An analysis of the hashtag #StopBulos

2021 ◽  
Vol 55 (2) ◽  
pp. e1371
Author(s):  
Roberto M. Lobato ◽  
Andrea Velandia Morales ◽  
Ángel Sánchez Rodríguez ◽  
Mar Montoya Lozano ◽  
Efraín García Sánchez
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Main Function ◽  
Virtual Networks ◽  
Fake News ◽  
False Information ◽  
Fact Checking ◽  
And Control ◽  
Social Media Network

The fact-checking is an important tool to improve the quality of the information that circulates in virtual networks. Although there are different fact-checking verification agencies, we also found some more informal strategies such as the use of the hashtag #Stopbulos. Thus, this research aims to characterize the #StopBulos hashtag on Twitter as a way to verify information and control the spread of fake news. The results showed that there was diversity among users and the themes of the tweets that included this hashtag, while the main function was to deny fake news. However, it was found that those who achieved greater dissemination were the users with the largest number of followers and institutional character. The implications of using the #StopBulos hashtag as a tool to identify false information on social networks are discussed. Keywords: fake news, post-truth, post-news, social media, network societies

scholarly journals JOURNALISM AND FACT-CHECKING: typification of sources used for checking and criteria for selecting fact-checked material – an analysis by Agência Lupa and Aos Fatos

2020 ◽  
Vol 16 (2) ◽  
pp. 368-393
Author(s):  
Daniel De Rezende Damasceno ◽  
Edgard Patrício
Keyword(s):  
Social Networks ◽  
Public Institutions ◽  
Behavioral Approach ◽  
The Political ◽  
Fake News ◽  
Redes Sociales ◽  
False Information ◽  
Fact Checking ◽  
Political Agents ◽  
Official Sources

Fact-checking was initially used to verify the factuality of information given by political agents. However, the proliferation of false information on social networks and concerns about the political use of spreading lies have led to fact-checking methodologies also being used to combat fake news. In terms of a cognitive and behavioral approach, Lazer et al. (2018) suggest there are some doubts as to how effective this methodology is. This article analyzes the performance of two Brazilian checking agencies, Aos Fatos and Agência Lupa. We demonstrate that, although checking discourse is directly related to the credibility of organizations, the agencies themselves do not lay out the criteria for selecting what is to be checked. The platforms that use this form of fact-checking mainly rely on data and studies provided by official sources and public institutions, once again compromising the credibility of the process.A prática de fact-checking foi iniciada para verificar a factualidade das informações nos discursos de agentes políticos. Mas a proliferação de informações falsas nas redes sociais da internet, e a preocupação com a disseminação de mentiras como instrumento político, fez com que as metodologias de fact-checking também fossem utilizadas para combater fake news. Levando em consideração uma abordagem cognitiva e comportamental, Lazer et al. (2018) alertam que existem dúvidas quanto à eficácia dessa utilização. Esse artigo analisa a atuação de duas agências brasileiras de checagem, Aos Fatos e Agência Lupa. Demonstramos que, apesar da checagem de discursos ter relação direta com a credibilidade das organizações, as próprias agências não explicitam os critérios que orientam a seleção do que é checado. E que nessa modalidade de checagem, as plataformas de fact-checking se valem, sobretudo, de dados e estudos fornecidos por fontes oficiais e instituições públicas, comprometendo mais uma vez a credibilidade do processo.La práctica de fact-checking inició para verificar la factualidad de las informaciones en los discursos de agentes políticos. Pero la proliferación de informaciones falsas en las redes sociales de internet, y la preocupación por la diseminación de mentiras como instrumento político, hizo que las metodologías de fact-checking también fueran utilizadas para combatir las fake news. Teniendo en cuenta un enfoque cognitivo y conductual, Lazer et al. (2018) advierten que existen dudas sobre la eficacia de esta utilización. Este artículo analiza la actuación de dos agencias brasileñas de chequeo, Aos Fatos y Agência Lupa. Demostramos que, aunque la verificación del discurso tiene una relación directa con la credibilidad de las organizaciones, las agencias mismas no detallan los criterios que guían la selección de lo que se verifica. Y que en este modo de verificación, las plataformas de verificación de hechos se basan principalmente en datos y estudios proporcionados por fuentes oficiales e instituciones públicas, comprometiendo una vez más la credibilidad del proceso.

scholarly journals The Trump Effect: With No Peer Review, How Do We Know What to Really Believe on Social Media?

2017 ◽  
Vol 30 (04) ◽  
pp. 270-276 ◽  
Author(s):  
Justin Brady ◽  
Molly Kelly ◽  
Sharon Stein
Keyword(s):  
Social Media ◽  
General Public ◽  
Fake News ◽  
False Information ◽  
Medical Field ◽  
Rapid Spread ◽  
Poor Differentiation ◽  
Fact Checking ◽  
Editorial Review ◽  
Political Election

AbstractSocial media is a source of news and information for an increasing portion of the general public and physicians. The recent political election was a vivid example of how social media can be used for the rapid spread of “fake news” and that posts on social media are not subject to fact-checking or editorial review. The medical field is susceptible to propagation of misinformation, with poor differentiation between authenticated and erroneous information. Due to the presence of social “bubbles,” surgeons may not be aware of the misinformation that patients are reading, and thus, it may be difficult to counteract the false information that is seen by the general public. Medical professionals may also be prone to unrecognized spread of misinformation and must be diligent to ensure the information they share is accurate.

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.

Communication in social networks of St. Petersburg as a means of adaptation of ethnocultural communities

Litera ◽  
2021 ◽  
pp. 38-55
Author(s):  
Rivaa Mukhammad Salem Alsalibi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Ethnic Communities ◽  
Sense Of Security ◽  
The Social ◽  
The Subject ◽  
Development Prospects

The subject of this research is the specifics, forms and functions of interaction in social media groups between the representatives of ethnic communities. The goal consists in determination of the role of social networks in adaptation of ethnocultural communities of St. Petersburg. The research is based on the polling technique for acquisition of information on the cognitive, emotional, and behavioral state of a person. The survey was conducted via distribution of questionnaires among the representatives of ethnic groups. The article also employs the method of systematic scientific observation over the social media groups, topic raised therein, as well as reading and analysis of the comments. The scientific novelty of this work consists in outlining of the nature, trends and development prospects of cross-cultural communications as the channel for ethnocultural interaction.  The main conclusions, which touch upon users from various ethnic communities who do not have enough experience in organization of activity of social media groups, demonstrate that it causes the loss of the sense of security, accumulation of prejudices and escalation of interethnic conflicts, as well as preference of the with restricted access, which contributes to lock down of the group and impedes adaptation in the accepting society. Stabilization of situation can be achieved by improvement of the quality of content posted in the social media, as well as level of their administration.

scholarly journals Fake and Real News detection Using Python

2020 ◽  
pp. 423-428
Author(s):  
Fakhra Akhtar ◽  
Faizan Ahmed Khan
Keyword(s):  
Social Media ◽  
Fake News ◽  
False Information ◽  
False Negatives ◽  
Mainstream Media ◽  
Social Media Platforms ◽  
The Masses ◽  
Negative Attributes ◽  
Entire Dataset ◽  
F Measure

<p>In the digital age, fake news has become a well-known phenomenon. The spread of false evidence is often used to confuse mainstream media and political opponents, and can lead to social media wars, hatred arguments and debates.Fake news is blurring the distinction between real and false information, and is often spread on social media resulting in negative views and opinions. Earlier Research describe the fact that false propaganda is used to create false stories on mainstream media in order to cause a revolt and tension among the masses The digital rights foundation DRF report, which builds on the experiences of 152 journalists and activists in Pakistan, presents that more than 88 % of the participants find social media platforms as the worst source for information, with Facebook being the absolute worst. The dataset used in this paper relates to Real and fake news detection. The objective of this paper is to determine the Accuracy , precision , of the entire dataset .The results are visualized in the form of graphs and the analysis was done using python. The results showed the fact that the dataset holds 95% of the accuracy. The number of actual predicted cases were 296. Results of this paper reveals that The accuracy of the model dataset is 95.26 % the precision results 95.79 % whereas recall and F-Measure shows 94.56% and 95.17% accuracy respectively.Whereas in predicted models there are 296 positive attributes , 308 negative attributes 17 false positives and 13 false negatives. This research recommends that authenticity of news should be analysed first instead of drafting an opinion, sharing fake news or false information is considered unethical journalists and news consumers both should act responsibly while sharing any news.</p>

scholarly journals A family of falsehoods: Deception, media hoaxes and fake news

2018 ◽  
Vol 39 (3) ◽  
pp. 350-361 ◽  
Author(s):  
Teri Finneman ◽  
Ryan J. Thomas
Keyword(s):  
Social Media ◽  
Fake News ◽  
Public Officials ◽  
False Information ◽  
Media Environment ◽  
News Organizations ◽  
Information Spread ◽  
History Of

“Fake news” became a concern for journalists in 2017 as news organizations sought to differentiate themselves from false information spread via social media, websites and public officials. This essay examines the history of media hoaxing and fake news to help provide context for the current U.S. media environment. In addition, definitions of the concepts are proposed to provide clarity for researchers and journalists trying to explain these phenomena.

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 Youth-led efforts against infodemic

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Uakkas ◽  
O El Omrani
Keyword(s):  
Social Media ◽  
Medical Students ◽  
Global Epidemic ◽  
Internet Radio ◽  
Slowing Down ◽  
Source Type ◽  
Public Figures ◽  
Social Media Platforms ◽  
Fact Checking

Abstract Problem The IFMSA, voicing the opinion of 1.3 million medical students from 129 countries, acknowledges the importance of health literacy in driving social change. Today, there is a global epidemic of misinformation, spreading rapidly through social media platforms and other outlets, posing a critical threat for public health due to the COVID-19 outbreak. Also, it threatens the possibility of slowing down the progression of the virus. The fight against such misinformation requires continuous provision of the most reliable and recent information. Description A global study was conducted by IFMSA, in collaboration with the WHO, composed of a survey to get data about all the organizations, institutions, NGOs, and other entities that focus on fact-checking and correcting misinformation about COVID-19. The survey was filled by medical students from end of April to end of May who reported name, type, scope of work, languages, primary funding source, type and source of information shared by the organization. Results We discovered 182 initiatives from 62 countries worldwide that verified information in 48 languages. Social media, internet, radio, SMS, printed media and hearsay were identified as the main sources of misinformation. Video podcasts with experts, regular social media updates and newsletters, were described as best practices, in addition to debunking myths on a regular basis and verifying statements by public figures. Also quality of fact-checking differed between initiatives. Lessons Data showed that myths and false information are spreading through different means from public figures to daily social media outlets. Fighting misinformation should use innovative and accessible approaches. There is urgent need for national initiatives and political engagement for myth-busting. IFMSA and WHO are following up by designing a platform to share fact-checking initiatives and recommendations openly, and by creating an AI system with Amazon to analyze articles in social media. Key messages Fact-checking and myth-busting are essential to limit the COVID-19 related infodemic spreading through different media and social media platforms, famous figures and others. Initiatives worldwide are doing fact-checking. Yet, the quality and quantity of available fact-checking differ between countries and there is a need for more universal good quality fact-checking.

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