scholarly journals Automatic Identification and Filtration of COVID-19 Misinformation

2021 ◽  
Vol 14 (4) ◽  
pp. 57
Author(s):  
Paras Gulati ◽  
Abiodun Adeyinka. O. ◽  
Saritha Ramkumar
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Automatic Identification ◽  
Fake News ◽  
Global Pandemic ◽  
Rapid Spread ◽  
Social Media Platforms ◽  
Data Explosion ◽  
Real Time Identification ◽  
The Right

The rapid spread of online fake news through some media platforms has increased over the last decade. Misinformation and disinformation of any kind is extensively propagated through social media platforms, some of the popular ones are Facebook and Twitter. With the present global pandemic ravaging the world and killing hundreds of thousands, getting fake news from these social media platforms can exacerbate the situation. Unfortunately, there has been a lot of misinformation and disinformation on COVID-19 virus implications of which has been disastrous for various people, countries, and economies. The right information is crucial in the fight against this pandemic and, in this age of data explosion, where TBs of data is generated every minute, near real time identification and tagging of misinformation is quintessential to minimize its consequences. In this paper, the authors use Natural Language Processing (NLP) based two-step approach to classify a tweet to be a potentially misinforming one or not. Firstly, COVID -19 tagged tweets were filtered based on the presence of keywords formulated from the list of common misinformation spread around the virus. Secondly, a deep neural network (RNN) trained on openly available real and fake news dataset was used to predict if the keyword filtered tweets were factual or misinformed.

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.

Governing Fake News: The Regulation of Social Media and the Right to Freedom of Expression in the Era of Emergency

2021 ◽  
pp. 1-41
Author(s):  
Donato VESE
Keyword(s):  
Social Media ◽  
Freedom Of Expression ◽  
Self Regulation ◽  
Public Law ◽  
Fake News ◽  
Human Right ◽  
Social Media Platforms ◽  
Definition Of ◽  
The Right ◽  
Media Censorship

Governments around the world are strictly regulating information on social media in the interests of addressing fake news. There is, however, a risk that the uncontrolled spread of information could increase the adverse effects of the COVID-19 health emergency through the influence of false and misleading news. Yet governments may well use health emergency regulation as a pretext for implementing draconian restrictions on the right to freedom of expression, as well as increasing social media censorship (ie chilling effects). This article seeks to challenge the stringent legislative and administrative measures governments have recently put in place in order to analyse their negative implications for the right to freedom of expression and to suggest different regulatory approaches in the context of public law. These controversial government policies are discussed in order to clarify why freedom of expression cannot be allowed to be jeopardised in the process of trying to manage fake news. Firstly, an analysis of the legal definition of fake news in academia is presented in order to establish the essential characteristics of the phenomenon (Section II). Secondly, the legislative and administrative measures implemented by governments at both international (Section III) and European Union (EU) levels (Section IV) are assessed, showing how they may undermine a core human right by curtailing freedom of expression. Then, starting from the premise of social media as a “watchdog” of democracy and moving on to the contention that fake news is a phenomenon of “mature” democracy, the article argues that public law already protects freedom of expression and ensures its effectiveness at the international and EU levels through some fundamental rules (Section V). There follows a discussion of the key regulatory approaches, and, as alternatives to government intervention, self-regulation and especially empowering users are proposed as strategies to effectively manage fake news by mitigating the risks of undue interference by regulators in the right to freedom of expression (Section VI). The article concludes by offering some remarks on the proposed solution and in particular by recommending the implementation of reliability ratings on social media platforms (Section VII).

scholarly journals The role of social media in delivering news related to the COVID-19 pandemic: Moroccan community as a case study

2021 ◽  
Vol 119 ◽  
pp. 07007
Author(s):  
Nezha Mejjad ◽  
Hanane Yaagoubi ◽  
Mourad Gourmaj ◽  
Aniss Moumen ◽  
Nabil Chakhchaoui ◽  
...  
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Survey Questionnaire ◽  
Fake News ◽  
Rapid Spread ◽  
Shared Information ◽  
Social Media Platforms ◽  
Source Of Information

The study aims to assess the Moroccan community’s using rate of social media, especially during the imposed lockdown, and analyze how the community is using and exploring the news published on Facebook. In this order, we prepared and shared a survey questionnaire among Facebook, Twitter and WhatsApp users. The obtained responses exhibit that only 5% of respondents share the news immediately without verifying the source, while 54 % share news only after verifying the source; the rest did not prefer to share COVID-19 related news. This may reflect the awareness level of the sampled population about the importance of verifying the source of information before sharing it, especially during such conditions. However, 64% of participants think that Social Media platforms are not sufficient and appropriate to warn and inform the population about this sanitary crisis as not all Moroccan citizens have access to the internet and do not use social media. Besides, the COVID-19 period has known a rapid spread of misinformation and fake news through these platforms, impacting community mental health. Although, it is recommended to consider warning people about the best practices and use of shared information through these platforms

scholarly journals Data mining applied in fake news classification through textual patterns

2020 ◽  
Author(s):  
Marcos Paulo Moraes ◽  
Jonice de Oliveira Sampaio ◽  
Anderson Cordeiro Charles
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Instant Messaging ◽  
Internet Access ◽  
Fake News ◽  
Security Issues ◽  
Automated Method ◽  
Rapid Spread ◽  
Long Time

Fake news has been around for a long time. But with the advancement of social media and internet access, fake news has become a bigger problem. Because of the rapid spread in social media and instant messaging applications, fake news can reach more people in less time by directly influencing democratic processes, leveraging security issues that sometimes lead to tragic ends. In order to promote a fast and automated method of fake news identification, in this study, we performed an analysis of false Brazilian news, identifying writing patterns through natural language processing and machine learning.

Facade of media and social media during COVID-19: A review

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 142-149 ◽  
Author(s):  
Kumar Chandan Srivastava ◽  
Deepti Shrivastava ◽  
Kumar Gaurav Chhabra ◽  
Waqar Naqvi ◽  
Arti Sahu
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Fake News ◽  
Health Authorities ◽  
Rapid Spread ◽  
Public Health Authorities ◽  
Social Media Platforms ◽  
The Government ◽  
Novel Coronavirus ◽  
Fear And Anxiety

A novel coronavirus (COVID-19) arose in Wuhan, China, in December 2019. Soon it spread to other countries worldwide to become a pandemic. Globally, governments enforced quarantine and social distancing measures to prevent the spread of the infection. Mass media and social media platforms played a crucial role in providing information regarding the Coronavirus. Since little is known about COVID-19, various fake news, misinformation and rumours spread across the digital media that panicked people into making panic decisions. The rapid spread of misinformation and stories via social media platforms such as Twitter, Facebook and YouTube became a vital concern of the government and public health authorities. Medical misinformation and unverifiable content about the COVID-19 pandemic are spreading on social media at an unprecedented pace. Mitigating the advent of rumours and misinformation during the COVID-19 epidemic is crucial, since misinformation and fake news creates panic, fear and anxiety among people, predisposing them to various mental health conditions. Instead of considering social media as a secondary medium, it should be utilised to convey important information. Besides, it allows citizens to address their queries directly. Several governments across the world have taken actions to contain the pandemic of misinformation, yet measures are required to prevent such communication complications.

scholarly journals Detection of Radicalisation and Extremism Online: A Survey

2022 ◽  
Author(s):  
Irfan Tanoli ◽  
Sebastião Pais ◽  
João Cordeiro ◽  
Muhammad Luqman Jamil
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Probabilistic Methods ◽  
Automatic Identification ◽  
News Sources ◽  
Independent Information ◽  
Social Media Platforms ◽  
The Subject

Abstract Introduction: Due to the lack of regulation, the large volume of user-generated online content reflects more closely the offline world than official news sources. Therefore, social media platforms have become an attractive space for anyone seeking independent information. One of the main goals of this work is to clarify concepts such as Extremism and Collective Radicalisation, Social Media, Sentiments/Emotions/Opinions Analysis, as well as the combinations of all of them. Methods: The automatic identification of extremism and collective radicalisation requires sophisticated Natural Language Processing (NLP) methods and resources, especially those dealing with opinions, emotions or sentiment analysis. Text mining and knowledge extraction are also crucial, in particular, directed toward social media and micro-blogging. Results: The present document comprehends a study on theoretical material, focusing on the main concepts of the subject, including the main problems and challenges, from the different areas that compose online radicalisation research. Understanding and detecting extremism and collective radicalism online has a connection to sentiment analysis and opinion mining. There are many barriers to understanding extremism and collective radicalisation; one is to differentiate between who is really engaged in the process and who is just eventually talking about it. Conclusions: The other focus of this work is to find the best ways to identify extremism and collective radicalisation on the internet, using sentiment analysis and focusing on probabilistic methods to create an unsupervised and language-independent approach.

Chinese Social Media Platforms and Microblogging in the Context of Public Diplomacy

Communicology ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 167-179
Author(s):  
E.S. Nadezhkina
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Public Diplomacy ◽  
User Generated Content ◽  
The Internet ◽  
Interactive Communication ◽  
Network Interaction ◽  
Social Media Platforms ◽  
The Right ◽  
Chinese Social Media

The term “digital public diplomacy” that appeared in the 21st century owes much to the emergence and development of the concept of Web 2.0 (interactive communication on the Internet). The principle of network interaction, in which the system becomes better with an increase in the number of users and the creation of user-generated content, made it possible to create social media platforms where news and entertainment content is created and moderated by the user. Such platforms have become an expression of the opinions of various groups of people in many countries of the world, including China. The Chinese segment of the Internet is “closed”, and many popular Western services are blocked in it. Studying the structure of Chinese social media platforms and microblogging, as well as analyzing targeted content is necessary to understand China’s public opinion, choose the right message channels and receive feedback for promoting the country’s public diplomacy. This paper reveals the main Chinese social media platforms and microblogging and provides the assessment of their popularity, as well as possibility of analyzing China’s public opinion based on “listening” to social media platforms and microblogging.

scholarly journals Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256696
Author(s):  
Anna Keuchenius ◽  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Language Processing ◽  
User Interaction ◽  
User Interactions ◽  
Online Interactions ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

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