scholarly journals Combating Fake News, Misinformation, and Machine Learning Generated Fakes: Insight's from the Islamic Ethical Tradition

ICR Journal ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 189-212
Author(s):  
Talat Zubair ◽  
Amana Raquib ◽  
Junaid Qadir
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
World Wide ◽  
Security And Privacy ◽  
Fake News ◽  
Islamic Ethics ◽  
The World ◽  
Social Media Platforms ◽  
Filter Bubbles

The growing trend of sharing and acquiring news through social media platforms and the World Wide Web has impacted individuals as well as societies, spreading misinformation and disinformation. This trend—along with rapid developments in the field of machine learning, particularly with the emergence of techniques such as deep learning that can be used to generate data—has grave political, social, ethical, security, and privacy implications for society. This paper discusses the technologies that have led to the rise of problems such as fake news articles, filter bubbles, social media bots, and deep-fake videos, and their implications, while providing insights from the Islamic ethical tradition that can aid in mitigating them. We view these technologies and artifacts through the Islamic lens, concluding that they violate the commandment of spreading truth and countering falsehood. We present a set of guidelines, with reference to Qur‘anic and Prophetic teachings and the practices of the early Muslim scholars, on countering deception, putting forward ideas on developing these technologies while keeping Islamic ethics in perspective.

scholarly journals A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

2021 ◽  
Vol 9 (8) ◽  
pp. 1046-1050
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
New Technologies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Fake News ◽  
Detection Techniques ◽  
Social Media Platforms ◽  
Extensive Growth

creasing number of social media platforms, emerging new technologies, and population growth which results in the rate of using social media has increased rapidly. With an increasing number of users on online platforms comes to a variety of problems like fake news. The extensive growth of fake news on social media can have a serious impact on the real world and became a cause of concern for net users and governments all over the world. Distinguishing between real news and fake news becoming more challenging. The amount of fake news has become a disguise. In this paper, we have done a survey on detection techniques for fake news using Algorithms and Deep learning techniques. We have compared machine learning algorithms like Naïve-Bayes, Decision tree, SVM, Adaboost, etc. Comparing the accuracy

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.

Spiritualism and the Resurgence of Fake News

2020 ◽  
pp. 222-237
Author(s):  
Kristy A. Hesketh
Keyword(s):  
Social Media ◽  
Mass Media ◽  
Fake News ◽  
News Stories ◽  
News Reports ◽  
The World ◽  
Spirit World ◽  
Social Media Platforms ◽  
Media Platform ◽  
False News

This chapter explores the Spiritualist movement and its rapid growth due to the formation of mass media and compares these events with the current rise of fake news in the mass media. The technology of cheaper publications created a media platform that featured stories about Spiritualist mediums and communications with the spirit world. These articles were published in newspapers next to regular news creating a blurred line between real and hoax news stories. Laws were later created to address instances of fraud that occurred in the medium industry. Today, social media platforms provide a similar vessel for the spread of fake news. Online fake news is published alongside legitimate news reports leaving readers unable to differentiate between real and fake articles. Around the world countries are actioning initiatives to address the proliferation of false news to prevent the spread of misinformation. This chapter compares the parallels between these events, how hoaxes and fake news begin and spread, and examines the measures governments are taking to curb the growth of misinformation.

Deep Learning and Machine Learning Techniques for Analyzing Travelers' Online Reviews

2022 ◽  
pp. 20-39
Author(s):  
Elliot Mbunge ◽  
Benhildah Muchemwa
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Hospitality Industry ◽  
Learning Models ◽  
Online Data ◽  
Social Media Platforms ◽  
Using Data ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Social media platforms play a tremendous role in the tourism and hospitality industry. Social media platforms are increasingly becoming a source of information. The complexity and increasing size of tourists' online data make it difficult to extract meaningful insights using traditional models. Therefore, this scoping and comprehensive review aimed to analyze machine learning and deep learning models applied to model tourism data. The study revealed that deep learning and machine learning models are used for forecasting and predicting tourism demand using data from search query data, Google trends, and social media platforms. Also, the study revealed that data-driven models can assist managers and policymakers in mapping and segmenting tourism hotspots and attractions and predicting revenue that is likely to be generated, exploring targeting marketing, segmenting tourists based on their spending patterns, lifestyle, and age group. However, hybrid deep learning models such as inceptionV3, MobilenetsV3, and YOLOv4 are not yet explored in the tourism and hospitality industry.

Conclusion

2018 ◽  
pp. 235-242
Author(s):  
Steven McKevitt
Keyword(s):  
Social Media ◽  
World Wide Web ◽  
World Wide ◽  
New Labour ◽  
General Election ◽  
Public Response ◽  
The Public ◽  
The World ◽  
Social Media Platforms

The Conclusion draws together the main findings of the study. Britain in 1997 was a far more emotional and expressive society. This is highlighted by two events: the public response to the death of Diana, Princess of Wales, and the success of New Labour in the general election. The extent to which persuasion industries were responsible for bringing these changes about is discussed. There is a discussion of some areas for further study: the subsequent impact of the World Wide Web and social media platforms; persuasion aimed at children/juvenile consumption, and the development of single British brand throughout the period—for example, Virgin.

scholarly journals The battle to end fake news: A qualitative content analysis of Facebook announcements on how it combats disinformation

2019 ◽  
Vol 82 (1) ◽  
pp. 60-81 ◽  
Author(s):  
Petros Iosifidis ◽  
Nicholas Nicoli
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Culture Media ◽  
Qualitative Content Analysis ◽  
Digital Culture ◽  
Fake News ◽  
Digital Democracy ◽  
Descriptive Account ◽  
The World ◽  
Social Media Platforms

The recent spread of online disinformation has been profound and has played a central role in the growth of populist sentiments around the world. Facilitating its progression has been politically and economically motivated culprits who have ostensibly taken advantage of the digital freedoms available to them. At the heart of these freedoms lie social media organisations that only a few years earlier techno-optimists were identifying as catalysts of an enhanced digital democracy. In order to curtail the erosion of information, policy reform will no doubt be essential. The UK's Department of Digital, Culture, Media and Sport Disinformation and ‘fake news’ Report and Cairncross Review, and the European Commission's Report on Disinformation are three recent examples seeking to investigate how precisely such reform policy might be implemented. Just as important is how social media organisations take on more responsibility and apply self-regulating mechanisms that stifle disinformation across their platforms (something the aforementioned reports identify). Doing so will go a long way in restoring legitimacy in these significant institutions. Facebook (which includes Instagram and Whatsapp), is the largest social media organisation in the world and must primarily bear the burden of this responsibility. The purpose of this article is to offer a descriptive account of Facebook's public announcements regarding how it tackles disinformation and fake news. Based on a qualitative content analysis covering the period November 16th 2016–March 4th 2019, this article will set out some groundwork on how to hold social media platforms more accountable for how they handle disinformation.

scholarly journals A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 ◽  
Vol 10 (1) ◽  
pp. 256-261
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
General Public ◽  
Classification Model ◽  
Fake News ◽  
Random Forest Classification ◽  
Forest Classification ◽  
The World ◽  
The Impact

Today the world is gripped with fear of the most infectious disease which was caused by a newly discovered virus namely corona and thus termed as COVID-19. This is a large group of viruses which severely affects humans. The world bears testimony to its contagious nature and rapidity of spreading the illness. 50l people got infected and 30l people died due to this pandemic all around the world. This made a wide impact for people to fear the epidemic around them. The death rate of male is more compared to female. This Pandemic news has caught the attention of the world and gained its momentum in almost all the media platforms. There was an array of creating and spreading of true as well as fake news about COVID-19 in the social media, which has become popular and a major concern to the general public who access it. Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base. At the time it is undeniable that spreading of such fake news in and around creates lots of confusion and fear to the public. To stop all such rumors detection of fake news has become utmost important. To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model. In the context of the COVID-19 pandemic, we investigated and implemented by collecting the training data and trained a machine learning model by using various machine learning algorithms to automatically detect the fake news about the Corona Virus. The machine learning algorithm used in this investigation is Naïve Bayes classifier and Random forest classification algorithm for the best results. A separate model for each classifier is created after the data preparation and feature extraction Techniques. The results obtained are compared and examined accurately to evaluate the accurate model. Our experiments on a benchmark dataset with random forest classification model showed a promising results with an overall accuracy of 94.06%. This experimental evaluation will prevent the general public to keep themselves out of their fear and to know and understand the impact of fast-spreading as well as misleading fake news.

scholarly journals The Negative Impact of Social Media during COVID-19

2021 ◽  
Vol 58 (2) ◽  
pp. 495-507
Author(s):  
Astha Kumari
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Negative Impact ◽  
Modern World ◽  
Fake News ◽  
The World ◽  
Negative Effect ◽  
Social Media Platforms ◽  
The Government ◽  
World Information

It has been observed that social media platforms have had both a positive and negative effect on how India has dealt with the COVID 19 pandemic. As the coronavirus took over the world, many took to social media to learn about how the virus spreads and what it is. Although this helped inform everyone on how to take precautions against this deadly virus, a lot of the information that users were reading was not verified or fact-checked and labeled as "fake news". In the modern world, information is spread very quickly through a variety of social media platforms. Because of this, there was widespread panic even before the COVID-19 virus had even reached India. Many citizens bought an excessive surplus of supplies such as masks, hand sanitizers, and food, which ultimately led to a shortage of these supplies for the 1.3 billion people in this country. The shortage of supplies along with the lockdown process which severely impacted the economy has led to an increase in price to the majority of essential products such as food, hand sanitizers, masks, etc. The most affected were the average day workers. Social media has caused widespread panic and hogging of essential supplies along with false facts of the virus itself, however, there are some things that we have benefited from due to social media. For example, social media has shown us the importance of social distancing and activities that we can do to keep our mental health in check while under lockdown. In short, I believe social media should be regulated and kept under watch by the government in certain aspects when it comes to spreading information about pandemics like covid19. If regulated properly we can avoid mass panic and anarchy and will be able to survive this pandemic as one.

scholarly journals People's Councils for Ethical Machine Learning

2018 ◽  
Author(s):  
Dan McQuillan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Knowledge Production ◽  
The Core ◽  
Machine Learning Methods ◽  
The Family ◽  
The World ◽  
Social Media Platforms ◽  
Drone Warfare

Machine learning is a form of knowledge production native to the era of big data. It is at the core of social media platforms and everyday interactions. It is also being rapidly adopted for research and discovery across academia, business and government. This paper will explore the way the affordances of machine learning itself, and the forms of social apparatus that it becomes a part of, will potentially erode ethics and draw us in to a drone-like perspective. Unconstrained machine learning enables and delimits our knowledge of the world in particular ways: the abstractions and operations of machine learning produce a ‘view from above’ whose consequences for both ethics and legality parallel the dilemmas of drone warfare. The family of machine learning methods is not somehow inherently bad or dangerous, nor does implementing them signal any intent to cause harm. Nevertheless, the machine learning assemblage produces a targeting gaze whose algorithms obfuscate the legality of its judgements, and whose iterations threaten to create both specific injustices and broader states of exception. Given the urgent need to provide some kind of balance before machine learning becomes embedded everywhere, this paper proposes people’s councils as a way to contest machinic judgements and reassert openness and discourse.

scholarly journals Detecting Fake News using Machine Learning: A Systematic Literature Review

2021 ◽  
Vol 58 (1) ◽  
pp. 1932-1939
Author(s):  
Alim Al Ayub Ahmed Et al.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Political Party ◽  
Fake News ◽  
Machine Learning Classifiers ◽  
Online Platforms ◽  
Learning Classifiers ◽  
Social Media Platforms

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through these online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These fake news can be a propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting the fake news is described in this systematic literature review.

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