scholarly journals Understanding the Strategies of Creating Fake News in Social Media

2020 ◽  
Author(s):  
Dinusha Vatsalan ◽  
Nalin A.G. Arachchilage
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online News ◽  
Data Sets ◽  
Fake News ◽  
Threat Model ◽  
Social Media Platforms ◽  
Model Understanding ◽  
Cyber Criminals

Social media giants like Facebook are struggling to keep up with fake news, in the light of the fact that disinformation diffuses at lightning speed. For example, the COVID-19 (i.e. Coronavirus) pandemic is testing the citizens' ability to distinguish real news from falsifying facts (i.e. disinformation). Cyber-criminals take advantage of the inability to cope with fake news diffusion on social media platforms. Fake news, created as a means to manipulate readers to perform various malicious IT activities such as clicking on fraudulent links associated with the fake news/posts. However, no previous study has investigated the strategies used to create fake news on social media. Therefore, we have analysed five data-sets using Machine Learning (ML) that contain online news articles (i.e. both fake and legitimate news) to investigate strategies of creating fake news on social media platforms. Our study findings revealed a threat model understanding strategies of crafting fake news which may highly likely diffuse on social media platforms.

scholarly journals Understanding the Strategies of Creating Fake News in Social Media

2020 ◽  
Author(s):  
Dinusha Vatsalan ◽  
Jeyakumar Samantha Tharani ◽  
Nalin A.G. Arachchilage
Keyword(s):  
Social Media ◽  
Online News ◽  
Data Sets ◽  
Fake News ◽  
Threat Model ◽  
Social Media Platforms ◽  
Model Understanding ◽  
Cyber Criminals

Social media giants like Facebook are struggling to keep up with fake news, in the light of the fact that disinformation diffuses at lightning speed. For example, the COVID-19 (i.e. Coronavirus) pandemic is testing the citizens' ability to distinguish real news from falsifying facts (i.e. disinformation). Cyber-criminals take advantage of the inability to cope with fake news diffusion on social media platforms. Fake news, crafted as a means to manipulate readers to perform various malicious IT activities. However, no previous study has investigated the strategies used to create fake news on social media. Therefore, we have analysed five data-sets that contain online news articles (i.e. both fake and legitimate news) to investigate strategies of crafting fake news on social media platforms. Our study findings revealed a threat model understanding strategies of crafting fake news which may highly likely diffuse on social media platforms.

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.

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 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.

scholarly journals Fake News Detection using Machine Learning

2021 ◽  
Vol 40 ◽  
pp. 03003
Author(s):  
Prasad Kulkarni ◽  
Suyash Karwande ◽  
Rhucha Keskar ◽  
Prashant Kale ◽  
Sumitra Iyer
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Extraction ◽  
Fake News ◽  
Detection Model ◽  
News Websites ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Short Time ◽  
The Given

Everyone depends upon various online resources for news in this modern age, where the internet is pervasive. As the use of social media platforms such as Facebook, Twitter, and others has increased, news spreads quickly among millions of users in a short time. The consequences of Fake news are far-reaching, from swaying election outcomes in favor of certain candidates to creating biased opinions. WhatsApp, Instagram, and many other social media platforms are the main source for spreading fake news. This work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data are Random Forest, Logistic Regression, Decision Tree, KNN and Gradient Booster. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.

scholarly journals Detecting Fake News Using Social Media Platforms

2021 ◽  
Vol 9 (10) ◽  
pp. 1115-1120
Author(s):  
Prof. B. J. Deokate
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Short Term Memory ◽  
Fake News ◽  
Online Social Media ◽  
News Websites ◽  
Computer Scientists ◽  
Social Media Platforms

Abstract: Fake news detection is an interesting topic for computer scientists and social science. The recent growth of the online social media fake news has great impact to the society. There is a huge information from disparate sources among various users around the world. Social media platforms like Facebook, WhatsApp and Twitter are one of the most popular applications that are able to deliver appealing data in timely manner. Developing a technique that can detect fake news from these platforms is becoming a necessary and challenging task. This project proposes a machine learning method which can identify the credibility of an article that will be extracted from the Uniform Resource Locator (URL) entered by the user on the front end of a website. The project uses the five widely used machine learning methods: Long Short Term Memory (LSTM), Random Forest (random tree), Random Forest (decision tree), Decision Tree and Neural Network to give a response telling the user about the credibility of that news. Our initial definition of reliable and unreliable will rely on the human-curated data http://opensources.co. OpenSources.co has a list of about 20 credible news websites and a list of over 700 fake news websites. The proposed model is working well and defining the correctness of results upto 87.45% of accuracy. Keywords: Data Pre-processing, Fake news datasets, ML algorithms, Prediction.

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

Opportunities for Qualitative Analysis of Social Media Platforms in Dietetic Research and Practice

2020 ◽  
pp. 1-5
Author(s):  
Meghan Lynch ◽  
Irena Knezevic ◽  
Kennedy Laborde Ryan
Keyword(s):  
Social Media ◽  
Low Cost ◽  
Online News ◽  
News Article ◽  
Discussion Forums ◽  
Research And Practice ◽  
The Public ◽  
Related Data ◽  
Social Media Platforms ◽  
Article Comment

To date, most qualitative knowledge about individual eating patterns and the food environment has been derived from traditional data collection methods, such as interviews, focus groups, and observations. However, there currently exists a large source of nutrition-related data in social media discussions that have the potential to provide opportunities to improve dietetic research and practice. Qualitative social media discussion analysis offers a new tool for dietetic researchers and practitioners to gather insights into how the public discusses various nutrition-related topics. We first consider how social media discussion data come with significant advantages including low-cost access to timely ways to gather insights from the public, while also cautioning that social media data have limitations (e.g., difficulty verifying demographic information). We then outline 3 types of social media discussion platforms in particular: (i) online news article comment sections, (ii) food and nutrition blogs, and (iii) discussion forums. We discuss how each different type of social media offers unique insights and provide a specific example from our own research using each platform. We contend that social media discussions can contribute positively to dietetic research and practice.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

Sign in / Sign up

Export Citation Format

Share Document