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 Prediction of Fake Tweets Using Machine Learning Algorithms

2021 ◽  
Author(s):  
M. Sreedevi ◽  
G. Vijay Kumar ◽  
K. Kiran Kumar ◽  
B. Aruna ◽  
Arvind Yadav
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Internet Media ◽  
Learning Techniques ◽  
False News

Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.

scholarly journals Fake News Detection on Social Media using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 940-943
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Huge Number ◽  
Web Based ◽  
Distributed Information ◽  
Learning Techniques ◽  
Day By Day

: Web based life administrations, as Facebook and Twitter, Renren, Instagram, and linkedin have recently become an enormous and persistent supply of day by day news. These stages give a huge number of clients and give numerous administrations, for example, content arrangement and distributing. Not all distributed information via internet based medium is dependable and exact. Numerous individuals attempt to distribute fake and mistaken news so as to control general conclusion. Counterfeit news might be intentionally made to advance money related, political and public premiums, and can lead to unsafe effects on people convictions and choices.. In this paper we examine different systems for recognizing counterfeit information via internet based networking medium. Our point is to locate a dependable and right model that arranges a given article as fake or genuine. For identification of fake articles we use machine learning algorithms.

Application of Machine Learning Techniques in Healthcare

2020 ◽  
pp. 289-304 ◽  
Author(s):  
Sathya D. ◽  
Sudha V. ◽  
Jagadeesan D.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Or Efficiency ◽  
Game Development ◽  
Learning Techniques ◽  
Media Services

Machine learning is an approach of artificial intelligence (AI) where the machine can automatically learn and improve its performance on experience. It is not explicitly programmed; the data is fed into the generic algorithm and it builds logic based on the data provided. Traditional algorithms have to define new rules or massive rules when the pattern varies or the number of patterns increases, which reduces the accuracy or efficiency of the algorithms. But the machine learning algorithms learn new input patterns capable of handling complex situations while maintaining accuracy and efficiency. Due to its effectual benefits, machine learning algorithms are used in various domains like healthcare, industries, travel, game development, social media services, robotics, and surveillance and information security. In this chapter, the application of machine learning technique in healthcare is discussed in detail.

scholarly journals Fake News Detection: A Survey of Techniques

2020 ◽  
Vol 9 (9) ◽  
pp. 383-386
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Fake News ◽  
False Information ◽  
News Stories ◽  
Learning Techniques ◽  
Negative Impacts ◽  
To Come

The extensive spread of fake news (low quality news with intentionally false information) has the potential for extremely negative impacts on individuals, society and particular in the political world. Therefore, fake news detection on social media has recently become an emerging research which is attracting tremendous attention. Detection of false information is technically challenging for several reasons. Use of various social media tools, content is easily generated and quickly spread, which lead to a large volume of content to analyze. Online information is very wide spread, which cover a large number of subjects, which contributes complexity to this task. The application of machine learning techniques are explored for the detection of ‘fake news’ that come from non-reputable sources which mislead real news stories. The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information. This paper performs survey of Machine learning techniques which is mainly used for false detection and provides easier way to generate results.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

scholarly journals Exploring Text-based Emotions Recognition Machine Learning Techniques on Social Media Conversation

2021 ◽  
Vol 179 ◽  
pp. 821-828
Author(s):  
Andry Chowanda ◽  
Rhio Sutoyo ◽  
Meiliana ◽  
Sansiri Tanachutiwat
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Techniques ◽  
Learning Techniques
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