scholarly journals Predicting COVID-19 Infection Groups using Social Networks and Machine Learning Algorithms

2020 ◽  
Author(s):  
Kyle Spurlock ◽  
Heba Elgazzar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Comparison of twitter spam detection using various machine learning algorithms

2017 ◽  
Vol 7 (1.3) ◽  
pp. 61
Author(s):  
M. Sangeetha ◽  
S. Nithyanantham ◽  
M. Jayanthi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Information Sharing ◽  
Online Social Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Critical Problem ◽  
Short Text ◽  
True Blue

Online Social Networks(OSNs) have mutual themes such as information sharing, person-to-person interaction and creation of shared and collaborative content.  Lots of micro blogging websites available like Twitter, Instagram, Tumblr. A standout amongst the most prominent online networking stages is Twitter. It has 313 million months to month dynamic clients which post of 500 million tweets for each day. Twitter allows users to send short text based messages with up to 140-character letters called "tweets". Enlisted clients can read and post tweets however the individuals who are unregistered can just read them. Due to the reputation it attracts the consideration of spammers for their vindictive points, for example, phishing true blue clients or spreading malevolent programming and promotes through URLs shared inside tweets, forcefully take after/unfollow valid clients and commandeer drifting subjects to draw in their consideration, proliferating obscenity. Twitter Spam has become a critical problem nowadays. By looking at the execution of an extensive variety of standard machine learning calculations, fundamentally expecting to distinguish the acceptable location execution in light of a lot of information by utilizing account-based and tweet content-based highlights.

Using E-Reputation for Sentiment Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 32-47
Author(s):  
Dhai Eddine Salhi ◽  
Abelkamel Tari ◽  
Mohand Tahar Kechadi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
Execution Time ◽  
Supervised Classification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mobile Operator ◽  
Strong Point

In a competitive world, companies are looking to gain a positive reputation through these clients. Electronic reputation is part of this reputation mainly in social networks, where everyone is free to express their opinion. Sentiment analysis of the data collected in these networks is very necessary to identify and know the reputation of a companies. This paper focused on one type of data, Twits on Twitter, where the authors analyzed them for the company Djezzy (mobile operator in Algeria), to know their satisfaction. The study is divided into two parts: The first part was the pre-processing phase, where this research filtered the Twits (eliminate useless words, use the tokenization) to keep the necessary information for a better accuracy. The second part was the application of machine learning algorithms (SVM and logistic regression) for a supervised classification since the results are binary. The strong point of this study was the possibility to run the chosen algorithms on a cloud in order to save execution time; the solution also supports the three languages: Arabic, English, and French.

scholarly journals Machine Learning Algorithms for Spam Detection in Social Networks

2019 ◽  
Vol 8 (S3) ◽  
pp. 41-44
Author(s):  
K. Nagaramani ◽  
K. Vandanarao ◽  
B. Mamatha
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Web Based ◽  
The Times ◽  
The Web

Most of the web based social systems like Face book, twitter, other mailing systems and social networks are developed for users to share their information, to interact and engage with the community. Most of the times these social networks will give some troubles to the users by spam messages, threaten messages, hackers and so on.. Many of the researchers worked on this and gave several approaches to detect the spam, hackers and other trouble shoots. In this paper we are discussing some tools to detect the spam messages in social networks. Here we are using RF, SVM, KNN and MLP machine learning algorithms across rapid miner and WEKA. It gives the better results when compared with other tools.

scholarly journals Adversarial Machine Learning on Social Network: A Survey

2021 ◽  
Vol 9 ◽  
Author(s):  
Sensen Guo ◽  
Xiaoyu Li ◽  
Zhiying Mu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Research Progress ◽  
Future Research

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

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