Twitter Spam Detection: A Systematic Review

by   Sepideh Bazzaz Abkenar, et al.

Nowadays, with the rise of Internet access and mobile devices around the globe, more people are using social networks for collaboration and receiving real-time information. Twitter, the microblogging that is becoming a critical source of communication and news propagation, has grabbed the attention of spammers to distract users. So far, researchers have introduced various defense techniques to detect spams and combat spammer activities on Twitter. To overcome this problem, in recent years, many novel techniques have been offered by researchers, which have greatly enhanced the spam detection performance. Therefore, it raises a motivation to conduct a systematic review about different approaches of spam detection on Twitter. This review focuses on comparing the existing research techniques on Twitter spam detection systematically. Literature review analysis reveals that most of the existing methods rely on Machine Learning-based algorithms. Among these Machine Learning algorithms, the major differences are related to various feature selection methods. Hence, we propose a taxonomy based on different feature selection methods and analyses, namely content analysis, user analysis, tweet analysis, network analysis, and hybrid analysis. Then, we present numerical analyses and comparative studies on current approaches, coming up with open challenges that help researchers develop solutions in this topic.


page 2

page 4

page 12


Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed

In the last decade, social networks became most popular medium for commu...

Social Fraud Detection Review: Methods, Challenges and Analysis

Social reviews have dominated the web and become a plausible source of p...

Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination

This paper covers the two approaches for sentiment analysis: i) lexicon ...

A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks

Fake news and misinformation are a matter of concern for people around t...

Research Status of Deep Learning Methods for Rumor Detection

To manage the rumors in social media to reduce the harm of rumors in soc...

Feature Selection on Noisy Twitter Short Text Messages for Language Identification

The task of written language identification involves typically the detec...

Sentiment and position-taking analysis of parliamentary debates: A systematic literature review

Parliamentary and legislative debate transcripts provide access to infor...

Please sign up or login with your details

Forgot password? Click here to reset