Detecting Fake Accounts on Twitter Social Network Using Multi-Objective Hybrid Feature Selection Approach

Journal Title: Webology - Year 2020, Vol 17, Issue 1

Abstract

The frequency of fake accounts or social bots is considered as one of serious challenges of online social networks, which are controlled by automatic operators and often used for malicious purposes. The researchers have performed many efforts to identify these entities in online social networks, which Machine Learning classifier technique using a distinctive feature set is the most common one and feature selection is the principal process of such feature-based classifiers. The present study was carried out the fake accounts detection by using a multi-objective hybrid feature selection approach that helps the feature set selection with optimal classification performance. First, the candidate feature set was identified based on the highest relation to the target class and the least redundancy among the features by the Minimum Redundancy – Maximum Relevance algorithm (mRMR). Then, the stable feature set with the minimum number of features, which can achieve optimal performance, is selected as the final feature set for the detection operations. The proposed approach is tested on two datasets from Twitter's social network and the results were compared to the results of efficient existing methods. According to the results, the performance of the proposed classifier approach is higher compared to existing methods.

Authors and Affiliations

Reza Ramzanzadeh Rostami and Soheila Karbasi

Keywords

Related Articles

University networks in the context of their academic excellence and openness: A comparative study of leading Czech and German universities

A simple methodology of multi-dimensional vector analysis for the comparison of the academic performance and the openness of university networks of the identical dimension was developed, which is illustrated by the examp...

Egyptian and American Internet-Based Cross-Cultural Information Seeking Behavior. Part I: Research Instrument

This article is the first of three in an exploratory study of the cross-cultural, cross-language information-seeking (IS) behavior of a group of eighty-four academic and public reference librarians from Egypt and the U...

Foreigners' point of view towards collaboration with Iranian authors

Co-authorship is a process in which two or more authors share their ideas, resources and data to create a joint work. This study examines the motivations of non-Iranians who had a joint work with Iranian authors. A web q...

Digital Literacies: Concepts, Policies and Practices

With Digital literacies … a group of internationally renowned authors, under the capable editorship of Colin Lankshear & Michele Knobel, succeed in raising awareness for the vast scope and complexities of literacies that...

Gaming Method of Ontology Clusterization

In the real world an intelligent system often consists of intelligent agents, each having its own perspective and goal and executing the common task interacting with others. Those agents are often created by different de...

Download PDF file
  • EP ID EP687832
  • DOI 10.14704/WEB/V17I1/a204
  • Views 202
  • Downloads 0

How To Cite

Reza Ramzanzadeh Rostami and Soheila Karbasi (2020). Detecting Fake Accounts on Twitter Social Network Using Multi-Objective Hybrid Feature Selection Approach. Webology, 17(1), -. https://europub.co.uk/articles/-A-687832