Document Details

Document Type : Thesis 
Document Title :
Detecting Spam Content in Arabic Tweets
الكشف عن المحتوى المزعج في التغريدات العربية
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : The evolution of information has led to an increased intensity in its flow, especially in social communication networks. Twitter, for example, has become an incredibly popular platform for information sharing and opinion expression. Unfortunately, spammers have exploited this situation by promoting their messages and seeking malicious purposes. Various researchers have struggled to tackle this problem, proposing many techniques for the spam detection process. While these studies have made important contributions to the field, they remain limited in their linguistic scope. The current body of literature has focused on English texts with few resources available in the Arabic language. Accordingly, this study proposed an effective method for detecting spam content in Arabic tweets, using a supervised machine learning system. This work employed a set of language-specific features with other features in order to attain a high level of accuracy in the detection process. The proposed approach was evaluated using a real-life dataset and standard evaluation measures. In conclusion, our study shows that the spam content can be detected by using Naïve Bayes classifier with accuracy 94%. 
Supervisor : Dr. Mohammed Basheri 
Thesis Type : Master Thesis 
Publishing Year : 1440 AH
2019 AD 
Added Date : Tuesday, August 27, 2019 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
ابتسام محمد القحطانيAl-Qahtani, Ebtesam MohammedResearcherMaster 

Files

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 44934.pdf pdf 

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