SentiFilter: A Personalized Filtering Model for Arabic Semi-Spam Content based on Sentimental and Behavioral Analysis
TLDR
The proposed SentiFilter model is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics and is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.Abstract:
Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual’s needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users’ explicit feedback. Findings also indicate that sentiment of users’ comments does not reflect users’ perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users’ explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.read more
Citations
More filters
Journal ArticleDOI
Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions
Malak Saleh Aljabri,Hanan S. Altamimi,Shahd A. Albelali,Maimunah AL-Harbi,Haya T. Alhuraib,Najd K. Alotaibi,Amal Alahmadi,Fahd Alhaidari,Rami Mustafa A. Mohammad,Khaled Salah +9 more
TL;DR: In this article , the authors provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used.
Journal ArticleDOI
Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions
TL;DR: In this paper , the authors provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used.
Journal ArticleDOI
Employing Behavioral Analysis to Predict User Attitude towards Unwanted Content in Online Social Network Services: The Case of Makkah Region in Saudi Arabia
TL;DR: This paper investigates the relationship between user attitude and user behavior among users from the Makkah region in Saudi Arabia to determine the applicability of considering users’ behaviors, as indicators of their attitudes towards unwanted content, and proposes a semi-explicit attitude measure to infer user attitude from user-selected examples.
References
More filters
Book
Support Vector Machines
TL;DR: This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications and provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature.
Proceedings ArticleDOI
Mean Birds: Detecting Aggression and Bullying on Twitter
Despoina Chatzakou,Nicolas Kourtellis,Jeremy Blackburn,Emiliano De Cristofaro,Gianluca Stringhini,Athena Vakali +5 more
TL;DR: The authors proposed a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users, finding that bullies are relatively popular and tend to include more negativity in their posts.
Journal ArticleDOI
Machine learning for email spam filtering: review, approaches and open research problems
Emmanuel Gbenga Dada,Joseph Stephen Bassi,Haruna Chiroma,Shafi’i Muhammad Abdulhamid,Adebayo Olusola Adetunmbi,Opeyemi Emmanuel Ajibuwa +5 more
TL;DR: A systematic review of some of the popular machine learning based email spam filtering approaches and recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.
Proceedings ArticleDOI
Abusive Language Detection on Arabic Social Media
TL;DR: A list of obscene words and hashtags is extracted using common patterns used in offensive and rude communications and Twitter users are classified according to whether they use any of these words or not in their tweets.
Proceedings ArticleDOI
Social Spammer Detection with Sentiment Information
TL;DR: Experimental results on real-world social media datasets show the superior performance of the proposed framework by harnessing sentiment analysis for social spammer detection.