@article {Zarnoufi2022223,
	title = {Classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media},
	journal = {Communications in Computer and Information Science},
	volume = {1577 CCIS},
	year = {2022},
	note = {cited By 0},
	pages = {223-235},
	abstract = {Cyber-violence is a largely addressed problem in e-health researches, its focus is the detection of harmful behavior from the online user-generated text in order to prevent and protect victims. In this work, we tackle the problem of Social Media (SM) text analysis to detect the harmful content that is the common characteristic of cyber-violence acts. For that, we use classical Machine Learning (ML) based on user psychological features that we compare with Deep Learning (DL) techniques in a small dataset setting. The results were in favor of classical ML. The findings highlight that psychological characteristics extracted from user-generated text are strong predictors of his harmful behavior. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Behavioral research, Classical machine learning, Cybe-violence, Deep learning, E health, Ehealth, Feature engineerings, Harmful behavior, Machine-learning, social media, Social networking (online), User-generated},
	doi = {10.1007/978-3-031-04447-2_15},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128968964\&doi=10.1007\%2f978-3-031-04447-2_15\&partnerID=40\&md5=70577a9ac5b248762e97757da4349369},
	author = {Zarnoufi, R. and Abik, M.}
}
@article {Zarnoufi2020182,
	title = {AI to prevent cyber-violence: Harmful behaviour detection in social media},
	journal = {International Journal of High Performance Systems Architecture},
	volume = {9},
	number = {4},
	year = {2020},
	note = {cited By 1},
	pages = {182-191},
	abstract = {Social media has allowed people to communicate freely. This total freedom has led to the emergence of cyber-violence with a growing number of victims. Many researches in psychology and e-health have been conducted to detect the act of cyber-violence. In computational field, most of works have focused on multiple aspects of cyber-violence, but none of them, to our knowledge, have studied the perpetrator{\textquoteright}s harmful behaviour from an emotional dimension. Our goal in this work is to discover the relationship between the emotional state of social media users and their harmful behaviour while engaged in the act of cyber-violence. Our approach is based on Ensemble Machine Learning and engineered features related to Plutchik wheel of basic emotions extracted with semantic similarity and word embedding. The results show a significant association between the individual{\textquoteright}s emotional state and the harmful intent, which may be a good indicator for cyber-violence detection. Copyright {\textcopyright} 2020 Inderscience Enterprises Ltd.},
	keywords = {Basic emotions, Behaviour detections, Computational field, Emotional dimensions, Emotional state, Semantic similarity, Semantics, social media, Social networking (online), Violence detections},
	doi = {10.1504/IJHPSA.2020.113679},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102891888\&doi=10.1504\%2fIJHPSA.2020.113679\&partnerID=40\&md5=3e11e2e6a4032f608b4b71f2d426dbca},
	author = {Zarnoufi, R. and Boutbi, M. and Abik, M.}
}
@article {Zarnoufi2019672,
	title = {Language identification for user generated content in social media},
	journal = {Smart Innovation, Systems and Technologies},
	volume = {111},
	year = {2019},
	note = {cited By 0},
	pages = {672-678},
	doi = {10.1007/978-3-030-03577-8_73},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056449806\&doi=10.1007\%2f978-3-030-03577-8_73\&partnerID=40\&md5=0788a6d5259f75585d3c1d415a6c7300},
	author = {Zarnoufi, R. and Jaafar, H. and Abik, M.}
}
