M. LOUCIF Hemza

MCA

Directory of teachers

Department

Informatics Department

Research Interests

Specialized in Informatics Department. Focused on academic and scientific development.

Contact Info

University of M'Sila, Algeria

Email : hemza.loucif@univ-msila.dz

On the Web:

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Recent Publications

2025-11-25

Dynamic Spammer Detection using deep learning with temporal graph embeddings

Spammers in online social networks continuously adapt their strategies, making detection a
challenging and dynamic task. While traditional machine learning models and static deep learning
approaches such as CNNs achieve good performance, they often fail to capture the temporal evolution
of user behavior and network interactions. In this paper, we propose a novel deep learning framework
for dynamic spammer detection that combines Principal Component Analysis (PCA) for feature
reduction, Convolutional Neural Networks (CNNs) for local content feature extraction, and Temporal
Graph Embeddings (TGEs) to capture evolving interaction patterns over time. Unlike prior static
models, our approach explicitly models the dynamics of user behavior and relational changes in the
social graph. Experiments conducted on benchmark Twitter datasets demonstrate that our hybrid
PCA–CNN–TGE model significantly outperforms classical baselines (ANN, CNN, SVM) and static
hybrid models, achieving an F1-score of 94 %. The results highlight the importance of temporal graph
learning for robust and adaptive spammer detection in social networks.
Keywords: Spam, Cybersecurity, CNN, Social Networks, Temporal Graph Embeddings, PCA.
Citation

M. LOUCIF Hemza, (2025-11-25), "Dynamic Spammer Detection using deep learning with temporal graph embeddings", [international] The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25) November 25, 2025 – M’Sila, Algeria , M’Sila, Algeria

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2022

Toward a New Recursive Model to Measure Influence in Subscription Social Networks: A Case Study Using Twitter

This chapter presents a new version of one of the models that we have proposed to measure the influence of web users on social networks like Facebook. The new version enhanced the previous one through the incorporation of two substantial modules, namely, the global impression that the postings of the potential influencer get from his followers and the entropy which manifests the quantity of information carried by those postings. The comparison of our model with the precedent version and the PageRank benchmark has shown the effectiveness of our updates and the importance of incorporating the entropy and the global impression factors in its formulation.
Citation

M. LOUCIF Hemza, (2022), "Toward a New Recursive Model to Measure Influence in Subscription Social Networks: A Case Study Using Twitter", [national] International Conference on Managing Business Through Web Analytics , Springer Sham

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2021

A Recursive Model to Measure Influence in Subscription Networks: A Case Study using Twitter.

This paper presents a new version of one of the models that we have proposed to measure the influence of web users in social networks like Facebook. The new version enhanced the previous one through the incorporation of two substantial modules, namely the global impression that the postings of the potential influencer get from his followers and the entropy which manifests the quantity of information carried by those postings. The comparison of our model with the precedent version and the PageRank benchmark has shown the effectiveness of our updates and the importance of incorporating the entropy and the global impression factors in its formulation.
Citation

M. LOUCIF Hemza, (2021), "A Recursive Model to Measure Influence in Subscription Networks: A Case Study using Twitter.", [international] International Conference on “Managing Business through Web Analytics" ICMBWA2020, , Université Djilali Bounaama – Khemis Miliana,

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