@conference {Bayram2022219,
	title = {Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data},
	booktitle = {CLPsych 2022 - 8th Workshop on Computational Linguistics and Clinical Psychology, Proceedings},
	year = {2022},
	note = {cited By 1},
	pages = {219-225},
	abstract = {In this shared task, we focus on detecting mental health signals in Reddit users{\textquoteright} posts through two main challenges: A) capturing mood changes (anomalies) from the longitudinal set of posts (called timelines), and B) assessing the users{\textquoteright} suicide risk-levels. Our approaches leverage emotion recognition on linguistic content by computing emotion/sentiment scores using pre-trained BERTs on users{\textquoteright} posts and feeding them to machine learning models, including XGBoost, Bi-LSTM, and logistic regression. For Task-A, we detect longitudinal anomalies using a sequence-to-sequence (seq2seq) autoencoder and capture regions of mood deviations. For Task-B, our two models utilize the BERT emotion/sentiment scores. The first computes emotion bandwidths and merges them with n-gram features, and employs logistic regression to detect users{\textquoteright} suicide risk levels. The second model predicts suicide risk on the timeline level using a Bi-LSTM on Task-A results and sentiment scores. Our results outperformed most participating teams and ranked in the top three in Task-A. In Task-B, our methods surpass all others and return the best macro and micro F1 scores. {\textcopyright} 2022 Association for Computational Linguistics.},
	keywords = {Auto encoders, Emotion Recognition, Learning systems, Logistics regressions, Long short-term memory, Machine learning models, Mental health, N-grams, Participating teams, Risk assessment, Risk levels, Sentiment scores, Social media datum, Social networking (online)},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137975576\&partnerID=40\&md5=3326dfc2470d3574a556dc3a88778a91},
	author = {Bayram, U. and Benhiba, L.}
}
@article {Hebroune2022581,
	title = {User-Enriched Embedding for Fake News Detection on Social Media},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 1},
	pages = {581-599},
	abstract = {Recent political, pandemic, and social turmoil events have led to an increase in the popularity and spread of misinformation. As demonstrated by the widespread effects of the large onset of fake news, humans are inconsistent if not outright poor detectors of fake news. Thereby, many efforts are being made to automate the process of fake news detection. The most popular of these approaches include blacklisting sources and authors that are unreliable. While these tools are useful, in order to create a more complete end to end solution, we need to account for more difficult cases where reliable sources and authors release fake news. As such, the goal of this paper is to propose an approach for detecting the language and behavioral patterns that characterize fake and real news through the use of social network analysis and natural language processing techniques. We have built a model that catches many intuitive indications of real and fake news using users and submissions attributes, and thus laid the foundation for an approach that concatenates multiple embeddings for better fake news detection on social media. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_44},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135051740\&doi=10.1007\%2f978-3-031-07969-6_44\&partnerID=40\&md5=d606f2faefcf64892e7ecc776b39da96},
	author = {Hebroune, O. and Benhiba, L.}
}
@article {Iraqi2021506,
	title = {Data Analytics in Investment Banks},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {5},
	year = {2021},
	note = {cited By 0},
	pages = {506-513},
	abstract = {Capital Markets are one of the most important pillars of worldwide economy. They gather skilled finance and IT professionals as well as economists in order to take the best investment decisions and choose the most suitable funding solutions every time. Data analytics projects in Capital Markets can definitely be very beneficial as all optimizations and innovations would have a financial impact, but can also be very challenging as the field itself has always incorporated a research component, thus finding out what could really be of an added value might be a tricky task. Based on a comprehensive literature review, this paper aims to structure the thoughts around data analytics in investment banks, and puts forward a classification of relevant data analytics use cases. Lastly, it also discusses how transforming to a data-driven enterprise is the real change investment banks should aim to achieve, and discusses some of the challenges that they might encounter when engaging in this transformation process. {\textcopyright} 2021. All Rights Reserved.},
	keywords = {Commerce, Data analytic use case, Data Analytics, Data driven, Data-driven transformation, Financial impacts, Financial markets, Investment bank, Investment decisions, Investments, IT professional, Metadata, Optimisations},
	doi = {10.14569/IJACSA.2021.0120562},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107532071\&doi=10.14569\%2fIJACSA.2021.0120562\&partnerID=40\&md5=7237f6b38c7eedbe1f23bb2968802d60},
	author = {Iraqi, B. and Benhiba, L. and Idrissi, M.A.J.}
}
@conference {Bayram202181,
	title = {Determining a Person{\textquoteright}s Suicide Risk by Voting on the Short-Term History of Tweets for the CLPsych 2021 Shared Task},
	booktitle = {Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021},
	year = {2021},
	note = {cited By 8},
	pages = {81-86},
	abstract = {In this shared task, we accept the challenge of constructing models to identify Twitter users who attempted suicide based on their tweets 30 and 182 days before the adverse event{\textquoteright}s occurrence. We explore multiple machine learning and deep learning methods to identify a person{\textquoteright}s suicide risk based on the short-term history of their tweets. Taking the real-life applicability of the model into account, we make the design choice of classifying on the tweet level. By voting the tweet-level suicide risk scores through an ensemble of classifiers, we predict the suicidal users 30-days before the event with an 81.8\% true-positives rate. Meanwhile, the tweet-level voting falls short on the six-month-long data as the number of tweets with weak suicidal ideation levels weakens the overall suicidal signals in the long term. {\textcopyright}2021 Association for Computational Linguistics.},
	keywords = {Adverse events, Constructing models, Deep learning, Ensemble of classifiers, Learning methods, Risk score, Risk-based, Short-term history, Suicidal ideation, True positive rates},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111364845\&partnerID=40\&md5=a90a61db35a9b8d92bdaa76eb1e44f49},
	author = {Bayram, U. and Benhiba, L.}
}
@conference {Boukhayma202037,
	title = {Evaluating Ongoing Decision Support System: A Case Study},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {37-42},
	abstract = {The aim of this paper is to put into practice a framework we proposed in an earlier work for evaluating on-going decision support systems. The framework serves as a base for a method to compute evaluation criteria scores using an AHP model. The paper also illustrates the automation of this method into a tool to enable GASCO company to choose between two already implemented DSSs in order to reduce IT operating cost. The evaluation covers three axes: Process, outcome and decision maker metrics. The results highlight the importance of using users{\textquoteright} learning as a measure to determine DSS success. {\textcopyright} 2020 ACM.},
	keywords = {Decision makers, Decision making, Decision support systems, Evaluation criteria, Intelligent systems, Three axes},
	doi = {10.1145/3419604.3419802},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096414260\&doi=10.1145\%2f3419604.3419802\&partnerID=40\&md5=d07ab659252fea5554cc22b2500955cb},
	author = {Boukhayma, K. and Idrissi, M.A.J. and Benhiba, L.}
}
@conference {Bourhim2018,
	title = {Towards a Novel Graph-based collaborative filtering approach for recommendation systems},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3289402.3289524},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062789723\&doi=10.1145\%2f3289402.3289524\&partnerID=40\&md5=77f17a12e2f7e3739a2e3042bb059b48},
	author = {Bourhim, S. and Benhiba, L. and Janati Idrissi, M.A.}
}
@article {ElAsri2017217,
	title = {From periphery to core: A temporal analysis of github contributors{\textquoteright} collaboration network},
	journal = {IFIP Advances in Information and Communication Technology},
	volume = {506},
	year = {2017},
	note = {cited By 0},
	pages = {217-229},
	abstract = {Open-source projects in GitHub exhibit rich temporal dynamics, and diverse contributors{\textquoteright} social interactions further intensify this process. In this paper, we analyze temporal patterns associated with Open Source Software (OSS) projects and how the contributor{\textquoteright}s notoriety grows and fades over time in a core-periphery structure. In order to explore the temporal dynamics of GitHub communities we formulate a time series clustering model using both Social Network Analysis (SNA) and technical metrics. By applying an adaptive time frame incremental approach to clustering, we locate contributors in different temporal networks. We demonstrate our approach on five long-lived OSS projects involving more than 700 contributors and found that there are three main temporal shapes of attention when contributors shift from periphery to core. Our analyses provide insights into common temporal patterns of the growing OSS communities on GitHub and broaden the understanding of the dynamics and motivation of open source contributors. {\textcopyright} IFIP International Federation for Information Processing 2017.},
	doi = {10.1007/978-3-319-65151-4_21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029582330\&doi=10.1007\%2f978-3-319-65151-4_21\&partnerID=40\&md5=741b4dffe841d068e54b517f5b1966c8},
	author = {El Asri, I. and Kerzazi, N. and Benhiba, L. and Janati, M.}
}
