@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.}
}
