Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep
  Learning Techniques
Asma Abdulsalam Areej Alhothali Saleh Al-Ghamdi
Social media platforms have revolutionized traditional communication
techniques by enabling people globally to connect instantaneously, openly, and
frequently. People use social media to share personal stories and express their
opinion. Negative emotions such as thoughts of death, self-harm, and hardship
are commonly expressed on social media, particularly among younger generations.
As a result, using social media to detect suicidal thoughts will help provide
proper intervention that will ultimately deter others from self-harm and
committing suicide and stop the spread of suicidal ideation on social media. To
investigate the ability to detect suicidal thoughts in Arabic tweets
automatically, we developed a novel Arabic suicidal tweets dataset, examined
several machine learning models, including Na\"ive Bayes, Support Vector
Machine, K-Nearest Neighbor, Random Forest, and XGBoost, trained on word
frequency and word embedding features, and investigated the ability of
pre-trained deep learning models, AraBert, AraELECTRA, and AraGPT2, to identify
suicidal thoughts in Arabic tweets. The results indicate that SVM and RF models
trained on character n-gram features provided the best performance in the
machine learning models, with 86% accuracy and an F1 score of 79%. The results
of the deep learning models show that AraBert model outperforms other machine
and deep learning models, achieving an accuracy of 91\% and an F1-score of 88%,
which significantly improves the detection of suicidal ideation in the Arabic
tweets dataset. To the best of our knowledge, this is the first study to
develop an Arabic suicidality detection dataset from Twitter and to use
deep-learning approaches in detecting suicidality in Arabic posts.