# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 1 Introduction
The primary objective of our study is to share the knowledge of suicide ideation in Reddit social media forums from a data analysis perspective using effective deep learning architectures. Our main task is to explore the potential of Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and their combined model applied in multiple classification tasks for suicide ideation struggles. We try to test if an implementation of CNN and LSTM classifiers into one model can improve the language modeling and text classification performance. We will try to demonstrate that LSTM-CNN model can outperform the performance of its individual CNN and LSTM classifiers as well as more traditional machine learning systems for suicide-related topics. Potentially, it can be embedded on any online forum's and blog's data sets. In our experiment, we first choose the data source, define our proposed model and analyze the baseline characteristics. Then, we compute the frequency of n-grams, such as unigrams and bigrams, in the dataset to detect the presence of suicidal thoughts. We evaluate the experimental approach based on the baseline and our proposed model. Finally, we train our LSTM-CNN model using 10-fold cross-validation to identify our best hyper-parameter selection for suicide ideation detection. For our dataset, we apply the data collected from Reddit social media which allow its users to create longer posts. Our study has specific three-fold contributions:  
* N-gram analysis: we evaluate the n-gram analysis to show that the expressions of suicidal tendencies and reduced social engagements are often discussed in suicide-related forums. We identify the transition towards the social ideation associated with different psychological stages such as heightened self-focused attention, a manifestation of hopelessness, frustration, anxiety or loneliness. * Classical features analysis: using CNN, LSTM and LSTM-CNN combined model analysis, we evaluate bag of words, TF-IDF and statistical features performance over word embedding. * Comparative evaluation: we explore the performance of LSTM-CNN combined class of deep neural networks as our proposed model for detection of suicide ideation tasks to improve the state-of-the-art method. In terms of evaluation metrics, we compare its strength and potential with CNN and LSTM deep learning techniques and four traditional machine learning classifiers including SVM, NB, RF and XGBost) on the real-world dataset. The structure of our paper is as follows: Section 2 describes related work on suicide and suicide ideation detected in social media. Section 3 analyzes a data collection method. Section 4 introduces a proposed methodology. For combined neural network classification approaches, it conducts the data pre-processing followed by a word embedding process. Section 5 focuses on an experimental set up concerning the baseline, model architectures, parameters and evaluation metrics. In section 5, we examine the results and the most powerful machine learning techniques for the detection of suicide ideation. In Section 6, we conclude our study and discuss the main limitations of our work. Finally, we define the main directions for future work.