An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms

Abstract

Suicidal ideation detection from social media is an evolving research with great challenges. Many of the people who have the tendency to suicide share their thoughts and opinions through social media platforms. As part of many researches it is observed that the publicly available posts from social media contain valuable criteria to effectively detect individuals with suicidal thoughts. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide. This can be achieved by identifying the sudden changes in a user's behavior automatically. Natural language processing techniques can be used to collect behavioral and textual features from social media interactions and these features can be passed to a specially designed framework to detect anomalies in human interactions that are indicators of suicidal intentions. We can achieve fast detection of suicidal ideation using deep learning and/or machine learning based classification approaches. For such a purpose, we can employ the combination of LSTM and CNN models to detect such emotions from posts of the users. In order to improve the accuracy, some approaches like using more data for training, using attention model to improve the efficiency of existing models etc. could be done. This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect any underlying suicidal intentions. During evaluations, the proposed model demonstrated an accuracy of 90.3% and an F1score of 92.6%, which is greater than the baseline models.

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

“…Although not a CLPsych participant, ref. [ 40 ] also used this dataset in their research as it is available to non-participants upon approval from the authors. The authors of [ 16 , 41 , 42 ] used a dataset created by [ 13 ].…”

“…Similarly, if word tokenization is applied, then each word in the string represents a separate token. The NLTK Python package was used by [ 16 , 40 , 48 ] to perform tokenization, whereas [ 38 , 49 ] chose the SpaCy package to split the text into individual tokens. Ríssola et al [ 46 ] used the Ekphrasis Python library for tokenization.…”

“…If lemmatization is not performed, words with the same meaning (e.g., “children” and “child”) will be seen by conventional ML models as unrelated. The authors of [ 16 , 40 ] used NLTK to lemmatize text and [ 38 ] used SpaCy for that purpose. Alternatively, [ 41 ] opted for stemming instead of lemmatization.…”

“…The study of [ 44 ] applied CNN over the word embedding to create feature maps and used a max-pooling layer over the features. Three studies [ 11 , 16 , 40 ] proposed an ensemble model that combines LSTM and CNN. Renjith et al [ 40 ] proposed an LSTM-Attention-CNN model with a 300-dimensional Word2Vec embedding acting as an input layer.…”

AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM

“…Yao et al [30] combine CNN and LSTM for traffic data prediction; this method proposes to combine the temporal dynamics and spatial dependence of traffic flow. Renjith et al [31] use CNN+LSTM anomaly detection technique to detect suicidal ideation in social media platform posts, combining anomaly detection with user multidimensional thought logic. The same CNN+LSTM model is used in [32] for user evaluation, which can detect anomalies in text analysis to determine psychiatric categories.…”