Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation

Mathur, Puneet; Sawhney, Ravinder Singh; Chopra, Shivang; Leekha, Maitree; Shah, Rajiv Ratn

doi:10.1007/978-3-030-45442-5_33

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…In [25], authors applied Long short-term memory (LSTM) to detect mental illness from Reddit posts and achieved promising results. In [24], applied LSTM with attention mechanism to estimate suicidal intent by utilizing temporal psycholinguistic. We applied several pre-trained deep learning algorithms for multi-class mental illness detection such as Convolutional Neural Network, Gated recurrent unit (GRU), Bidirectional Gated recurrent units (Bi-GRU), LSTM, and Bidirectional LSTM.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

Ameer¹,

Arif²,

Sidorov³

et al. 2022

Preprint

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Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individual's physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision-making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

Ameer¹,

Arif²,

Sidorov³

et al. 2022

Preprint

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show abstract

“…Recent studies have focused on incorporating more social media components to capture as much available contextual information as possible. Among these are historical posts [4,[9][10][11][12][13][14][15][16][17][18], conversation trees [19], social and interaction graphs [4,10,12,17,20], user and post metadata information [10,11], and images [10]. While more contextual sources may be ideal for assessing an individual's mental health state, access to these data has become increasingly restrictive due to heightened data privacy concerns.…”

Section: Social Media Mental Health Classificationmentioning

confidence: 99%

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

Cabral,

Han,

Poon

et al. 2024

Robotics

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More than 80% of people who commit suicide disclose their intention to do so on social media. The main information we can use in social media is user-generated posts, since personal information is not always available. Identifying all possible emotions in a single textual post is crucial to detecting the user’s mental state; however, human emotions are very complex, and a single text instance likely expresses multiple emotions. This paper proposes a new multi-label emotion graph representation for social media post-based mental health classification. We first construct a word–document graph tensor to describe emotion-based contextual representation using emotion lexicons. Then, it is trained by multi-label emotions and conducts a graph propagation for harmonising heterogeneous emotional information, and is applied to a textual graph mental health classification. We perform extensive experiments on three publicly available social media mental health classification datasets, and the results show clear improvements.

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“…Trifan Alina et al in [26] suggested a rule-based estimator using a tf-idf weighting technique for the bag-of-words characteristics to detect depression from the Reddit social media site. Mathur and Puneet in [27] proposed a solution using Bidirectional LSTM (BLSTM) + Attention model for the early detection of depression from the historic tweets of Twitter users.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Depression Detection from Textual Data

et al. 2022

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Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present, with the profound expansion of social media and users of the internet. Mental illnesses are highly hazardous, stirring more than three hundred million people. Moreover, that is why research is focused on this subject. With the advancements of machine learning and the availability of sample data relevant to depression, there is the possibility of developing an early depression diagnostic system, which is key to lessening the number of afflicted individuals. This paper proposes a productive model by implementing the Long-Short Term Memory (LSTM) model, consisting of two hidden layers and large bias with Recurrent Neural Network (RNN) with two dense layers, to predict depression from text, which can be beneficial in protecting individuals from mental disorders and suicidal affairs. We train RNN on textual data to identify depression from text, semantics, and written content. The proposed framework achieves 99.0% accuracy, higher than its counterpart, frequency-based deep learning models, whereas the false positive rate is reduced. We also compare the proposed model with other models regarding its mean accuracy. The proposed approach indicates the feasibility of RNN and LSTM by achieving exceptional results for early recognition of depression in the emotions of numerous social media subscribers.

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Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation

Cited by 16 publications

References 15 publications

Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media

Deep Learning for Depression Detection from Textual Data

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