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Found 7 papers, 2 papers with code
Detecting Suicidality with a Contextual Graph Neural Network
Discovering individuals’ suicidality on social media has become increasingly important.
A Dual-Prompting for Interpretable Mental Health Language Models
Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content.
Learning Co-Speech Gesture for Multimodal Aphasia Type Detection
By learning the correlation between the speech and gesture modalities for each aphasia type, our model can generate textual representations sensitive to gesture information, leading to accurate aphasia type detection.
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Bipolar disorder (BD) is closely associated with an increased risk of suicide.
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time.
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention
However, little attention has been paid to validate whether and how the existing dictionaries for other languages (i. e., English and Chinese) can be used for predicting suicidal ideation for a low-resource language (i. e., Korean) where a knowledge-based suicide dictionary has not yet been developed.
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest.
