publications

2021

1. 

   Robust Suicide Risk Assessment on Social Media via Deep Adversarial Learning

   Sawhney, Ramit, Joshi, Harshit, Gandhi, Saumya, Jin, Di, and Shah, Rajiv Ratn

   Journal of the American Medical Informatics Association 2021

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   The prevalence of social media for sharing personal thoughts makes it a viable platform for the assessment of suicide risk. However, deep learning models are not able to capture the diverse nature of linguistic choices and temporal patterns that can be exhibited by a suicidal user on social media and end up overfitting on specific cues that are not generally applicable. We propose Adversarial Suicide assessment Hierarchical Attention (ASHA), a hierarchical attention model that employs adversarial learning for improving the generalization ability of the model.We assess the suicide risk of a social media user across 5 levels of increasing severity of risk. ASHA leverages a transformer-based architecture to learn the semantic nature of social media posts and a temporal attention-based long short-term memory architecture for the sequential modeling of a user’s historical posts. We dynamically generate adversarial examples by adding perturbations to actual examples that can simulate the stochasticity in historical posts, thereby making the model robust.Through extensive experiments, we establish the face-value of ASHA and show that it significantly outperforms existing baselines, with the F1 score of 64%. This is a 2% and a 4% increase over the ContextBERT and ContextCNN baselines, respectively. Finally, we discuss the practical applicability and ethical aspects of our work pertaining to ASHA, as a human-in-the-loop framework.Adversarial samples can be helpful in capturing the diverse nature of suicidal ideation. Through ASHA, we hope to form a component in a larger human-in-the-loop infrastructure for suicide risk assessment on social media.

2. 

   Tweets Classification to Assist Human Moderation for Suicide Prevention

   Sawhney, Ramit*, Joshi, Harshit*, Nobles, Alicia*, and Shah, Rajiv Ratn

   In Proceedings of the International AAAI Conference on Web and Social Media 2021
3. 

   Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning

   Sawhney, Ramit*, Joshi, Harshit*, Shah, Rajiv Ratn, and Flek, Lucie

   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) 2021
4. 

   PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media

   Sawhney, Ramit*, Joshi, Harshit*, Flek, Lucie, and Shah, Rajiv Ratn

   In Proceedings of 16th Conference of the European Chapter of the Association for Computational Linguistics 2021

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   Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the buildup of such ideation is critical for the identification of users at risk. In this work, we focus on identifying suicidal intent in tweets by augmenting linguistic models with emotional phases modeled from users’ historical context.We propose PHASE, a time-and phase-aware framework that adaptively learns features from a user’s historical emotional spectrum on Twitter for preliminary screening of suicidal risk. Building on clinical studies, PHASE learns phase-like progressions in users’ historical Plutchik-wheel-based emotions to contextualize suicidal intent. While outperforming state-of-the-art methods, we show the utility of temporal and phase-based emotional contextual cues for suicide ideation detection. We further discuss practical and ethical considerations.

5. 

   Towards Ordinal Suicide Ideation Detection on Social Media

   Sawhney, Ramit, Joshi, Harshit, Gandhi, Saumya, and Shah, Rajiv Ratn

   In Proceedings of 14th ACM International Conference On Web Search And Data Mining 2021

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   The rising ubiquity of social media presents a platform for individuals to express suicide ideation, instead of traditional, formal clinical settings. While neural methods for assessing suicide risk on social media have shown promise, a crippling limitation of existing solutions is that they ignore the inherent ordinal nature across fine-grain levels of suicide risk. To this end, we reformulate suicide risk assessment as an Ordinal Regression problem, over the Columbia-Suicide Severity Scale. We propose SISMO, a hierarchical attention model optimized to factor in the graded nature of increasing suicide risk levels, through soft probability distribution since not all wrong risk-levels are equally wrong. We establish the face value of SISMO for preliminary suicide risk assessment on real-world Reddit data annotated by clinical experts. We conclude by discussing the empirical, practical, and ethical considerations pertaining to SISMO ina larger picture, as a human-in-the-loop framework.

2020

1. 

   A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media

   Sawhney, Ramit, Joshi, Harshit, Gandhi, Saumya, and Shah, Rajiv Ratn

   In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020

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   Social media’s ubiquity fosters a space for users to exhibit suicidal thoughts outside of traditional clinical settings. Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention. Suicide ideation is often linked to a history of mental depression. The emotional spectrum of a user’s historical activity on social media can be indicative of their mental state over time. In this work, we focus on identifying suicidal intent in English tweets by augmenting linguistic models with historical context. We propose STATENet, a time-aware transformer based model for preliminary screening of suicidal risk on social media. STATENet outperforms competitive methods, demonstrating the utility of emotional and temporal contextual cues for suicide risk assessment. We discuss the empirical, qualitative, practical, and ethical aspects of STATENet for suicide ideation detection.