CLPsych 2025 @naaclmeeting is happening soon! We're looking forward to seeing you all. Stay tuned for the Best Poster Award, which will be voted on the day of the workshop after the poster session.
Yaara Shriki and colleagues in "Masking Morphosyntactic Categories to Evaluate Salience for Schizophrenia Diagnosis" fine-tune a language model and mask nouns, verbs and some other morphosyntactic categories to detect transcribed speech of patients diagnosed with schizophrenia
Daeun Lee et al. developed the suicidality detection model C-GraphSAGE based on a graph neural network to grasp the dynamic semantic information of social media post by using the suicide vocabulary efficiently.
Check out shared task paper "Multi-Task Learning to Capture Changes in Mood Over Time". The authors investigate the impact of using MTL to predict mood changes over time of social media users --From: uOttawa-AI
To what extent do people who engage in non-suicidal self-injury conceptualize their behavior as an addiction? Using #reddit data we identify shared language across #NSSI and #SubstanceUse Reddits, showing borrowed #recovery language when describing self-harm
Neural networks can extract emotions from online therapy messages and predict depression and anxiety, a work by Hannah Burkhardt and colleagues at CLPsych.
Check out shared task paper by John Culnan et al. where authors used RoBERTa-based models and a feature representing time between posts to model changes in mood over time in Reddit posts.
What is Linguistic Synchrony? How can it be measured? And what is it good for? Work by Natalie Shapira et al. "Measuring Linguistic Synchrony in Psychotherapy" at CLPsych
How are language-based predictions of psychological factors associated with more frequent mood changes? Check out the work from @WWBProject at #CLPsych to learn interesting associations, and about how transformer-based models track mental health signals over time. #NAACL2022
The relationship between multiple mental health conditions are explorable and exploitable. To learn more check the work "Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses" by @ankitaich30 and colleagues
How to build more explainable depression detection models that the clinicians want to use? Check @AyaZirikly@mdredze work on how using PHQ-9 auxiliary models can better explain depression detection models @jhuclsp
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