Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology最新文献_第2页

Predicting Moments of Mood Changes Overtime from Imbalanced Social Media Data 从不平衡的社交媒体数据中预测情绪变化时刻

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.23

Falwah AlHamed, Julia Ive, Lucia Specia

引用次数: 3

Capturing Changes in Mood Over Time in Longitudinal Data Using Ensemble Methodologies 使用集成方法在纵向数据中捕捉情绪随时间的变化

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.18

Ana-Maria Bucur, Hyewon Jang, F. F. Liza

引用次数: 1

Towards Capturing Changes in Mood and Identifying Suicidality Risk 捕捉情绪变化和识别自杀风险

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.24

Sravani Boinepelli, S. Subramanian, Abhijeeth Singam, Tathagata Raha, Vasudeva Varma

引用次数: 2

Multi-Task Learning to Capture Changes in Mood Over Time 多任务学习捕捉情绪随时间的变化

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.22

Prasadith Kirinde Gamaarachchige, Ahmed Husseini Orabi, Mahmoud Husseini Orabi, D. Inkpen

引用次数: 1

Tracking Mental Health Risks and Coping Strategies in Healthcare Workers’ Online Conversations Across the COVID-19 Pandemic 追踪COVID-19大流行期间医护人员在线对话中的心理健康风险和应对策略

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.7

Molly Ireland, K. Adams, Seán Farrell

{"title":"Tracking Mental Health Risks and Coping Strategies in Healthcare Workers’ Online Conversations Across the COVID-19 Pandemic","authors":"Molly Ireland, K. Adams, Seán Farrell","doi":"10.18653/v1/2022.clpsych-1.7","DOIUrl":"https://doi.org/10.18653/v1/2022.clpsych-1.7","url":null,"abstract":"The mental health risks of the COVID-19 pandemic are magnified for medical professionals, such as doctors and nurses. To track conversational markers of psychological distress and coping strategies, we analyzed 67.25 million words written by self-identified healthcare workers (N = 5,409; 60.5% nurses, 40.5% physicians) on Reddit beginning in June 2019. Dictionary-based measures revealed increasing emotionality (including more positive and negative emotion and more swearing), social withdrawal (less affiliation and empathy, more “they” pronouns), and self-distancing (fewer “I” pronouns) over time. Several effects were strongest for conversations that were least health-focused and self-relevant, suggesting that long-term changes in social and emotional behavior are general and not limited to personal or work-related experiences. Understanding protective and risky coping strategies used by healthcare workers during the pandemic is fundamental for maintaining mental health among front-line workers during periods of chronic stress, such as the COVID-19 pandemic.","PeriodicalId":107109,"journal":{"name":"Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126881668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Exploring transformers and time lag features for predicting changes in mood over time 探索变形金刚和时间滞后特征，预测情绪随时间的变化

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.21

John M. Culnan, Damian Romero Diaz, S. Bethard

引用次数: 1

Psychotherapy is Not One Thing: Simultaneous Modeling of Different Therapeutic Approaches 心理治疗不是一件事:同时模拟不同的治疗方法

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.5

Maitrey Mehta, Derek D. Caperton, Katherine Axford, L. Weitzman, David C. Atkins, Vivek Srikumar, Zac E. Imel

{"title":"Psychotherapy is Not One Thing: Simultaneous Modeling of Different Therapeutic Approaches","authors":"Maitrey Mehta, Derek D. Caperton, Katherine Axford, L. Weitzman, David C. Atkins, Vivek Srikumar, Zac E. Imel","doi":"10.18653/v1/2022.clpsych-1.5","DOIUrl":"https://doi.org/10.18653/v1/2022.clpsych-1.5","url":null,"abstract":"There are many different forms of psychotherapy. Itemized inventories of psychotherapeutic interventions provide a mechanism for evaluating the quality of care received by clients and for conducting research on how psychotherapy helps. However, evaluations such as these are slow, expensive, and are rarely used outside of well-funded research studies. Natural language processing research has progressed to allow automating such tasks. Yet, NLP work in this area has been restricted to evaluating a single approach to treatment, when prior research indicates therapists used a wide variety of interventions with their clients, often in the same session. In this paper, we frame this scenario as a multi-label classification task, and develop a group of models aimed at predicting a wide variety of therapist talk-turn level orientations. Our models achieve F1 macro scores of 0.5, with the class F1 ranging from 0.36 to 0.67. We present analyses which offer insights into the capability of such models to capture psychotherapy approaches, and which may complement human judgment.","PeriodicalId":107109,"journal":{"name":"Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124945111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data 在纵向社交媒体数据中检测变化时刻和自杀风险水平的情感知情模型

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.20

Ulya Bayram, Lamia Benhiba

{"title":"Emotionally-Informed Models for Detecting Moments of Change and Suicide Risk Levels in Longitudinal Social Media Data","authors":"Ulya Bayram, Lamia Benhiba","doi":"10.18653/v1/2022.clpsych-1.20","DOIUrl":"https://doi.org/10.18653/v1/2022.clpsych-1.20","url":null,"abstract":"In this shared task, we focus on detecting mental health signals in Reddit users’ posts through two main challenges: A) capturing mood changes (anomalies) from the longitudinal set of posts (called timelines), and B) assessing the users’ suicide risk-levels. Our approaches leverage emotion recognition on linguistic content by computing emotion/sentiment scores using pre-trained BERTs on users’ posts and feeding them to machine learning models, including XGBoost, Bi-LSTM, and logistic regression. For Task-A, we detect longitudinal anomalies using a sequence-to-sequence (seq2seq) autoencoder and capture regions of mood deviations. For Task-B, our two models utilize the BERT emotion/sentiment scores. The first computes emotion bandwidths and merges them with n-gram features, and employs logistic regression to detect users’ suicide risk levels. The second model predicts suicide risk on the timeline level using a Bi-LSTM on Task-A results and sentiment scores. Our results outperformed most participating teams and ranked in the top three in Task-A. In Task-B, our methods surpass all others and return the best macro and micro F1 scores.","PeriodicalId":107109,"journal":{"name":"Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114933530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Measuring Linguistic Synchrony in Psychotherapy 测量心理治疗中的语言同步性

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.14

Natalie Shapira, Dana Atzil-Slonim, Rivka Tuval Mashiach, Ori Shapira

{"title":"Measuring Linguistic Synchrony in Psychotherapy","authors":"Natalie Shapira, Dana Atzil-Slonim, Rivka Tuval Mashiach, Ori Shapira","doi":"10.18653/v1/2022.clpsych-1.14","DOIUrl":"https://doi.org/10.18653/v1/2022.clpsych-1.14","url":null,"abstract":"We study the phenomenon of linguistic synchrony between clients and therapists in a psychotherapy process. Linguistic Synchrony (LS) can be viewed as any observed interdependence or association between more than one person?s linguistic behavior. Accordingly, we establish LS as a methodological task. We suggest a LS function that applies a linguistic similarity measure based on the Jensen-Shannon distance across the observed part-of-speech tag distributions (JSDuPos) of the speakers in different time frames. We perform a study over a unique corpus of 872 transcribed sessions, covering 68 clients and 59 therapists. After establishing the presence of client-therapist LS, we verify its association with therapeutic alliance and treatment outcome (measured using WAI and ORS), and additionally analyse the behavior of JSDuPos throughout treatment.Results indicate that (1) higher linguistic similarity at the session level associates with higher therapeutic alliance as reported by the client and therapist at the end of the session, (2) higher linguistic similarity at the session level associates with higher level of treatment outcome as reported by the client at the beginnings of the next sessions, (3) there is a significant linear increase in linguistic similarity throughout treatment, (4) surprisingly, higher LS associates with lower treatment outcome. Finally, we demonstrate how the LS function can be used to interpret and explore the mechanism for synchrony.","PeriodicalId":107109,"journal":{"name":"Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128166806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts CLPsych 2022共享任务概述:捕捉纵向用户帖子中的变化时刻

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.clpsych-1.16

A. Tsakalidis, Jenny Chim, I. Bilal, Ayah Zirikly, Dana Atzil-Slonim, F. Nanni, P. Resnik, Manas Gaur, Kaushik Roy, B. Inkster, Jeff Leintz, M. Liakata

{"title":"Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts","authors":"A. Tsakalidis, Jenny Chim, I. Bilal, Ayah Zirikly, Dana Atzil-Slonim, F. Nanni, P. Resnik, Manas Gaur, Kaushik Roy, B. Inkster, Jeff Leintz, M. Liakata","doi":"10.18653/v1/2022.clpsych-1.16","DOIUrl":"https://doi.org/10.18653/v1/2022.clpsych-1.16","url":null,"abstract":"We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year’s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual’s mood (dras- tic changes-‘Switches’- and gradual changes -‘Escalations’- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual – a continuation of the CLPsych 2019 Shared Task– where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).","PeriodicalId":107109,"journal":{"name":"Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132654274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26