CASS: Towards Building a Social-Support Chatbot for Online Health Community

Wang, Liuping; Wang, Dakuo; Tian, Feng; Peng, Zhenhui; Fan, Xiangmin; Zhang, Zhan; Ma, Shuai; Yu, Mo; Ma, Xiaojuan; Wang, Hongan

doi:10.48550/arxiv.2101.01583

Cited by 3 publications

(3 citation statements)

References 68 publications

(119 reference statements)

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“…Curation of robust labeled data for training AI-driven models to recognize support-seeking text in usergenerated content is a persistent challenge. Recently, Wang et al [117] implemented convolutional neural network (CNN)-based text classification with 2,300 labeled posts to distinguish informational support-seeking posts and non-informational seeking posts. Although their model achieved robust performance (accuracy: 0.86 / F1: 0.87), they did not release their dataset publicly.…”

Section: Annotation For Support-seeking Postsmentioning

confidence: 99%

Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic

Lee,

Rajtmajer,

Srivatsavaya

et al. 2023

Trans. Soc. Comput.

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We investigate relationships between online self-disclosure and received social support and user engagement during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users’ comments. We then analyze the effect of personal information sharing and posts’ topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: 1) users were more likely to share their age, education, and location information when seeking both informational and emotional support, as opposed to pursuing either one; 2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; 3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers’ self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; 4) post characteristics affecting audience response differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social support and user engagement.

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Section: Annotation For Support-seeking Postsmentioning

confidence: 99%

Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic

Lee,

Rajtmajer,

Srivatsavaya

et al. 2023

Trans. Soc. Comput.

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“…Empathy is a desirable trait of human daily conversations that enables individuals to understand, perceive, and respond appropriately to the situation and feelings of others (Keskin 2014). Previous research has demonstrated that empathetic dialogue systems can improve user experience and satisfaction in multiple domains (Fitzpatrick, Darcy, and Vierhile 2017;Liu et al 2021;Wang et al 2021). Hence, it is important to discover ways that allow us to equip open-domain dialogue systems with empathy.…”

Section: Introductionmentioning

confidence: 99%

CEM: Commonsense-Aware Empathetic Response Generation

Sabour

Zheng

Huang

2022

AAAI

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A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user’s emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user’s emotion, cognitive understanding of the user’s situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user’s situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EMPATHETICDIALOGUES, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses. Our code is available at https://github.com/Sahandfer/CEM.

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Section: Introductionmentioning

confidence: 99%

CEM: Commonsense-aware Empathetic Response Generation

Sabour¹,

Zheng²,

Huang³

2021

Preprint

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A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user's emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user's emotion, cognitive understanding of the user's situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user's situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EMPATHETICDIALOGUES, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses.

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CASS: Towards Building a Social-Support Chatbot for Online Health Community

Cited by 3 publications

References 68 publications

Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic

Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic

CEM: Commonsense-Aware Empathetic Response Generation

CEM: Commonsense-aware Empathetic Response Generation

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