TeenChat: A Chatterbot System for Sensing and Releasing Adolescents’ Stress

Huang, Jing; Li, Qi; Xue, Yi; Cheng, Taoran; Xu, Sheng; Jia, Jia; Feng, Ling

doi:10.1007/978-3-319-19156-0_14

Cited by 45 publications

(66 citation statements)

References 26 publications

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“…With regard to human-computer interaction, there is evidence suggesting that chatbots and virtual agents have the potential to reduce negative emotions such as stress (Prendinger et al, 2005;Huang et al, 2015), emotional distress (Klein et al, 2002), and frustration (Hone, 2006), as well as comfort users. Talking to chatbots about negative emotions or stressful experiences may also have benefits over discussing these issues with other humans.…”

Section: Agents For Emotional and Social Supportmentioning

confidence: 99%

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

2020

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From past research it is well known that social exclusion has detrimental consequences for mental health. To deal with these adverse effects, socially excluded individuals frequently turn to other humans for emotional support. While chatbots can elicit social and emotional responses on the part of the human interlocutor, their effectiveness in the context of social exclusion has not been investigated. In the present study, we examined whether an empathic chatbot can serve as a buffer against the adverse effects of social ostracism. After experiencing exclusion on social media, participants were randomly assigned to either talk with an empathetic chatbot about it (e.g., "I'm sorry that this happened to you") or a control condition where their responses were merely acknowledged (e.g., "Thank you for your feedback"). Replicating previous research, results revealed that experiences of social exclusion dampened the mood of participants. Interacting with an empathetic chatbot, however, appeared to have a mitigating impact. In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed.

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Section: Agents For Emotional and Social Supportmentioning

confidence: 99%

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

2020

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“…Furthermore, Esterling et al have found that verbal expression about stressful events, compared to written expression, achieved greater improvements in cognitive change, self-esteem, and adaptive coping strategies. More recently, talking with an online chatbot has been shown effective in reducing participants' stress (Fitzpatrick et al, 2017;Huang et al, 2015). Therefore, the current study explores the idea of designing a social robot that engages and encourages users to selfdisclose in a stress-sharing activity.…”

Section: The Benefits Of Self-disclosurementioning

confidence: 99%

Sharing Stress With a Robot: What Would a Robot Say?

Ling

Björling

2020

HMC

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With the prevalence of mental health problems today, designing human-robot interaction for mental health intervention is not only possible, but critical. The current experiment examined how three types of robot disclosure (emotional, technical, and by-proxy) affect robot perception and human disclosure behavior during a stress-sharing activity. Emotional robot disclosure resulted in the lowest robot perceived safety.Post-hoc analysis revealed that increased perceived stress predicted reduced human disclosure, user satisfaction, robot likability, and future robot use. Negative attitudes toward robots also predicted reduced intention for future robot use. This work informs on the possible design of robot disclosure, as well as how individual attributes, such as perceived stress, can impact human robot interaction in a mental health context.

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“…The collected data consists of 1,920 posts based on these data we have evaluated PSD model and it showed an increased accuracy compared to J. Huang's Teenchat [22] and M.thelwall's TensiStrength [27] system for stress detection. The figure 7 shows a comparison of efficiency of the PSD model from the result shown in table 4 we see that PSD model achieved better detection performance and showed F1-Score of 95.7% whereas TensiStrength shows 5.9% of less F1score i.e., 90.10% and J. Huang's Teenchat [22] shows 15.5% of less F1-score i.e., 80.20% and when compared to our PSD model. parameters, as shown Tensistrength [27] achieved 89% of accuracy and it supports only text parameter for stress detection and for the same.…”

Section: Resultsmentioning

confidence: 99%

“…With the development of social networks people are willing to share their daily events and moods, and interact with friends through the social networks, making it possible to leverage online social network data for stress detection. There are also some exploration [22], and [27] using user posting contents on social network to identify user's stress revealed that leveraging this social media data for healthcare, and in particular stress detection, is feasible. However, these mechanisms mainly leverage the textual contents and consider only single post of an individual in social networks.…”

Section: Related Work and Problem Definationmentioning

confidence: 99%

Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

Ali

Hajera²

2018

ijisae

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One of the mental threat for individual's health identified is Psychological stress from social media data. Hence, necessity is to predict and manage stress before it turns into a serious problem. However, Conventional stress detection methods exist, that rely on psychological scales & physiological devices that need full of victims participation which is time-consuming, complex and expensive. With the trending growth of social networks, people are addicted towards sharing personal moods via social media platforms to influence other users, leading to stressfulness. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the victims's psychological stress from social media data. It comprises of three (3) modules Probabilistic Naïve Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information; we defined set of stress-related textual 'F = {f1, f2, f3, f4}', visual 'vF = {vf1, vf2}', and social features 'sf' to predict stress from social media content. Experimental results show the proposed PSD model improves the detection process when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model will assist in developing stress detection tools for mental health agencies.

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TeenChat: A Chatterbot System for Sensing and Releasing Adolescents’ Stress

Cited by 45 publications

References 26 publications

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood

Sharing Stress With a Robot: What Would a Robot Say?

Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

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