TickTalkTurk

Qiu, Sihang; Gadiraju, Ujwal; Bozzon, Alessandro

doi:10.1145/3406865.3418572

Cited by 11 publications

(1 citation statement)

References 4 publications

(4 reference statements)

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“…A community sharing a similar burden can be an excellent resource for self-care methods. While various other means of serving these methods exist, researchers are currently actively looking into the affordances of chat-based conversational agents, chatbots, due to their inherent relatability and rapidly increasing interaction capabilities (see, e.g., ( 9 , 10 )).…”

Section: Introductionmentioning

confidence: 99%

Supporting mental health self-care discovery through a chatbot

Moilanen

Berkel²,

Visuri³

et al. 2023

Front. Digit. Health

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Good mental health is imperative for one’s wellbeing. While clinical mental disorder treatments exist, self-care is an essential aspect of mental health. This paper explores the use and perceived trust of conversational agents, chatbots, in the context of crowdsourced self-care through a between-subjects study (N = 80). One group used a standalone system with a conventional web interface to discover self-care methods. The other group used the same system wrapped in a chatbot interface, facilitating utterances and turn-taking between the user and a chatbot. We identify the security and integrity of the systems as critical factors that affect users’ trust. The chatbot interface scored lower on both these factors, and we contemplate the potential underlying reasons for this. We complement the quantitative data with qualitative analysis and synthesize our findings to identify suggestions for using chatbots in mental health contexts.

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Section: Introductionmentioning

confidence: 99%

Supporting mental health self-care discovery through a chatbot

Moilanen

Berkel²,

Visuri³

et al. 2023

Front. Digit. Health

Self Cite

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Using Worker Avatars to Improve Microtask Crowdsourcing

Qiu

Bozzon

Birk

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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The future of crowd work has been identified to depend on worker satisfaction, but we lack a thorough understanding of how worker satisfaction can be increased in microtask crowdsourcing. Prior work has shown that one solution is to build tasks that are engaging. To facilitate engagement, two methods that have received attention in recent HCI literature are the use of video games and conversational interfaces. While these are largely different techniques, they aim for the same goal of reducing worker burden and increasing engagement in a task. On one hand, video games have huge motivation potential and translating game design elements for motivational purposes has shown positive effects. Recent work in games research has shown that the use of player avatars is effective in fostering interest, enjoyment, and other aspects pertaining to intrinsic motivation. On the other hand, conversational interfaces have been argued to have advantages over traditional GUIs due to facilitating a more human-like interaction. "Conversational" microtasking has recently been proposed to improve worker engagement in microtask marketplaces. The contexts of games and crowd work are underlined by the need to motivate and engage participants, yet the potential of using worker avatars to promote self-identification and improve worker satisfaction in microtask crowdsourcing has remained unexplored. Addressing this knowledge gap, we carried out a between-subject study involving 360 crowd workers. We investigated how worker avatars influence quality related outcomes of workers and their perceived experience, in conventional web and novel conversational interfaces. We equipped workers with the functionality of customizing their avatars, and selecting characterizations for their avatars, to understand whether identifying with an avatar can increase the motivation of workers. We found that using worker avatars with conversational interfaces can effectively reduce cognitive workload and increase worker retention. Our results indicate the occurrence of similarity and wishful avatar identification in crowdsourcing. Our findings have important implications in alleviating workers' perceived workload and on the design of crowdsourcing microtasks.CCS Concepts: • Information systems → Crowdsourcing; • Human-centered computing → Empirical studies in HCI.

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