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Connie Ye

dblp:264/7337 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 50% Collaborative and social computing · 50%
Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › online communities
online game communities
0.412020
It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming Community · CHI 2020
Natural language and speech › Language models and text generation › language acquisition
vocabulary learning
0.112020
It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming Community · CHI 2020

Methods — techniques the papers use, named apart from their topics

deployment study · 0.9
YearPublicationVenuePosition
2020 It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming Community
abstract
While the majority of research in chatbot design has focused on creating chatbots that engage with users one-on-one, less work has focused on the design of conversational agents for online communities. In this paper we present results from a three week test of a social chatbot in an established online community. During this study, the chatbot "grew up" from "birth" through its teenage years, engaging with community members and "learning" vocabulary from their conversations. We discuss the design of this chatbot, how users' interactions with it evolved over the course of the study, and how it impacted the community as a whole. We discuss how we addressed challenges in developing a chatbot whose vocabulary could be shaped by users, and conclude with implications for the role of machine learning in social interactions in online communities and potential future directions for design of community-based chatbots.
Joseph Seering, Michal Luria, Connie Ye, Geoff Kaufman, Jessica Hammer
CHI3