Demo Data from VLDB 2026 / reviewers in the wild / expert
Connie Ye
dblp:264/7337
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing › online communities
online game communities |
0.4 | 1 | 2020 | 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 |
0.1 | 1 | 2020 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | It Takes a Village: Integrating an Adaptive Chatbot into an Online Gaming CommunityabstractWhile 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 |
CHI | 3 |