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Hector Fried

dblp:217/9546 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
0009-0003-8397-4205ORCID · reported

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
Ubiquitous computing and smart environments · 44% Design research and methods · 44% Human-AI interaction · 13%

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

TopicWeightPapersLastEvidence papers
Design research and methods › field study
ethnographic study
0.312018
The Ethnobot: Gathering Ethnographies in the Age of IoT · CHI 2018
Ubiquitous computing and smart environments
internet of things
0.312018
The Ethnobot: Gathering Ethnographies in the Age of IoT · CHI 2018
Human-AI interaction › conversational agents
chatbot interaction
0.112018
The Ethnobot: Gathering Ethnographies in the Age of IoT · CHI 2018

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

field study · 0.3chatbot · 0.3
YearPublicationVenuePosition
2018 The Ethnobot: Gathering Ethnographies in the Age of IoT
abstract
Computational systems and objects are becoming increasingly closely integrated with our daily activities. Ubiquitous and pervasive computing first identified the emerging challenges of studying technology used on-the-move and in widely varied contexts. With IoT, previously sporadic experiences are interconnected across time and space in numerous and complex ways. This increasing complexity has multiplied the challenges facing those who study human experience to inform design. This paper describes the results of a study that used a chatbot or 'Ethnobot' to gather ethnographic data, and considers the opportunities and challenges in collecting this data in the absence of a human ethnographer. This study involved 13 participants gathering information about their experiences at the Royal Highland Show. We demonstrate the effectiveness of the Ethnobot in this setting, discuss the benefits and drawbacks of chatbots as a tool for ethnographic data collection, and conclude with recommendations for the design of chatbots for this purpose.
Ella Tallyn, Hector Fried, Rory Gianni, Amy Isard, Chris Speed
CHI2