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dc.contributor.authorPark, Soya
dc.contributor.authorZhang, Amy Xian
dc.contributor.authorMurray, Luke S.
dc.contributor.authorKarger, David R
dc.date.accessioned2021-01-20T15:41:56Z
dc.date.available2021-01-20T15:41:56Z
dc.date.issued2019-05
dc.identifier.isbn9781450359702
dc.identifier.urihttps://hdl.handle.net/1721.1/129464
dc.description.abstractEmail management consumes significant effort from senders and recipients. Some of this work might be automatable. We performed a mixed-methods need-finding study to learn: (i) what sort of automatic email handling users want, and (ii) what kinds of information and computation are needed to support that automation. Our investigation included a design workshop to identify categories of needs, a survey to better understand those categories, and a classification of existing email automation software to determine which needs have been addressed. Our results highlight the need for: a richer data model for rules, more ways to manage attention, leveraging internal and external email context, complex processing such as response aggregation, and affordances for senders. To further investigate our findings, we developed a platform for authoring small scripts over a user’s inbox. Of the automations found in our studies, half are impossible in popular email clients, motivating new design directions.en_US
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/3290605.3300604en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleOpportunities for Automating Email Processing: A Need-Finding Studyen_US
dc.typeArticleen_US
dc.identifier.citationPark, Soya et al. "Opportunities for Automating Email Processing: A Need-Finding Study." Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, Scotland, Association for Computing Machinery, May 2019. © 2019 Association for Computing Machineryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journalProceedings of the 2019 CHI Conference on Human Factors in Computing Systemsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-23T15:32:36Z
dspace.orderedauthorsPark, S; Zhang, AX; Murray, LS; Karger, DRen_US
dspace.date.submission2020-12-23T15:32:38Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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