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Detecting temporal cognition in text: Comparison of judgements by self, expert and machine

Walsh, Erin; Busby Grant, Janie

Description

There is a growing research focus on temporal cognition, due to its importance in memory and planning, and links with psychological wellbeing. Researchers are increasingly using diary studies, experience sampling and social media data to study temporal thought. However, it remains unclear whether such reports can be accurately interpreted for temporal orientation. In this study, temporal orientation judgements about text reports of thoughts were compared across human coding, automatic text...[Show more]

dc.contributor.authorWalsh, Erin
dc.contributor.authorBusby Grant, Janie
dc.date.accessioned2019-11-25T04:18:43Z
dc.date.available2019-11-25T04:18:43Z
dc.identifier.citationWalsh EI and Busby Grant J (2018) Detecting Temporal Cognition in Text: Comparison of Judgements by Self, Expert and Machine. Front. Psychol. 9:2037. doi: 10.3389/fpsyg.2018.02037
dc.identifier.issn1664-1078
dc.identifier.urihttp://hdl.handle.net/1885/186597
dc.description.abstractThere is a growing research focus on temporal cognition, due to its importance in memory and planning, and links with psychological wellbeing. Researchers are increasingly using diary studies, experience sampling and social media data to study temporal thought. However, it remains unclear whether such reports can be accurately interpreted for temporal orientation. In this study, temporal orientation judgements about text reports of thoughts were compared across human coding, automatic text mining, and participant self-report.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherFrontiers Research Foundation
dc.rights© 2018 Walsh and Busby Grant
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceFrontiers in Psychology
dc.subjecttemporal cognition
dc.subjectStanford Natural Language Parser
dc.subjectself-report
dc.subjecttemporal orientation
dc.subjecttense extraction
dc.titleDetecting temporal cognition in text: Comparison of judgements by self, expert and machine
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume9
dcterms.dateAccepted2018-10-03
dc.date.issued2018-10-26
local.identifier.absfor111706 - Epidemiology
local.identifier.absfor110902 - Cellular Nervous System
local.identifier.ariespublicationu4485658xPUB1382
local.type.statusPublished Version
local.contributor.affiliationWalsh, Erin, Centre for Research on Ageing, Health & Wellbeing, ANU
local.contributor.affiliationBusby Grant, Janie, University of Canberra
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage6
local.identifier.doi10.3389/fpsyg.2018.02037
local.identifier.absseo920502 - Health Related to Ageing
local.identifier.absseo920410 - Mental Health
dc.date.updated2019-05-19T08:21:49Z
local.identifier.scopusID2-s2.0-85055527869
dcterms.accessRightsOpen Access
dc.provenanceThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.licenseCreative Commons Attribution License (CC BY)
CollectionsANU Research Publications

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