Synopsis of Social media discussions
Overall, the social discussions reflect a cautious curiosity, with many posters highlighting the novelty of the data-driven approach and its potential significance, using words like 'robust,' 'new research,' and 'big data,' which convey both interest and recognition of the study's relevance.
Agreement
Neither agree nor disagreeThe discussions show mixed reactions, with some acknowledging the robustness of the data-driven approach but others questioning its broader implications.
Interest
Moderate level of interestParticipants are generally interested, especially in the novelty of identifying four personality types across large datasets, indicated by phrases like 'new research' and 'big data.'
Engagement
Neutral engagementThe engagement appears moderate; some posts cite the study directly and discuss methodology, but few delve deeply into critique or detailed analysis.
Impact
Moderate level of impactThe discussions suggest moderate impact, with mentions of the research being a 'game changer' and references to its relevance in psychology and data science, indicating recognition of its significance.
Social Mentions
YouTube
3 Videos
8 Posts
10 Posts
Blogs
18 Articles
News
92 Articles
2 Posts
Metrics
Video Views
291
Total Likes
7
Extended Reach
41,981
Social Features
133
Timeline: Posts about article
Top Social Media Posts
Posts referencing the article
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@PaisleyJanine @RageSheen “A robust data-driven approach identifies four personality types across four large data sets” Gerlach et al, Nature Human Behaviour, 2018 https://t.co/xdIIaEIQh8
view full postNovember 17, 2023
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Jim Bloom
@jimmyroybloom (Twitter)A robust data-driven approach identifies four personality types across four large data sets | Nature Human Behaviour https://t.co/eMU93hBF3m
view full postDecember 14, 2022
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호모심비어스
@kzaan (Twitter)이는 과학자들이 발표한 최초의 성격유형 연구 사례다. 관련 논문은 과학저널 ‘네이처 인간 행동(Nature Human Behavior)’에 게재됐다. 논문 제목은 ‘A robust data-driven approach identifies four personality types across four large data sets’. https://t.co/OpcN62jxwl
view full postNovember 19, 2022
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Talent Metrics LLC
@Talent_metrics (Twitter)A robust data-driven approach identifies four personality types across four large data sets https://t.co/BBM475pGO8 #IOPsych #psychology #science #research #DataDriven
view full postMarch 5, 2020
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Huan Liu
@liuhuan (Twitter)RT @FrontAIBigData: New Research: Commentary: A robust data-driven approach identifies four personality types across four large data sets h…
view full postFebruary 25, 2020
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Frontiers in AI & Big Data
@FrontAIBigData (Twitter)New Research: Commentary: A robust data-driven approach identifies four personality types across four large data sets https://t.co/kI0FrTXuvz #BigData
view full postFebruary 25, 2020
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Anton Pashkov
@anton_pashkov03 (Twitter)A robust data-driven approach identifies four personality types across four large data sets https://t.co/Q2zbMfrAFT
view full postSeptember 22, 2019
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Rina Saltman
@rinatie_ceo (Twitter)RT @sendaitribune: (メモ)ヒトの性格の分類について、話はそう、単純ではないらしい。>>>A robust data-driven approach identifies four personality types across four large dat…
view full postAugust 2, 2019
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大隅典子@東北大学(『小説みたいに楽しく読める脳科学講義』羊土社より刊行)
@sendaitribune (Twitter)(メモ)ヒトの性格の分類について、話はそう、単純ではないらしい。>>>A robust data-driven approach identifies four personality types across four large data sets. https://t.co/Pgbkdaa3fv
view full postAugust 1, 2019
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Stephen Pimentel
@StephenPiment (Twitter)A robust data-driven approach identifies four personality types across four large data sets https://t.co/8EWgPi5yWZ
view full postMarch 20, 2019
Abstract Synopsis
- The study explores human personality by identifying four consistent personality types across four large datasets with over 15 million participants, offering a new approach to understanding personality classifications.
- It addresses the controversy around personality types, noting that previous small-scale studies lacked reproducibility, but this research provides strong evidence for four distinct types using a data-driven method.
- The findings also reveal that traditional clustering techniques might produce many false or spurious personality groups, emphasizing the need for more careful analysis when applying machine learning to big data in personality research.]
David Ashley Davies
@DavidAshleyDavi (Twitter)