M
Morgan R. Frank
Researcher at Massachusetts Institute of Technology
Publications - 41
Citations - 2149
Morgan R. Frank is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Population & Computer science. The author has an hindex of 16, co-authored 36 publications receiving 1662 citations. Previous affiliations of Morgan R. Frank include University of Vermont & Stanford University.
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Journal ArticleDOI
The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place
Lewis Mitchell,Morgan R. Frank,Kameron Decker Harris,Kameron Decker Harris,Peter Sheridan Dodds,Christopher M. Danforth +5 more
TL;DR: The results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.
Journal ArticleDOI
Human language reveals a universal positivity bias
Peter Sheridan Dodds,Eric M. Clark,Suma Desu,Morgan R. Frank,Andrew J. Reagan,Jake Ryland Williams,Lewis Mitchell,Kameron Decker Harris,Isabel M. Kloumann,James P. Bagrow,Karine Megerdoomian,Matthew T. McMahon,Brian F. Tivnan,Brian F. Tivnan,Christopher M. Danforth +14 more
TL;DR: Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, evidence of a deep imprint of human sociality in language is presented, observing that the words of natural human language possess a universal positivity bias.
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Toward understanding the impact of artificial intelligence on labor
Morgan R. Frank,David H. Autor,James Bessen,Erik Brynjolfsson,Erik Brynjolfsson,Manuel Cebrian,David J. Deming,Maryann P. Feldman,Matthew Groh,José Lobo,Esteban Moro,Esteban Moro,Dashun Wang,Hyejin Youn,Iyad Rahwan,Iyad Rahwan +15 more
TL;DR: The barriers that inhibit scientists from measuring the effects of AI and automation on the future of work are discussed and a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior is recommended.
Journal ArticleDOI
An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
TL;DR: In this article, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is applied to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices.
Journal ArticleDOI
An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
TL;DR: This work provides an approach to predicting future links by applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices.