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Jake Ryland Williams

Showing results (1-10 of 9) with videos related to

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Plos One|March 20, 2019
A scalable machine learning approach for measuring violent and peaceful forms of political protest participation with social media dataLefteris Jason Anastasopoulos, Jake Ryland Williams
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|June 13, 2015
Text mixing shapes the anatomy of rank-frequency distributionsJake Ryland Williams, James P Bagrow, Christopher M Danforth, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|November 14, 2015
Identifying missing dictionary entries with frequency-conserving context modelsJake Ryland Williams, Eric M Clark, James P Bagrow, et al.
Scientific Reports|August 12, 2015
Zipf's law holds for phrases, not wordsJake Ryland Williams, Paul R Lessard, Suma Desu, et al.
Plos One|July 14, 2016
Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on TwitterEric M Clark, Chris A Jones, Jake Ryland Williams, et al.
Physical Review. E|June 17, 2017
Simon's fundamental rich-get-richer model entails a dominant first-mover advantagePeter Sheridan Dodds, David Rushing Dewhurst, Fletcher F Hazlehurst, et al.
Plos One|February 11, 2017
The Lexicocalorimeter: Gauging public health through caloric input and output on social mediaSharon E Alajajian, Jake Ryland Williams, Andrew J Reagan, et al.
Proceedings of the National Academy of Sciences of the United States of America|February 13, 2015
Human language reveals a universal positivity biasPeter Sheridan Dodds, Eric M Clark, Suma Desu, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 23, 2015
Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristicsPeter Sheridan Dodds, Eric M Clark, Suma Desu, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Plos One|March 20, 2019
A scalable machine learning approach for measuring violent and peaceful forms of political protest participation with social media dataLefteris Jason Anastasopoulos, Jake Ryland Williams
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|June 13, 2015
Text mixing shapes the anatomy of rank-frequency distributionsJake Ryland Williams, James P Bagrow, Christopher M Danforth, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|November 14, 2015
Identifying missing dictionary entries with frequency-conserving context modelsJake Ryland Williams, Eric M Clark, James P Bagrow, et al.
Scientific Reports|August 12, 2015
Zipf's law holds for phrases, not wordsJake Ryland Williams, Paul R Lessard, Suma Desu, et al.
Plos One|July 14, 2016
Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on TwitterEric M Clark, Chris A Jones, Jake Ryland Williams, et al.
Physical Review. E|June 17, 2017
Simon's fundamental rich-get-richer model entails a dominant first-mover advantagePeter Sheridan Dodds, David Rushing Dewhurst, Fletcher F Hazlehurst, et al.
Plos One|February 11, 2017
The Lexicocalorimeter: Gauging public health through caloric input and output on social mediaSharon E Alajajian, Jake Ryland Williams, Andrew J Reagan, et al.
Proceedings of the National Academy of Sciences of the United States of America|February 13, 2015
Human language reveals a universal positivity biasPeter Sheridan Dodds, Eric M Clark, Suma Desu, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 23, 2015
Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristicsPeter Sheridan Dodds, Eric M Clark, Suma Desu, et al.
Pageof 1