34 citations · 104 across the 8 of their papers we have counts for
8 papers
Instagram photos reveal predictive markers of depression
Andrew G. Reece, Christopher M. Danforth
Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted fro…
Public Opinion Polling with Twitter
Emily M. Cody, Andrew J. Reagan, Peter Sheridan Dodds +1
Solicited public opinion surveys reach a limited subpopulation of willing participants and are expensive to conduct, leading to poor time resolution and a restricted pool of expert…
Robustness of Spatial Micronetworks
Thomas C. McAndrew, Christopher M. Danforth, James P. Bagrow
Power lines, roadways, pipelines and other physical infrastructure are critical to modern society. These structures may be viewed as spatial networks where geographic distances pla…
Constructing a taxonomy of fine-grained human movement and activity motifs through social media
Morgan R. Frank, Jake Ryland Williams, Lewis Mitchell +3
Profiting from the emergence of web-scale social data sets, numerous recent studies have systematically explored human mobility patterns over large populations and large time scale…
Computational Studies of Multiple-Particle Nonlinear Dynamics in a Spatio-Temporally periodic potential
Owen D. Myers, Junru Wu, Jeffrey S. Marshall +1
The spatio-temporally periodic (STP) potential is interesting in Physics due to the intimate coupling between its time and spatial components. In this paper we begin with a brief d…
Text mixing shapes the anatomy of rank-frequency distributions: A modern Zipfian mechanics for natural language
Jake Ryland Williams, James P. Bagrow, Christopher M. Danforth +1
Natural languages are full of rules and exceptions. One of the most famous quantitative rules is Zipf's law which states that the frequency of occurrence of a word is approximately…