C. Danforth

Publications
Influence

Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter

P. Dodds, K. D. Harris, Isabel M. Kloumann, C. Bliss, C. Danforth
Biology, Computer Science
PloS one
26 January 2011

We construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. Expand

Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents

P. Dodds, C. Danforth
Sociology, Computer Science
ArXiv
1 August 2010

The importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Expand

Instagram photos reveal predictive markers of depression

Andrew G. Reece, C. Danforth
Computer Science, Physics
EPJ Data Science
10 August 2016

Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Expand

The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place

L. Mitchell, K. D. Harris, M. Frank, P. Dodds, C. Danforth
Geography, Physics
PloS one
13 February 2013

We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. Expand

Characterizing the Google Books Corpus: Strong Limits to Inferences of Socio-Cultural and Linguistic Evolution

E. Pechenick, C. Danforth, P. Dodds
History, Computer Science
PloS one
5 January 2015

It is tempting to treat frequency trends from the Google Books data sets as indicators of the “true” popularity of various words and phrases. Expand

An evolutionary algorithm approach to link prediction in dynamic social networks

C. Bliss, M. Frank, C. Danforth, P. Dodds
Computer Science, Physics
J. Comput. Sci.
23 April 2013

We provide 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. Expand

Forecasting the onset and course of mental illness with Twitter data

Andrew G. Reece, A. J. Reagan, Katharina L. M. Lix, P. Dodds, C. Danforth, E. Langer
Psychology, Computer Science
Scientific Reports
27 August 2016

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Expand

Estimating and Correcting Global Weather Model Error

C. Danforth, E. Kalnay, T. Miyoshi
Mathematics
1 February 2007

The purpose of the present study is to explore the feasibility of estimating and correcting systematic model errors using a simple and efficient procedure, inspired by papers by Leith as well as… Expand

Twitter reciprocal reply networks exhibit assortativity with respect to happiness

C. Bliss, Isabel M. Kloumann, K. D. Harris, C. Danforth, P. Dodds
Computer Science, Physics
J. Comput. Sci.
5 December 2011

We construct and examine the social network structure and dynamics of Twitter users over the time scales of days, weeks, and months. Expand

Positivity of the English Language

Isabel M. Kloumann, C. Danforth, K. D. Harris, C. Bliss, P. Dodds
Psychology, Computer Science
PloS one
25 August 2011

We show that the human-perceived positivity of over 10,000 of the most frequently used English words exhibits a clear positive bias. Expand

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