Kameron Decker Harris

TL;DR: Examination of expressions made on the online, global microblog and social networking service Twitter is examined, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years.

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.

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.

TL;DR: This work investigates how the dimension of a representation formed by a population of neurons depends on how many inputs each neuron receives and what this implies for learning associations and predicts that the dimensions of the cerebellar granule-cell and Drosophila Kenyon-cell representations are maximized at degrees of synaptic connectivity that match those observed anatomically.

TL;DR: This work constructs and examines the revealed social network structure and dynamics within Twitter over the time scales of days, weeks, and months, and finds users’ average happiness scores to be positively and significantly correlated with those of users one, two, and three links away.