Characterizing the dynamics of cultural phenomena with Tweets
Conference Year
January 2019
Abstract
Quantifying public discourse is a topic of perennial interest in the social sciences. With the advent of social media, researchers now have the ability to collect real-time, high-volume conversational data from a diverse group of individuals. The global micro-blogging service Twitter has become a prominent conduit for both information dissemination and interpersonal conversation. In this work, we characterize common motifs in collective attention at varying time scales, and cataloging similarities and divergences in the spectral properties of time series derived from observed word and phrase frequencies (N-grams). This corpus enables automated event detection and extraction of "shapelets"---characteristic shapes common to many N-gram time series. Using a corpus of 1-grams---collection of words---parsed from approximately 10% of all tweets authored between 2009 and 2019, we demonstrate the dynamics of social media conversations surrounding major hurricanes and US presidents.
Primary Faculty Mentor Name
Peter Dodds
Secondary Mentor Name
Chris Danforth
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Complex Systems
Primary Research Category
Social Sciences
Secondary Research Category
Engineering & Physical Sciences
Characterizing the dynamics of cultural phenomena with Tweets
Quantifying public discourse is a topic of perennial interest in the social sciences. With the advent of social media, researchers now have the ability to collect real-time, high-volume conversational data from a diverse group of individuals. The global micro-blogging service Twitter has become a prominent conduit for both information dissemination and interpersonal conversation. In this work, we characterize common motifs in collective attention at varying time scales, and cataloging similarities and divergences in the spectral properties of time series derived from observed word and phrase frequencies (N-grams). This corpus enables automated event detection and extraction of "shapelets"---characteristic shapes common to many N-gram time series. Using a corpus of 1-grams---collection of words---parsed from approximately 10% of all tweets authored between 2009 and 2019, we demonstrate the dynamics of social media conversations surrounding major hurricanes and US presidents.