How Can We Use Fan Data in the Music Industry to Increase Voter Turnout?

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Hackerman B17

Elections are decided by the size of concert venues. As music fans, we know the power of stan communities. At the same time, culture and voting have worked together for years. Yet now with the power of data, how can we activate music fan communities to statistically increase voter turnout and more?

Led by SNF Agora Visiting Fellow Emily White, this is an informative, informal, and interactive brainstorming workshop to share how Emily and Professor Eliza O’Reilly along with JHU students Jasmine Lafita, Jackson Shapiro, and Tomoya Furutani have been analyzing fan/stan data on the top trending artists in key locations whose electoral margins are decided by the size of concert venues. They have been simultaneously comparing artists’ fans’ demographic data with traditionally low turnout groups to share which artists can impact increasing voter turnout the most. We also want to hear from you! How do you feel we can utilize fan/stan demographic data for artists and their teams to make informed decisions on supporting important causes?

No preparation required to attend. Light refreshments will be served.

About our speakers:

Emily White is the founder of #iVoted, an organization that produces record-breaking events and activates entertainment venues to admit fans who show a selfie either from outside their polling place or at home with a blank and unmarked ballot. In 2018 #iVoted activated over 150 concerts in 37 states. In 2020, the group pivoted to launch #iVoted Festival, which was the largest digital concert in history and featured over 400 artists, and followed in January 2021 with the #iVoted Festival Georgia. In 2022, the inaugural #iVoted Early Sweepstakes launched with over 500 partner concerts. All entrants were also RSVP’d to #iVoted Festival 2022’s election night webcast. #iVoted events have featured Billie Eilish, Run the Jewels, Phish, Steph Curry, and more.


Eliza O’Reilly is an Assistant Professor in the Department of Applied Mathematics and Statistics. Her research in the mathematics of data science lies at the intersections of stochastic and convex geometry, high dimensional probability, and statistical learning. She was previously a postdoc at the California Institute of Technology and received her PhD in mathematics from the University of Texas at Austin in 2019.