Using Facebook Data to Predict the 2016 U.S. Presidential Election

46 Pages Posted: 18 Nov 2021

See all articles by Keng-Chi Chang

Keng-Chi Chang

University of California, San Diego (UCSD)

Chun-Fang Chiang

National Taiwan University - Department of Economics

Ming-Jen Lin

National Taiwan University - Department of Economics

Date Written: October 15, 2018

Abstract

We use 19 billion likes on the posts of top 2000 U.S. fan pages on Facebook from 2015 to 2016 to measure the dynamic ideological positions for politicians, news outlets, and users at the national and state levels. We then use these measures to derive support rates for 2016 presidential candidates in all 50 states, to predict the election, and to compare them with state-level polls and actual vote shares. We find that: (1) Assuming that users vote for candidates closer to their own ideological positions, support rates calculated using Facebook predict that Trump will win the electoral college vote while Clinton will win the popular vote. (2) State-level Facebook support rates track state-level polling averages and pass the cointegration test, showing two time series share similar trends. (3) Compared with actual vote shares, polls generally have smaller margin of errors, but polls also often overestimate Clinton’s support in right-leaning states. Overall, we provide a method to forecast elections at low cost, in real time, and based on passively revealed preference and little researcher discretion.

Keywords: Facebook, social media, ideology, scaling, dimension reduction, election, polling, public opinion, data science

Suggested Citation

Chang, Keng-Chi and Chiang, Chun-Fang and Lin, Ming-Jen, Using Facebook Data to Predict the 2016 U.S. Presidential Election (October 15, 2018). Available at SSRN: https://ssrn.com/abstract=3939262 or http://dx.doi.org/10.2139/ssrn.3939262

Keng-Chi Chang (Contact Author)

University of California, San Diego (UCSD) ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0112
United States

Chun-Fang Chiang

National Taiwan University - Department of Economics ( email )

21 Hsiu Chow Rd
Taipei, 10020
Taiwan

Ming-Jen Lin

National Taiwan University - Department of Economics ( email )

21 Hsiu Chow Rd
Taipei, 10020
Taiwan

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
4
Abstract Views
216
PlumX Metrics