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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Using Facebook data to predict the 2016 U.S. presidential election.

Keng-Chi Chang¹, Chun-Fang Chiang², Ming-Jen Lin²

¹Department of Political Science, University of California, San Diego, La Jolla, California, United States of America.

|December 1, 2021

Summary

Facebook likes reveal dynamic user ideologies, predicting the 2016 US presidential election

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Area of Science:

Computational Social Science
Political Science
Social Media Analytics

Background:

Understanding user political leanings is crucial for election forecasting.
Traditional polling methods have limitations in real-time data acquisition and potential biases.

Purpose of the Study:

To develop a low-cost, real-time election forecasting method using social media data.
To measure dynamic ideological positions of users and political entities on Facebook.
To predict the 2016 US presidential election outcomes at the state and national levels.

Main Methods:

Utilized 19 billion likes from top 2000 US fan pages on Facebook (2015-2016).
Derived dynamic ideological positions for politicians, news outlets, and users.
Calculated state-level candidate support rates based on user ideological alignment.

Main Results:

Facebook data predicted Trump winning the electoral college and Clinton winning the popular vote.
State-level Facebook support rates demonstrated similar trends to state-level polls (cointegration).
Polls showed smaller margins of error but often overestimated Clinton's support in conservative states.

Conclusions:

Social media likes offer a viable, low-cost method for real-time election forecasting.
Passively revealed preferences on platforms like Facebook provide valuable insights into voter behavior.
This approach minimizes researcher discretion and offers a scalable alternative to traditional polling.