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New model for predicting presidential election results based on television viewership


New model for predicting presidential election results based on television viewership

Credit: 2017, Mary Ann Liebert, Inc., publishers A comparative study on predicting presidential election outcomes using models built on watch data for thousands of television shows has found that simple ”single-show models” can have high predictive accuracy. Given the recent performance of poll-data-driven models in predicting the 2016 U.S. presidential election and the Brexit vote outcomes, models based on television viewership offer an accurate predictive tool, as reported in Big Data.

Arash Barfar, PhD, University of Nevada, Reno, and Balaji Padmanabhan, PhD, University of South Florida, Tampa, explore the use of predictive models built on Nielsen national watch data for both partisan and non-partisan television shows. They analyzed the model using data from the 2012 presidential election and then applied it to viewership information gathered during the 2016 presidential primaries. The researchers discuss the practical implications of their findings for campaigns and the media, and how political parties might be able to use this model to target certain shows with specific messaging.
”Bias in polling data can be difficult to detect in cases of highly infrequent outcomes such as a presidential elections,” says Big Data Editor-in-Chief Vasant Dhar, Professor at the Stern School of Business and the Center for Data Science at New York University. ”We learned this lesson in the recent U.S. election, in which models based on such data were uniformly wrong. On the other hand, if what people watch is based on some latent tendencies that correlate with other economic, political, and social issues germane to an election, such data can be predictive of who you are likely to vote for.
Explore further:Can data on TV watching predict presidential election outcomes?
More information: Arash Barfar et al. Predicting Presidential Election Outcomes from What People Watch, Big Data (2017). DOI: 10.1089/big.2017.0013

Provided by:Mary Ann Liebert, Inc

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