image: Big Data, published quarterly online with open-access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website. view more
Credit: ©Mary Ann Liebert, Inc., publishers
New Rochelle, November 17, 2015--A provocative new study shows that big data-derived models developed and trained based on people's television viewing behavior in "safe" U.S. states can be used to forecast the presidential election outcomes in "swing" states. The model design, potential for its use in 2016 and beyond, and implications for the billions of dollars of advertising spent in presidential elections are discussed in an Open Access article published in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available to download free on the Big Data website.
"Does Television Viewership Predict Presidential Election Outcomes?" by Arash Barfar and Balaji Padmanabhan, University of South Florida, Tampa, describes the creation and training of a model using data from the 4 weeks leading up to the 2012 U.S. presidential election. The researchers selected several television programs with political relevance and collected data on minutes watched and percentage of viewers in "safe" states (where election outcomes are assumed), and used that data to train the model to predict the election outcomes in "swing" states (where outcomes are uncertain).
The results showed that 99 television programs had predictive accuracies greater than 59%, and three programs could predict outcomes with an accuracy greater than 79%.
"This very interesting research demonstrates the prediction of election outcomes at the state and county levels based on an analysis of television viewership across the country," says Big Data Editor-in-Chief Vasant Dhar, Professor at the Stern School of Business, New York University. "The results from the predictive model provide useful insights into some of the major drivers that drove 2012 election results. It will be very interesting to see the model applied to the 2016 elections."
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About the Journal
Big Data, published quarterly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website.
About the Publisher
Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known for establishing authoritative medical and biomedical peer-reviewed journals, including OMICS: A Journal of Integrative Biology, Journal of Computational Biology, New Space, and 3D Printing and Additive Manufacturing. Its biotechnology trade magazine, Genetic Engineering & Biotechnology News (GEN), was the first in its field and is today the industry's most widely read publication worldwide. A complete list of the firm's more than 80 journals, newsmagazines, and books is available on the Mary Ann Liebert, Inc., publishers website.
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Big Data