Nav: Home

New model for predicting presidential election results based on television viewership

May 01, 2017

New Rochelle, NY, May 1, 2017--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, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website until May 22, 2017.

In the article entitled "Predicting Presidential Election Outcomes from What People Watch" coauthors

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."
-end-
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 (http://www.liebertpub.com/big)

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, GEN (Genetic Engineering & Biotechnology News), 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 (http://www.liebertpub.com.)

Mary Ann Liebert, Inc./Genetic Engineering News

Related Big Data Articles:

Big data could yield big discoveries in archaeology, Brown scholar says
Parker VanValkenburgh, an assistant professor of anthropology, curated a journal issue that explores the opportunities and challenges big data could bring to the field of archaeology.
Army develops big data approach to neuroscience
A big data approach to neuroscience promises to significantly improve our understanding of the relationship between brain activity and performance.
'Big data' for life sciences
Scientists have produced a co-regulation map of the human proteome, which was able to capture relationships between proteins that do not physically interact or co-localize.
Molecular big data, a new weapon for medicine
Being able to visualize the transmission of a virus in real-time during an outbreak, or to better adapt cancer treatment on the basis of the mutations present in a tumor's individual cells are only two examples of what molecular Big Data can bring to medicine and health globally.
Big data says food is too sweet
New research from the Monell Center analyzed nearly 400,000 food reviews posted by Amazon customers to gain real-world insight into the food choices that people make.
Querying big data just got universal
A universal query engine for big data that works across computing platforms could accelerate analytics research.
What 'Big Data' reveals about the diversity of species
'Big data' and large-scale analyses are critical for biodiversity research to find out how animal and plant species are distributed worldwide and how ecosystems function.
Big data takes aim at a big human problem
A James Cook University scientist is part of an international team that's used new 'big data' analysis to achieve a major advance in understanding neurological disorders such as Epilepsy, Alzheimer's and Parkinson's disease.
Small babies, big data
The first week of a newborn's life is a time of rapid biological change as the baby adapts to living outside the womb, suddenly exposed to new bacteria and viruses.
Using big data to help manage global natural assets
Research led by the University of Southampton is helping to tackle one of the biggest sustainability challenges -- looking after and nurturing the natural resources in the world around us.
More Big Data News and Big Data Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Uncharted
There's so much we've yet to explore–from outer space to the deep ocean to our own brains. This hour, Manoush goes on a journey through those uncharted places, led by TED Science Curator David Biello.
Now Playing: Science for the People

#555 Coronavirus
It's everywhere, and it felt disingenuous for us here at Science for the People to avoid it, so here is our episode on Coronavirus. It's ok to give this one a skip if this isn't what you want to listen to right now. Check out the links below for other great podcasts mentioned in the intro. Host Rachelle Saunders gets us up to date on what the Coronavirus is, how it spreads, and what we know and don't know with Dr Jason Kindrachuk, Assistant Professor in the Department of Medical Microbiology and infectious diseases at the University of Manitoba. And...
Now Playing: Radiolab

Dispatch 1: Numbers
In a recent Radiolab group huddle, with coronavirus unraveling around us, the team found themselves grappling with all the numbers connected to COVID-19. Our new found 6 foot bubbles of personal space. Three percent mortality rate (or 1, or 2, or 4). 7,000 cases (now, much much more). So in the wake of that meeting, we reflect on the onslaught of numbers - what they reveal, and what they hide.  Support Radiolab today at Radiolab.org/donate.