How Big Data Polls Helped Predict the Outcome of Elections
Elections are one of the most crucial events in a democracy, where people choose their leaders. But predicting election outcomes has long been a challenge for pollsters due to the complexity of human behavior. However, with the advent of big data technologies, election predictions have become more accurate than ever before. In this article, we discuss how big data polls helped predict the outcome of elections.
What is Big Data Polling?
Big data polling refers to the collection, analysis, and interpretation of large-scale data sets to predict human behavior based on past actions. Election pollsters can use big data to segment voters based on demographics, past voting behavior, social media engagement, and other relevant factors. By analyzing patterns and trends in these massive datasets, pollsters can predict election outcomes with greater accuracy than traditional polling methods.
The Rise of Big Data Polling
Big data polling gained popularity during the 2008 US Presidential elections when the Obama campaign used big data to segment voters and target them with personalized messages. The campaign targeted specific demographics such as young people and minorities, which helped secure Obama’s victory. Since then, big data polling has become an essential tool for political campaigns worldwide.
In the 2012 US Presidential election, big data played an even larger role. The Obama campaign used social media engagement data to target voters with personalized ads on Facebook. Analysis of big data also helped the Obama campaign allocate resources more efficiently, leading to a victory.
How Big Data Impacted the 2016 US Presidential Elections
In the 2016 US Presidential elections, big data played a massive role in predicting the outcome. Political campaigns used big data to segment voters by demographics, age, race, and other factors to target them with customized messages. Also, social media platforms like Facebook and Twitter gave political campaigns access to vast amounts of data on users’ behavior, including likes, shares, comments, and hashtags.
However, big data’s predictions in the 2016 US Presidential elections were not entirely accurate. The predictions underestimated Trump’s support among rural voters, leading to incorrect predictions in some states.
The Effect of Social Media on Election Predictions
Social media plays an immense role in big data polling. Social media platforms generate massive amounts of data on users’ behavior, providing an invaluable source of information for pollsters.
For example, Facebook allows political campaigns to use its vast data trove on users’ interests, likes, and behaviors to target specific demographics with tailored messages. By analyzing social media data, pollsters can identify candidate support, gauge public sentiment, and predict the election outcome.
Conclusion
Big data polling has revolutionized political campaigning by providing accurate predictions of voter behavior. By analyzing massive datasets on past voting behavior, demographics, and social media engagement, pollsters can segment voters and target them with customized messages. Using big data, political campaigns can allocate resources more efficiently, leading to a higher return on investment. As big data technologies continue to evolve, big data polling will become even more accurate and reliable, making election predictions more accurate than ever before.
