Big Data Analytics — Nostradamus of the 21st Century

Big Data Analytics — Nostradamus of the 21st Century

Published By: Griffith University, 11/30/2016

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Researchers at Griffith University successfully predicted the winner of the 2016 presidential election, including the outcomes in 49 out of 50 states, using data collected from social media interactions. The prediction ran contrary to general expectations based on polling, suggesting that more accurate election predictions can be obtained by analyzing social media interactions — which requires large-scale data analytics.

Extended Discussion Questions

  • Polling, which can be conducted and analyzed without computers, has traditionally been used as a predictor for elections.
    • Would it be possible to analyze such a large amount of interaction data by hand (without automatic analysis)?
    • Could you perform a study like this using offline interaction data? How would you gather it?
  • If big-data analysis of social media proves to be a consistently more accurate predictor of elections, do you think it could eventually replace traditional polling? Why or why not?
  • Are there groups of voters who might be better represented in social media posts than in poll responses?  Groups who might be better represented in polls than social media? (If you’ve talked about norming) How could you compensate for that in the two different methods?

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

  • LO 7.2.1 Explain how computing has impacted innovations in other fields.

Global Impact Essential Knowledge:

  • EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.
  • EK 7.3.1J Technology enables the collection, use, and exploitation of information about, by, and for individuals, groups, and institutions.

Other CSP Big Ideas:

  • Idea 3 Data and Information
  • Idea 4 Algorithms

Banner Image: “Network Visualization – Violet – Offset Crop”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0

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