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Can you predict who will win the US election?


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Who would win the battle for the White House to become the next President of the United States was a topic of hot debate in 2012.

Much of that debate was taking place online, with plenty of people blogging, tweeting or updating social media with their thoughts on Mitt Romney versus Barack Obama.


This provided us with a rich source of information about what people were thinking and feeling about the election race. So today I've decided to cover Techniques of Digital Data Analysis that are used to predict the US election. And perhaps the 2012 election will be remembered as the first election where big data analysis played a crucial role and had a tremendous impact on the outcome of the presidential election.

I am fairly familiar with the above mentioned techniques, because I had an opportunity to meet the CEO of EMC company on January 2013 in Singapore. EMC was one of a selected few companies that Twitter had entrusted to syndicate and provide access to the full Twitter feed for use in internal analytics applications for Obama's campaign in 2012. In my humble opinion that was the reason that in 2015 this company was sold to Dell for $67B in largest deal in Tech history.

The techniques of big data analysis remain the same, so let’s jump to year 2016 and see what social media data is used to predict the US election nowadays.

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Can you predict who will win the US election?

  1. 1. Can you predict who will win the US election? 2016 © CYBER GATES Samvel Gevorgyan CEO, CYBER GATES MSc Cyber Security from C-DAC University
  2. 2. Who Dares Wins WWW.CYBERGATES.ORG 2016 2012
  3. 3. Who will win the 2016 U.S. presidential election? WWW.CYBERGATES.ORG Source:
  4. 4. How good are you at predicting election winners? WWW.CYBERGATES.ORG Dell Buys EMC For $67B In Largest Deal In Tech History EMC, a company worth twice Dell's value.
  5. 5. What happens in an internet minute? WWW.CYBERGATES.ORG Think before you post online what you are thinking and feeling about the election race. Facebook 293,000 statuses are updated 31,25 million messages are sent Twitter 440,640 new tweets go online, etc. Sources:   statistics/
  6. 6. Data analysis WWW.CYBERGATES.ORG Data is Messy and Noisy 1.Data: raw data that has not been processed for use. 2.Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions 3.Knowledge: application of data and information; answers "how" questions 4.Understanding: appreciation of "why" 5.Wisdom: evaluated understanding.
  7. 7. Text Mining and Sentiment Analysis WWW.CYBERGATES.ORG Source: Negative Words A abysmal adverse alarming angry annoy anxious apathy appalling atrocious awful B bad banal barbed belligerent bemoan beneath boring broken Positive Words A absolutely adorable accepted acclaimed accomplish accomplishment achievement action active admire adventure affirmative affluent agree agreeable amazing angelic appealing approve
  8. 8. Text Mining and Sentiment Analysis WWW.CYBERGATES.ORG positive negative The objective is to classify to tweets as Positive, Neutral, or Negative.
  9. 9. US presidential candidate Twitter analysis WWW.CYBERGATES.ORG Source:
  10. 10. Positive and negative reactions WWW.CYBERGATES.ORG Assume you hate Donald Trump and you post some negative thoughts about him online. Can we predict your friends’ thoughts based on their likes or other reactions?
  11. 11. Facebook Reactions: anger vs laughs WWW.CYBERGATES.ORG Source:
  12. 12. WWW.CYBERGATES.ORG Source: Facebook Reactions: likes vs comments vs shares
  13. 13. Other JUICY information WWW.CYBERGATES.ORG Metadata  Genders  Ages  Locations  Interests
  14. 14. Thank You!  Corporate website  Company profile on Twitter  Company fan page on Facebook  Company profile on LinkedIn  Company channel on Vimeo  Company channel on YouTube Contacts