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. Photo: usatoday.com 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. Read as an article: http://news.cybergates.org/en/articles/can-you-predict-who-will-win-the-us-election

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. Who Dares Wins
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2016
2012

3. Who will win the 2016 U.S.
presidential election?
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Source: https://www.predictit.org/Market/1234/Who-will-win-the-2016-US-presidential-election

4. How good are you at predicting
election winners?
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Dell Buys EMC For $67B In
Largest Deal In Tech History
EMC, a company worth twice Dell's value.

5. What happens in an internet
minute?
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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:
 http://www.internetlivestats.com
 https://zephoria.com/top-15-valuable-facebook-
statistics/

6. Data analysis
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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. Text Mining and Sentiment Analysis
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Source: http://www.enchantedlearning.com/wordlist/negativewords.shtml
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. Text Mining and Sentiment Analysis
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positive
negative
The objective is to classify to tweets as Positive, Neutral, or Negative.

9. US presidential candidate Twitter
analysis
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Source: https://my.datameer.com/apps/US_Presidential_Candidates/details

10. Positive and negative reactions
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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. Facebook Reactions:
anger vs laughs
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Source: http://vocativ.com

12. WWW.CYBERGATES.ORG
Source: http://vocativ.com
Facebook Reactions:
likes vs comments vs shares

13. Other JUICY information
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Metadata
 Genders
 Ages
 Locations
 Interests

14. Thank You!
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