1. The document analyzes factors that lead to content going viral on social media, such as emotions, author influence, and content type. 2. Models like random forests and support vector machines were used to predict popularity based on features like title sentiment and LIWC analysis. These achieved over 84% accuracy. 3. Causality analysis through randomized experiments found that altering article tone to induce more negative emotions increased likes and comments, but decreased shares, indicating anger may provoke reaction but not widespread sharing.

1. WHAT MAKES
CONTENT
GO VIRAL?

2. SPOILER ALERT
It’s not this!

3. (P.S. OUR INSPIRATION)

4. 1SOCIAL CURRENCY
People talk about what
makes them look good.

5. 2 TRIGGERS
People talk about content
that reminds them of something.

6. 3 EMOTIONS
When we care,
we share.

7. 4 PUBLIC
If others are talking about it,
people are more likely to share.

8. 5PRACTICAL VALUE
People will share anything
that has practical value.

9. 6 STORIES
People will share content
that has a great narrative.

10. OUR
FOCUS?

11. EMOTIONS!

12. OUR
OBJECTIVES?

13. WHAT ARE THE OBJECTIVES?
Determine the factors that lead to
an article going viral on social
media.
Predict the popularity of a given
article on social media.
Determine the causal effect of
emotions on an article’s popularity.
1.
2.
3.

14. LET’S DEFINE VIRAL FIRST
A BRIEF INTRODUCTION

15. WHY DO PEOPLE SHARE ANYWAY?
WHY?
AWE
LAUGHTER
AMUSEMENT
JOY
ANGER
EMPATHY
SURPRISE
SADNESS
“Must. Share. Now.” *clicks*
“ROFLMAOOOOOOO”
“Wow, this is quite something.”
“I’m feeling good!”
“NO WAAAAAAY!”
“Oh no! :(”
“Damn, that took me by surprise!”
“Just chill. We’re all gonna die anyways.” *sigh*

16. OUR
METHODOLOGY?

17. LET’S OUTLINE OUR METHODOLOGY
HOW DID WE SOLVE IT?
SCRAPING OF ARTICLES
FROM MEDIUM
DATA CLEANING
& PRE-PROCESSING
FEATURE
ENGINEERING & LIWC
1 2 3 4
MODEL
BUILDING
5
CAUSALITY
ANALYSIS (RCT)

18. OUR DATA SOURCE
WHAT DATA DID WE USE?
NUMBER OF ARTICLES: 5011
CATEGORIES: Social Media, Entrepreneurship, Culture, Technology, Self, Politics, Media
DATE RANGE: October 25th, 2013 - March 30th, 2018

19. DATA SCRAPED
WHAT DATA DID WE USE?
Title
Author
Followed By
Tags
Date Published
Paragraphs
Images
Bullets
Links
Claps (DV)
Author Followed By
Author Following
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.

20. LINGUISTIC INQUIRY & WORD COUNT (LIWC)
WHAT DATA DID WE USE?
Explains how the words
we use in everyday
language reveal our
thoughts, feelings,
personality, and
motivations.

21. FEATURE ENGINEERING
WHAT DATA DID WE USE?
1. Images Exist
2. Number of Images
3. Bullets Exist
4. Number of Bullets
Article
Content
Time
5. Links Exists
6. Number of Links
7. Tags
8. Number of Tags
1. Date Published
2. Days Since Published
3. Day
4. Month
5. Year
1. Title Sentiment
2. Title Word Count
3. Title Tone
4. Title Analytic
5. Title Authentic
6. Title Tone
7. Title Six Letter Words
8. Tile I
9. Title We
10. Title You
11. Title She/He
12. Title They
13. Title Compare
A B Social Media
1. Facebook Shares
2. Facebook Comments
3. Facebook Reactions
4. LinkedIn Shares
5. Pinterest Shares
D
E
Author
1. Author
2. Author Followers
3. Author Following
C
14. Title Interrogation
15. Title Number
16. Title Positive Emotions
17. Title Negative Emotions
18. Title Anxiety
19. Title Anger
20. Title Sad
21. Title Cause
22. Title Sexual
23. Title Power
24. Title Risk
25. Title Focus Past
26. Title Focus Present
27. Title Focus Future
28. Title Religion
29. Title Swear
30. Title Net Speak
31. Title QMark
32. Content Word Count
33. Content Analytic
34. Content Authentic
35. Content Tone
36. Content Six Letter Words
37. Content I
38. Content We
39. Content You
40. Content She/He
41. Content They
42. Content Compare
43. Content Interrogation
44. Content Number
45. Content Positive Emotion
46. Content Negative Emotion
47. Content Anxiety
48. Content Anger
49. Content Sad
50. Content Cause
51. Content Sexual
52. Content Power
40. Content Risk
41. Content Focus Past
42. Content Focus Present
43. Content Focus Future
44. Content Religion
45. Content Swear
46. Content Net Speak
47. Content QMark
48. Content Words Per Sentence
49. Concept Count
50. Classification Label
51. Classification Confidence

22. HOW ARE THE CLAPS CATEGORISED?
WHAT DATA DID WE USE?
LOW: < 1,000 (3,893)
MEDIUM: >= 1,000 & < 3,000 (604)
HIGH: >= 3,000 & < 10,000 (273)
VIRAL: >= 10,000 (62)

23. OUR
MODELS?

24. THE MODELS!
WHICH MODELS DID WE USE?
RANDOM
FOREST
SUPPORT VECTOR
MACHINES (SVM)
1 2

25. FEATURE ENGINEERING
WHAT DATA DID WE USE?
1. Images Exist
2. Number of Images
3. Bullets Exist
4. Number of Bullets
Article
Content
Time
5. Links Exists
6. Number of Links
7. Tags
8. Number of Tags
1. Date Published
2. Days Since Published
3. Day
4. Month
5. Year
1. Title Sentiment
2. Title Word Count
3. Title Tone
4. Title Analytic
5. Title Authentic
6. Title Tone
7. Title Six Letter Words
8. Tile I
9. Title We
10. Title You
11. Title She/He
12. Title They
13. Title Compare
A B Social Media
1. Facebook Shares
2. Facebook Comments
3. Facebook Reactions
4. LinkedIn Shares
5. Pinterest Shares
D
E
Author
1. Author
2. Author Followers
3. Author Following
C
14. Title Interrogation
15. Title Number
16. Title Positive Emotions
17. Title Negative Emotions
18. Title Anxiety
19. Title Anger
20. Title Sad
21. Title Cause
22. Title Sexual
23. Title Power
24. Title Risk
25. Title Focus Past
26. Title Focus Present
27. Title Focus Future
28. Title Religion
29. Title Swear
30. Title Net Speak
31. Title QMark
32. Content Word Count
33. Content Analytic
34. Content Authentic
35. Content Tone
36. Content Six Letter Words
37. Content I
38. Content We
39. Content You
40. Content She/He
41. Content They
42. Content Compare
43. Content Interrogation
44. Content Number
45. Content Positive Emotion
46. Content Negative Emotion
47. Content Anxiety
48. Content Anger
49. Content Sad
50. Content Cause
51. Content Sexual
52. Content Power
40. Content Risk
41. Content Focus Past
42. Content Focus Present
43. Content Focus Future
44. Content Religion
45. Content Swear
46. Content Net Speak
47. Content QMark
48. Content Words Per Sentence
49. Concept Count
50. Classification Label
51. Classification Confidence

26. WHAT ARE THE RESULTS?
FOR THE STATISTICIANS
84.6% 84.2%
RANDOM FOREST SUPPORT VECTOR
MACHINES

27. VARIABLE IMPORTANCE PLOT
FOR THE STATISTICIANS

28. WHAT SHOULD YOU FOCUS ON?
FOR THE PUBLISHERS
AUTHOR LONG FORM MORE IMAGES
CONTENT
TYPE
CONTENT
POWER
NEGATIVE
EMOTIONS

29. OUR
TIPS?

30. 1 AUTHOR
Authors with more social media
presence tend to produce viral content.

31. 2 LONG FORM
Long form content engages users and
hence is more likely to be shared.

32. 3 IMAGES
A picture speaks a 1000 words.
More images lead to more shares.

33. 4CONTENT TYPE
The content topic is crucial and
readers need to associate with it.

34. 5POWER CONTENT
Content with references to social
hierarchies and society do better.

35. 6 EMOTIONS
Content that generates negative
emotions tend to be shared more.

36. ECONOMETRIC
MODELS?

37. WHAT DID WE TRY?
ECONOMETRICS! ECONOMETRICS!
1
2
3
4
TWO-STAGE LEAST SQUARES
THREE-STAGE LEAST SQUARES
SEEMINGLY UNRELATED REGRESSIONS (SUR)

38. WHAT DID WE GO WITH?
ECONOMETRICS! ECONOMETRICS!
1
2
3
4
TWO-STAGE LEAST SQUARES
THREE-STAGE LEAST SQUARES
SEEMINGLY UNRELATED REGRESSIONS (SUR)

39. WHAT DID WE GET?
ECONOMETRICS! ECONOMETRICS!
EQUATION 1 EQUATION 2
WALD TEST
(INSTRUMENT RELEVANCE)

40. CAUSALITY ANALYSIS OF EMOTIONS
RANDOMISED CONTROL TRIALS
ORGINAL ARTICLE EDITED ARTICLE

41. THE RESULTS?
RANDOMISED CONTROL TRIALS
1
2
3
4
ARTICLE CHOICE OF WORDS AFFECTS EMOTIONS. USING
NEGATIVE WORDS MAKES READERS MORE AGITATED.
EDITED ARTICLE GETS MORE LIKES - ANGER POTENTIALLY
TRIGGERS MORE THAN INSPIRATIONAL.
EDITED ARTICLE GETS MORE COMMENTS THAN ORIGINAL.
ANGER PROVOKES REACTION.
EDITED ARTICLE GOT LESS SHARES. :(
2

42. !