Emotions, Demographics and
Sociability in Twitter
Interactions
Kristina Lerman, Megha Arora, Luciano Gallegos,
Ponnurangam...
Who am I?
2
• A proud alumna of
• Going to join for MSCS, with a specialization in
data science
• Started working with Dr....
3
Motivation
Structure of social interactions shapes individual fitness and
well-being
• What factors affect social interact...
Motivation
Structure of social interactions shapes individual fitness and
well-being
• What factors affect social interact...
What we need?
Methodology
Results
Conclusions
What We Need
• What does the structure of social interactions look like?
• Social Tie strength and Mobility analysis from ...
What We Need
• What does the structure of social interactions look like?
• Social Tie strength and Mobility analysis from ...
What We Need
• What does the structure of social interactions look like?
• Social Tie strength and Mobility analysis from ...
What we need?
Methodology
Results
Conclusions
Data Collection
• 6M geo-tagged tweets
posted from around Los
Angeles County
• 340K users
• 2.6M mentions
6
Demographics Data
Obtained socioeconomic characteristics for all Los
Angeles County tracts from the 2012 US Census.
Income...
What is a Tract?
Relatively homogeneous
units with respect to
population characteristics,
economic status, and
living cond...
Emotion Analysis
-5 -1 +1 +5
Analyze text of tweets to measure emotions
SentiStrength to measure
Negativity | Positivity
9
Emotion Analysis
Analyze text of tweets to measure emotions
WKB (Warriner et al.) lexicon to measure
Valence | Arousal | D...
Emotion Analysis
Analyze text of tweets to measure emotions
WKB (Warriner et al.) lexicon to measure
Valence | Arousal | D...
Emotion Analysis
Analyze text of tweets to measure emotions
WKB (Warriner et al.) lexicon to measure
Valence | Arousal | D...
Emotion Analysis
Analyze text of tweets to measure emotions
WKB (Warriner et al.) lexicon to measure
Valence | Arousal | D...
Social Interactions Analysis
Social Tie Strength of tract i: how much do
people there interact with others?
Si : average s...
Social Interactions Analysis
Tie Strength
tract1 tract2
Si = 7.33 (Strong Ties) Si = 1.08 (Weak Ties)
12
Mobility Analysis
Spatial Diversity of tract i: how many other
places do people tweet from?
ni : number of tracts from whi...
What we need?
Methodology
Results
Conclusions
Weaker Ties → more Happiness
-0.36***
14
*p < 0.05, **p < 0.01, ***p < 0.001
… also
Stronger ties are
associated with
more...
Weaker Ties → lower Arousal and
higher Dominance
15
-0.36*** 0.14***-0.31***
*p < 0.05, **p < 0.01, ***p < 0.001
Weaker Ties → more Education,
more Income, fewer Hispanics
16
-0.12*** 0.35***-0.27***
*p < 0.05, **p < 0.01, ***p < 0.001
Higher Mobility → more Happiness
17
0.32***
*p < 0.05, **p < 0.01, ***p < 0.001
Higher Mobility → more Income,
more Education, fewer Hispanics
18
0.08** -0.27***0.35***
*p < 0.05, **p < 0.01, ***p < 0.0...
What we need?
Methodology
Results
Conclusions
Conclusions
Cognitive factors (emotions) and demographics of places
affect the quality of online social interactions
• Pla...
Conclusions
• Places with more Hispanic residents are associated with
stronger social ties and lower mobility (spatial
div...
Thank You!
21
@meghaarora42
marora@andrew.cmu.edu
Questions?
Upcoming SlideShare
Loading in …5
×

ICWSM 2016 paper presentation, Megha Arora

1,157 views

Published on

Megha Arora presented the paper at ICWSM 2016

Published in: Data & Analytics
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,157
On SlideShare
0
From Embeds
0
Number of Embeds
214
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

ICWSM 2016 paper presentation, Megha Arora

  1. 1. Emotions, Demographics and Sociability in Twitter Interactions Kristina Lerman, Megha Arora, Luciano Gallegos, Ponnurangam Kumaraguru (PK), David Garcia Session V: Users, Opinions and Attitudes 1 @ ICWSM’16 @meghaarora42 19th May 2016
  2. 2. Who am I? 2 • A proud alumna of • Going to join for MSCS, with a specialization in data science • Started working with Dr. Lerman at • Have been a part of for last 3 years
  3. 3. 3
  4. 4. Motivation Structure of social interactions shapes individual fitness and well-being • What factors affect social interactions? –Cognitive factors: emotions –Socio-economic factors: income, education, demographics • Can we empirically validate for online social interactions? *Glyphicons taken from http://www.flaticon.com/ 4
  5. 5. Motivation Structure of social interactions shapes individual fitness and well-being • What factors affect social interactions? –Cognitive factors: emotions –Socio-economic factors: income, education, demographics • Can we empirically validate for online social interactions? *Glyphicons taken from http://www.flaticon.com/ 4
  6. 6. What we need? Methodology Results Conclusions
  7. 7. What We Need • What does the structure of social interactions look like? • Social Tie strength and Mobility analysis from Twitter • How happy, how anxious, how powerful people feel? • Emotions • How much they earn; what are their education levels, their ethnicities? • Socio-economic characteristics 5
  8. 8. What We Need • What does the structure of social interactions look like? • Social Tie strength and Mobility analysis from Twitter • How happy, how anxious, how powerful people feel? • Emotions • How much they earn; what are their education levels, their ethnicities? • Socio-economic characteristics 5
  9. 9. What We Need • What does the structure of social interactions look like? • Social Tie strength and Mobility analysis from Twitter • How happy, how anxious, how powerful people feel? • Emotions • How much they earn; what are their education levels, their ethnicities? • Socio-economic characteristics 5
  10. 10. What we need? Methodology Results Conclusions
  11. 11. Data Collection • 6M geo-tagged tweets posted from around Los Angeles County • 340K users • 2.6M mentions 6
  12. 12. Demographics Data Obtained socioeconomic characteristics for all Los Angeles County tracts from the 2012 US Census. Income | Education | Ethnicity hsp 7
  13. 13. What is a Tract? Relatively homogeneous units with respect to population characteristics, economic status, and living conditions. 8 All tracts in the Los Angeles County
  14. 14. Emotion Analysis -5 -1 +1 +5 Analyze text of tweets to measure emotions SentiStrength to measure Negativity | Positivity 9
  15. 15. Emotion Analysis Analyze text of tweets to measure emotions WKB (Warriner et al.) lexicon to measure Valence | Arousal | Dominance 10
  16. 16. Emotion Analysis Analyze text of tweets to measure emotions WKB (Warriner et al.) lexicon to measure Valence | Arousal | Dominance Quantifies the level of pleasure or pleasantness expressed by a word → Happiness 10
  17. 17. Emotion Analysis Analyze text of tweets to measure emotions WKB (Warriner et al.) lexicon to measure Valence | Arousal | Dominance Quantifies the level of activity induced by the emotions associated with a word 10
  18. 18. Emotion Analysis Analyze text of tweets to measure emotions WKB (Warriner et al.) lexicon to measure Valence | Arousal | Dominance Quantifies the level of subjective power associated with an emotional word 10
  19. 19. Social Interactions Analysis Social Tie Strength of tract i: how much do people there interact with others? Si : average social tie strength for tract i wj : is the weight of the jth edge (number of times a user mentions another), and ki : number of distinct users mentioned in tract i 11
  20. 20. Social Interactions Analysis Tie Strength tract1 tract2 Si = 7.33 (Strong Ties) Si = 1.08 (Weak Ties) 12
  21. 21. Mobility Analysis Spatial Diversity of tract i: how many other places do people tweet from? ni : number of tracts from which users who tweeted from tract i also tweeted from, and pij : proportion of tweets posted by these users from tract j 13
  22. 22. What we need? Methodology Results Conclusions
  23. 23. Weaker Ties → more Happiness -0.36*** 14 *p < 0.05, **p < 0.01, ***p < 0.001 … also Stronger ties are associated with more Negativity
  24. 24. Weaker Ties → lower Arousal and higher Dominance 15 -0.36*** 0.14***-0.31*** *p < 0.05, **p < 0.01, ***p < 0.001
  25. 25. Weaker Ties → more Education, more Income, fewer Hispanics 16 -0.12*** 0.35***-0.27*** *p < 0.05, **p < 0.01, ***p < 0.001
  26. 26. Higher Mobility → more Happiness 17 0.32*** *p < 0.05, **p < 0.01, ***p < 0.001
  27. 27. Higher Mobility → more Income, more Education, fewer Hispanics 18 0.08** -0.27***0.35*** *p < 0.05, **p < 0.01, ***p < 0.001
  28. 28. What we need? Methodology Results Conclusions
  29. 29. Conclusions Cognitive factors (emotions) and demographics of places affect the quality of online social interactions • Places with better educated, younger and higher- earning population are associated with weaker social ties and greater mobility (spatial diversity) – These Twitter users express happier, more positive emotions 19
  30. 30. Conclusions • Places with more Hispanic residents are associated with stronger social ties and lower mobility (spatial diversity) – People also express less positive, sadder emotions in these areas 20
  31. 31. Thank You! 21 @meghaarora42 marora@andrew.cmu.edu Questions?

×