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ICWSM 2016 paper presentation, Megha Arora

Megha Arora presented the paper at ICWSM 2016

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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
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
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
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
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 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
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
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
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 | Education | Ethnicity
hsp
7
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
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 | Dominance
10
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
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
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
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
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 which users who tweeted from tract i also
tweeted from, and
pij : proportion of tweets posted by these users from tract j
13
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 Negativity
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 < 0.001
What we need?
Methodology
Results
Conclusions
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
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
Thank You!
21
@meghaarora42
marora@andrew.cmu.edu
Questions?

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ICWSM 2016 paper presentation, Megha Arora

  • 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. 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
  • 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. 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
  • 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. 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. 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
  • 11. Data Collection • 6M geo-tagged tweets posted from around Los Angeles County • 340K users • 2.6M mentions 6
  • 12. Demographics Data Obtained socioeconomic characteristics for all Los Angeles County tracts from the 2012 US Census. Income | Education | Ethnicity hsp 7
  • 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. Emotion Analysis -5 -1 +1 +5 Analyze text of tweets to measure emotions SentiStrength to measure Negativity | Positivity 9
  • 15. Emotion Analysis Analyze text of tweets to measure emotions WKB (Warriner et al.) lexicon to measure Valence | Arousal | Dominance 10
  • 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. 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. 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. 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. Social Interactions Analysis Tie Strength tract1 tract2 Si = 7.33 (Strong Ties) Si = 1.08 (Weak Ties) 12
  • 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
  • 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. Weaker Ties → lower Arousal and higher Dominance 15 -0.36*** 0.14***-0.31*** *p < 0.05, **p < 0.01, ***p < 0.001
  • 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. Higher Mobility → more Happiness 17 0.32*** *p < 0.05, **p < 0.01, ***p < 0.001
  • 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
  • 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. 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