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Machine learning approaches for understanding social interactions on Twitter
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- 1. Machine learning approaches for understanding social interactions on Twitter May 6, 2014 Alice Oh alice.oh@kaist.edu aoh@seas.harvard.edu http://uilab.kaist.ac.kr/members/aliceoh/
- 2. Our Research • Topic Modeling • ICML 2014: Hierarchical Dirichlet scaling process • IJCAI 2013: Context-dependent conceptualization • NIPS Big Learning Workshop 2012: Distributed online learning for latent Dirichlet allocation • CIKM 2012: Recursive Chinese restaurant processes for modeling topic hierarchies • ICML 2012: Dirichlet processes with mixed random measures • Social Media Analysis • ACL 2014 Workshop: Self-disclosure topic model • WWW 2014: Computational analysis of agenda setting theory • AAAI 2013: Hierarchical aspect-sentiment model • ICWSM 2012: Social aspects of emotions in Twitter conversations • ACL 2012: Self-disclosure and relationship strength in Twitter conversations • WSDM 2011: Aspect sentiment unification model for online review analysis 2
- 3. Contact Information • At Harvard until end of July, 2014 and open for • Collaborations: writing papers, sharing data, etc. • Discussions about topic modeling and computational social science • Going back to KAIST in August • http://uilab.kaist.ac.kr • alice.oh@kaist.edu • Can recommend students for intern, postdoc, and researcher positions • Please consider attending • ICWSM (program co-chair), Ann Arbor, MI • ACL Workshop on Social Dynamics and Personal Attributes (co- organizer), Baltimore, MD 3
- 4. What is topic modeling?
- 5. Blei, Communications of the ACM, 2012
- 6. Motivation
- 7. Motivation • What are the topics discussed in the article? • Is the article related to • household finances? • price of gasoline? • price of Apple stock? • How would you build an automatic system for answering these questions?
- 8. http://www.nytimes.com/2010/08/09/sports/autoracing/09nascar.html?hp nascar, races, track, raceway, race, cars, fuel, auto, racing economic, slowdown, sales, recession, costs, spending, save fans, spectators, sports, leagues, teams, competition 8
- 9. http://www.nytimes.com/2010/08/09/sports/autoracing/09nascar.html? nascar, races, track, raceway, race, cars, fuel, auto, racing economic, slowdown, sales, recession, costs, spending, save fans, spectators, sports, leagues, teams, competition Topics: multinomial over wordsTopic Distributions
- 11. Topics Discovered by LDA nascar 0.12 spending 0.09 sports 0.12 races 0.1 economic 0.07 team 0.11 cars 0.1 recession 0.06 game 0.1 racing 0.09 save 0.05 player 0.1 track 0.08 money 0.05 athlete 0.09 speed 0.06 cut 0.04 win 0.07 ... ... ... money 0.002 speed 0.003 nascar 0.001 Topics: multinomial over vocabulary 11
- 12. http://www.nytimes.com/2010/08/09/sports/autoracing/09nascar.html? nascar, races, track, raceway, race, cars, fuel, auto, racing economic, slowdown, sales, recession, costs, spending, save fans, spectators, sports, leagues, teams, competition Topics: multinomial over wordsTopic Distributions
- 13. Graphical Representation of LDA Topic Distributions nascar, races, track, raceway, race, cars, fuel, auto, racing economic, slowdown, sales, recession, costs, spending, save fans, spectators, sports, leagues, teams, competition Topics: multinomial over words Topics sales xxx slowdown recession cars races spending xxx save costs fuel 13
- 14. Do you feel what I feel? Social Aspects of Emotions in Twitter Conversations Suin Kim, JinYeong Bak, Alice Oh ICWSM 2012 14
- 15. Twitter conversation data • Twitter conversation data: approx 220k dyads who “reply” to each other, 1,670k conversational chains (We now have about 5x this amount) ! 1! 2! 3! 4!
- 17. Emotion cycles We propose that organizational dyads and groups inhabit emotion cycles: Emotions of an individual influence the emotions, thoughts and behaviors of others; others’ reactions can then influence their future interactions with the individual expressing the original emotion, as well as that individual’s future emotions and behaviors. People can mimic the emotions of others, thereby extending the social presence of a specific emotion, but can also respond to others’ emotions, extending the range of emotions present. 17
- 18. Topic model with a twist • Dirichlet forest prior (Andrzejewski et al.) • Mixture of Dirichlet tree distribution • Dirichlet tree: Generalization of Dirichlet distribution • Knowledge is expressed using Must-link and Cannot-link primitives • Must-link(love, sweetheart) • Cannot-link(exciting, bored) 18 q ⌘ DF-LDA
- 19. Domain knowledge in Dirichlet forest prior 19 Seed Words anticipation hope wait await inspir excit bore readi expect nervou calm motiv prepar certain anxiou optimist forese joy awesom amaz wonder excit glad fine beauti high lucki super perfect complet special bless safe proud anger shit bitch ass mean damn mad jealou piss annoi angri upset moron rage screw stuck irrit surprise amaz wow wonder weird lucki differ awkward confus holi strang shock odd embarrass overwhelm astound astonish fear scare stress horror nervou terror alarm behind panic fear afraid desper threaten tens terrifi fright anxiou sadness sorri bad aw sad wrong hurt blue dead lost crush weak depress wors low terribl lone disgust sick wrong evil fat ugli horribl gross terribl selfish miser pathet disgust worthless aw asham fuck acceptance okai ok same alright safe lazi relax peac content normal secur complet numb fulfil comfort defeat Must-link within a class Cannot-link between classes
- 20. Emotion Topics How do we express emotions? JoyAnticipation Anger Topic 114 omg love haha thank really Topic 107 love thank follow wow Topic 159 good day hope morning thank Topic 158 love thank miss hug Topic 125 hope better feel thank soon Topic 26 good thank hope miss Topic 146 come wait week day june Topic 146 good day time work Topic 131 lmao fuck ass bitch shit Topic 4 ass yo lmao nigga Topic 19 lmao shit damn fuck oh Topic 13 shit nigga smh yea Fear Topic 48 omg oh lmao shit scare Topic 78 happen heart attack hospital Topic 27 don’t come night sleep outside Topic 140 time got work day Surprise Topic 172 yeag know think true funny Topic 89 know don’t think look Topic 15 think don’t know make really Topic 94 haha dont think really 29 70 21 14 5 Sadness Disgust Topic 6 oh sorry haha know didnt Topic 59 hurt got good bad Topic 106 tweet reply didn’t read sorry Topic 155 oh really make feel Topic 116 oh fuck don’t ye ew Topic 116 look haha oh know Topic 22 don’t oh think yeah lmao Topic 174 don’t think say people Acceptance Topic 43 ok oh thank cool okay Topic 102 know try let ok Topic 199 xx thank good okay follow Topic 8 night love good sleep 17 7 18 Neutral Topic 180 com www http check youtube Topic 156 twitter facebook people account Topic 184 account google app work email Topic 67 food chicken cook rt 19 20
- 21. Emotion Topics How do we express emotions? JoyAnticipation Topic 114 omg love haha thank really Topic 107 love thank follow wow Topic 125 hope better feel thank soon Topic 26 good thank hope miss Sadness Topic 6 oh sorry haha know didnt Topic 59 hurt got good bad Neutral Topic 180 com www http check youtube Topic 156 twitter facebook people account GreetingCaring Sympathy IT/Tech 21
- 22. Emotion-tagged conversations 22 A (Love): @amithpr @dhempe @OperaIndia - Would you have any update on @mrunmaiy's health - hope she is recovering well? B (neut): @labnol @dhempe she is recovering but slow. The injury is on the spine therefore worrisome. Still in icu. A (Sadness): @amithpr thanks for the update.. extremely said to hear that news.. B (neut): @labnol #prayformrun She is a fighter and will come out of this B (neut): @AyeItsMeiMei just tell ur followers to report her for spam. then she'll be kicked off twitter A (Anger): @Jakeosaurous dude I didn't even do shit to her I'm just here tweeting & she calls me a ugly bitch? I was like oh wow thanks? B (neut): @AyeItsMeiMei yeah clearly shes so ugly she cant even use her real pic:P so dont feel bad A (Love): @Jakeosaurous haha. I don't care. She's getting spammed with hate. Hahaha. (": thanks though. B (neut): @AyeItsMeiMei np
- 23. Emotion Transitions Plutchik’s Wheel of Emotions Joy 39.7% 0.51 Acceptance 10.4% 0.23 Fear 2.6% 0.11 Surprise 7.4% 0.17 Anticipation 15.1% 0.26 Disgust 2.9% 0.11 Sadness 9.1% 0.19 0.31 Anger 12.8% 0.37 0.33 0.32 0.31 0.33 0.21 0.34 0.15 0.14 0.13 0.15 23
- 24. Defining “Influence” emotion influencing tweet User A User B Having a tough day today. RIP Harrison. I’ll miss you a ton :/ Just pray about it. God will help you. Not really religious, but thanks man. :) If you need talk you know I’m here. Time (Sadness) (Acceptance) (Anticipation) 24
- 25. Topic 117 tweet people don’t read post Topic 59 hurt got bad pain feel Emotion Influences What can you say to make your partner feel better? Joy → SadnessSadness → Joy Topic 18 wear look think love black Topic 24 love thank great new look Anticipation → Surprise Topic 96 music listen play song good Topic 178 follow tweet people twitter thank Acceptance → Anger Topic 31 i’m got lmax shit da Topic 13 lmao shit nigga smh yea Disgust → Joy Topic 61 watch new live tv tonight Topic 63 watch good think know look Suggesting Greeting Sympathy Swear words Complaining 25
- 26. 0 0.075 0.15 0.225 0.3 Anticipation Joy Surprise Fear Anger Sadness Disgust Acceptance Neutral 0.041 0.0710.082 0.053 0.265 0.061 0.081 0.0420.051 Emotion Influence: Sadness to Joy Emotion Influence: Joy to Anger 0 0.09 0.18 0.27 0.36 Anticipation Joy Surprise Fear Anger Sadness Disgust Acceptance Neutral 0.211 0.230.2140.209 0.191 0.2370.253 0.358 0.273 Expressing Anger has 26.5% of chance of changing the partner’s emotion from Joy to Anger. 26 Expressing Joy has 35.8% of chance of changing the partner’s emotion from Sadness to Joy.
- 27. Self-disclosure topic model JinYeong Bak, Chin-Yew Lin, and Alice Oh ACL 2014 Workshop on Social Dynamics and Personal Attributes 27
- 28. Self-disclosure Research using Twitter • People disclose personal and secretive information • to build and maintain interpersonal relationship • to get social support • Twitter is a great source for naturally-occurring, large- scale, longitudinal data on self-disclosure behavior • We develop a topic model for classifying self-disclosure behavior into three categories: G (general, no disclosure), M (medium disclosure), H (high disclosure) • We look at the correlation of self-disclosure behavior and frequency of Twitter conversations in longitudinal data 28
- 29. Self-disclosure in Twitter conversations 29 Conversa)on 2: I'm moving out. @xxxx ??? What's going on bb? @yyyy Mother. Done with her. I am planning to get out now. There's nothing I can do, we dont get along @xxxx I'm.sorry hunn. That's rough. Where are you going to go though? @yyyy Probably stay at a friends place in the Cmebeing unCl I find a place to live! @xxxx :/ well I'm glad your geHng out if she is being horrible to you Conversa)on 3: Oh, prepregnancy pants, you are so uncomfortable. @eeee You can put them on? Jealous. @ffff they are cuHng into my flesh and are giving me a ridiculous muffin top. It isn't preOy. But we have company coming over. @eeee Yea, I tried yesterday. I got one pair of shorts to buOon painfully and my jeans just laughed at me. Conversa)on 1: So my brother is going to Roskilde FesCval and my mother and sister is going to England.. That leaves me, my dad and my dog. @cccc why aren't you going to england? @dddd because my sister is going with 3 of her friends and my mom's just there... to be there. And my sister didn't want me to come :(
- 30. Data • Full data • 88k users, 51k dyads • 1.3M conversations • 10.5M tweets • Longitudinal data from August 2007 to July 2013 • Labeled data (gold standard for self-disclosure level) • 101 conversations • 673 tweets 30
- 31. Graphical Representation of SDTM 3 sets of topics, one for G, M, and H levels By using a topic model, we can ! -classify the levels of disclosure! -discover topics associated with each level! -generalize to other social media sites using the same set of seed words
- 32. Seed Words • Medium level: frequent trigrams for personally identifiable information ! ! ! ! • High level: automatically extracted from sixbillionsecrets Website 32
- 33. Classification Results 33 Direct Classification using the Models Classification with SVM using Features Learned from Models
- 35. SD level & conversation frequency 35
- 36. Sociolinguistic Analysis of Twitter in Multilingual Societies Suin Kim, Ingmar Weber, Li Wei, and Alice Oh Under Review 36
- 37. Data
- 38. Data
- 39. Visualization of the network
- 40. How are they connected? • English monolinguals and X-EN bilinguals bridge the network
- 41. Closer look at Bilinguals: Which language do they choose?
- 42. Closer look at Bilinguals: Hashtag usage
- 43. Closer look at Bilinguals: Topics (Results of LDA)
- 44. Closer look at Bilinguals: Topics (Results of LDA)
- 45. Future directions • Develop model for prediction of language choice in bilinguals • Look at how English is used throughout the world • Cognitive studies of first- and second- language • Self-disclosure and relationship building • Email me for data sharing, collaborating, discussing, … • alice.oh@kaist.edu