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Self-disclosure in twitter conversations - talk in QCRI

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Self-disclosure in twitter conversations - talk in QCRI

  1. 1. Self-disclosure in Twitter conversations JinYeong Bakjy.bak@kaist.ac.krDepartment of Computer Science, KAIST
  2. 2. About Me 2 2014-10-23  JinYeong Bak  Ph.D. student at KAIST, U&I Lab  Research interests Bayesian Data Analysis Computational Social Science
  3. 3. About Me 2 2014-10-23  JinYeong Bak  Ph.D. student at KAIST, U&I Lab  Research interests Bayesian Data Analysis Computational Social Science  Research Intern, MSRA, 2013, Supervisor: Chin-Yew Lin  Related publications Self-Disclosure and Relationship Strength in Twitter Conversations, ACL 2012 (with Suin Kim, Alice Oh) Self-disclosure topic model for classifying and analyzing Twitter conversations, EMNLP 2014 (with Chin-Yew Lin, Alice Oh)
  4. 4. 2014-10-23 Overview 2014-10-23
  5. 5. Self-disclosure 4 2014-10-23
  6. 6. Self-disclosure 4 2014-10-23
  7. 7. Self-disclosure 4 2014-10-23
  8. 8. Self-disclosure 4 2014-10-23
  9. 9. Limitations in Previous Works 5 2014-10-23  Survey  Hand coding  Lab environment
  10. 10. Limitations in Previous Works 5 2014-10-23  Survey  Hand coding  Lab environment Hard to identify self-disclosure in naturally occurring and large dataset
  11. 11. Twitter Conversations https://twitter.com/britneyspears Example ofa Twitter conversation 6 2014-10-23
  12. 12. Graphical model of Self-Disclosure Topic Model Self-Disclosure Topic Model (SDTM) 7 2014-10-23
  13. 13. Graphical model of Self-Disclosure Topic Model Self-Disclosure Topic Model (SDTM) 7 2014-10-23 Accuracy and average F1
  14. 14. Self-disclosure & Social features What are relations between self-disclosure and social features in Twitter conversations? 8
  15. 15. 2014-10-23 Self-disclosure (SD)
  16. 16.  The verbal expressions by which a person reveals aspects of self to others [Jourard1971b]  Process of making the self known to others [Jourard&Lasakow1958]  3~40% of everyday conversation is consist of self-disclosure [Dunbar et al.1997] Self-disclosure: Definition 10 2014-10-23
  17. 17. Self-disclosure: Level 11 2014-10-23 Self-disclosure level [Vondracek et al.1971, Barak et al.2007]  No disclosure (G level) General information and ideas  Medium disclosure (M level) General information about self or someone close to him  High disclosure (H level) Sensitive information about self or someone close to him
  18. 18. Self-disclosure: G level  General information and ideas  No information about self or someone close to him 12 2014-10-23
  19. 19. Self-disclosure: M level  General information about self or someone close to him  Personal events, age, occupation and family members 13 2014-10-23
  20. 20. Self-disclosure: H level  Sensitive information about self or someone close to him  Problematic behaviors of self and family members  Physical appearance, health, death, sexual topics 14 2014-10-23
  21. 21. Self-disclosure: Relations 15 2014-10-23 Human relationship  Degree of self-disclosure in a relationship depends on the strength of the relationship [Duck2007]  Strategic self-disclosure can strengthen the relationship
  22. 22. Self-disclosure: Relations 16 2014-10-23 Benefits  Can get social support from others [Derlega et al.1993]  Can cope with stress [Derlega et al.1993,Tamir and Mitchell2012]  Examples
  23. 23. Self-disclosure: Relations 17 2014-10-23 Consideration  Easy to be attacked when private information is opened  Need to manage privacy boundary (e.g. people, topics) [Petronio2002]  Example
  24. 24. Limitations in Previous Works 18 2014-10-23  Survey Asking questions to participants Cons) Biased by participants memory
  25. 25. Limitations in Previous Works 18 2014-10-23  Survey Asking questions to participants Cons) Biased by participants memory  Hand coding Analyzing dataset by human Cons) Cannot apply to large dataset
  26. 26. Limitations in Previous Works 18 2014-10-23  Survey Asking questions to participants Cons) Biased by participants memory  Hand coding Analyzing dataset by human Cons) Cannot apply to large dataset  Lab environment Experiments held in lab or artificial environment Cons) Not real/naturally occurring dataset
  27. 27. Research Questions 19 2014-10-23  How can we find self-disclosure in large & naturally occurring corpus automatically?
  28. 28. Research Questions 19 2014-10-23  How can we find self-disclosure in large & naturally occurring corpus automatically?  What are relations between self-disclosure and social features in large & naturally occurring corpus?
  29. 29. 2014-10-23 Twitter Conversations
  30. 30. Twitter 21  Online social networking service  www.twitter.com  200 million users send over 400 million tweets daily (2013.09) 2014-10-23 https://twitter.com/NoSyu
  31. 31. Tweet 22 2014-10-23  Users write 140-characters messages  Users mention others or re-tweet other’s tweet
  32. 32. Conversation in Twitter 23 2014-10-23  Users have a conversation in Twitter https://twitter.com/britneyspears
  33. 33. Conversation Topics 24 2014-10-23  Users discuss several topics with others Soccer Politics
  34. 34. Conversation Topics 25 2014-10-23  Users discuss several topics with others Places Family
  35. 35. Twitter Conversations  A Twitter conversation  5 or more tweets  At least one reply by each user https://twitter.com/britneyspears Example ofa Twitter conversation 26 2014-10-23
  36. 36. Twitter Conversations  A Twitter conversation  5 or more tweets  At least one reply by each user  Twitter conversation data  Aug 2007 to Jul 2013  102K users  2M conversations  17M tweets https://twitter.com/britneyspears Example ofa Twitter conversation 26 2014-10-23
  37. 37. 2014-10-23 Self-disclosure and relationship strength in Twitter conversations ACL 2012 short paper 2014-10-23
  38. 38. Self-disclosure: Relations 28 2014-10-23 Human relationship  Degree of self-disclosure in a relationship depends on the strength of the relationship [Duck2007]  Strategic self-disclosure can strengthen the relationship
  39. 39. Research Question 29 2014-10-23 Does Twitter conversations also show a similar pattern?  Dyads with high relationship strength show more self-disclosure behavior  Dyads with low relationship strength show less self-disclosure behavior
  40. 40. Methodology  Twitter data  131K users  2M conversations 30 2014-10-23
  41. 41. Methodology  Twitter data  131K users  2M conversations  Relationship strength  Conversation frequency (CF)  Conversation length (CL) 30 2014-10-23
  42. 42. Methodology  Twitter data  131K users  2M conversations  Relationship strength  Conversation frequency (CF)  Conversation length (CL)  Self-disclosure  Personal information  Profanity 30 2014-10-23
  43. 43. Methodology  Twitter data  131K users  2M conversations  Relationship strength  Conversation frequency (CF)  Conversation length (CL)  Self-disclosure  Personal information  Profanity  Analysis with topic models  Latent Dirichlet allocation (LDA, [Blei, JMLR 2003]) 30 2014-10-23
  44. 44. Relationship Strength  CF: conversation frequency  The numberof conversational chains between the dyad averaged per month  CL: conversation length  The lengthof conversational chains between the dyad averaged per month 31 2014-10-23
  45. 45. Relationship Strength  CF: conversation frequency  The numberof conversational chains between the dyad averaged per month  CL: conversation length  The lengthof conversational chains between the dyad averaged per month  Relationship strength  A high CF or CL for a dyad means the relationship is strong  A low CF or CL for a dyad means the relationship is weak 31 2014-10-23
  46. 46. Self-disclosure  Personal information  Personally Identifiable Information (PII)  Personally Embarrassing Information (PEI)  Profanity  nigga, ass, wtf, lmao 32 2014-10-23
  47. 47. Self-disclosure: Personal Information Personally Identifiable Information (PII) Personally Embarrassing Information (PEI) 33 2014-10-23 Ex) name, location, email address, job, social security number Ex) clinical history, sexual life, job loss, family problem
  48. 48. Self-disclosure: Personal Information  Discover topics in each conversation  Use LDA[Blei2003]with 푘푘=300  LDA outputs a topic proportion for each conversation  LDA outputs a multinomial word distribution for each topic 34 2014-10-23
  49. 49. Self-disclosure: Personal Information  Discover topics in each conversation  Use LDA[Blei2003]with 푘푘=300  LDA outputs a topic proportion for each conversation  LDA outputs a multinomial word distribution for each topic  Find related topics  Annotate conversations that best represent each topic  Use Amazon Mechanical Turk  Turkers annotated conversations for  Existence of PII  Existence of PEI  Keywords 34 2014-10-23
  50. 50. Self-disclosure: Personal Information Example of PII, PEI and Profanity topics  Shown by high probability words in each topic PII 1 PII 2 PEI1 PEI 2 PEI 3 Profanity san tonight pants teeth family nigga live time wear doctor brother lmao state tomorrow boobs dr sister shit texas good naked dentist uncle ass south ill wearing tooth cousin bitch 35 2014-10-23
  51. 51. Results 36 2014-10-23 weak  strong weak  strong Profanity PII & PEI Conversation Frequency Conversation Length Profanity PII & PEI
  52. 52. Results 37 2014-10-23 weak  strong weak  strong profanity PII & PEI Conversation Frequency Conversation Length profanity PII & PEI
  53. 53. Results: Interpretation  PII  When they meet new acquaintances, they use PII to introduce themselves 38 2014-10-23
  54. 54. Summary  Used a large corpus of Twitter conversations  Measured relationship strength by conversation frequency and conversation length  Measured self-disclosure by  PII, PEI  Profanity  Confirmed hypothesis that stronger relationships show more self-disclosure behaviors in Twitter conversations 39 2014-10-23
  55. 55. Weakness of the Paper 40 2014-10-23  Use naïve definition of degree of self-disclosure PII, PEI, Profanity Need to use more concrete definition for self-disclosure degree
  56. 56. Weakness of the Paper 40 2014-10-23  Use naïve definition of degree of self-disclosure PII, PEI, Profanity Need to use more concrete definition for self-disclosure degree Self-disclosure level
  57. 57. Weakness of the Paper 40 2014-10-23  Use naïve definition of degree of self-disclosure PII, PEI, Profanity Need to use more concrete definition for self-disclosure degree  Use naïve computational method LDA with post-processing Need to build more concrete novel method Self-disclosure level
  58. 58. Weakness of the Paper 40 2014-10-23  Use naïve definition of degree of self-disclosure PII, PEI, Profanity Need to use more concrete definition for self-disclosure degree  Use naïve computational method LDA with post-processing Need to build more concrete novel method Self-disclosure level Self-disclosure Topic Model
  59. 59. 2014-10-23 Self-disclosure Topic Model (SDTM) EMNLP 2014 long paper 2014-10-23
  60. 60. Difficulties for SD research  Lack of ground-truth dataset of SD level  No tagged dataset for Twitter conversation  No accessible self-disclosure datasets 42 2014-10-23
  61. 61. Difficulties for SD research  Lack of ground-truth dataset of SD level  No tagged dataset for Twitter conversation  No accessible self-disclosure datasets  Lack of study about SD in computational linguistics  Definitions and relations with others in social psychology  Survey or hand-coding  Related word categories in LIWC [Houghton et al.2012] 42 2014-10-23
  62. 62. Ground-truth Dataset  Process  Sample random 301 Twitter conversations  Ask it to three judges  Tag self-disclosure level to each tweet  Work on a web-based platform 43 Screenshot of annotation web-based platform 2014-10-23
  63. 63. Ground-truth Dataset  Process  Sample random 301 Twitter conversations  Ask it to three judges  Tag self-disclosure level to each tweet  Work on a web-based platform  Result  Tagged G: 122, M: 147, H: 32 conversations  Fleiss kappa: 0.68 43 Screenshot of annotation web-based platform 2014-10-23
  64. 64. Assumptions: First person pronouns First person pronouns are good indicators for self-disclosure  Ex) ‘I’, ‘My’  Used in previous research [Joinsonet al.2001, Barak et al.2007] 44 2014-10-23
  65. 65. Assumptions: First person pronouns First person pronouns are good indicators for self-disclosure  Ex) ‘I’, ‘My’  Observed highly discriminative features between G and M/H in annotated dataset 45 Unigram Bigram Trigram my I love I have a I I was is going to I’m I have to go to but my dad wantto go was go to and I was I’ve my mom going to miss 2014-10-23
  66. 66. Assumptions: Topics M and H level have different topics  [General vsSensitive] information about self or intimate 46 2014-10-23
  67. 67. Assumptions: Topics Self-disclosure related topics by LDA Location Time Adult Health Family Profanity san tonight pants teeth family nigga live time wear doctor brother lmao state tomorrow boobs dr sister shit texas good naked dentist uncle ass south ill wearing tooth cousin bitch 47 2014-10-23
  68. 68. Assumptions: Topics M and H level have different topics  [General vsSensitive] information about self or intimate  Can be formalized as topics  Personally Identifiable Information  General information about self  Ex) name, location, email address, job, …  Secrets  Sensitive information about self  Ex) physical appearance, health, sexuality, death, … 48 2014-10-23
  69. 69. Graphical model of Self-Disclosure Topic Model Self-Disclosure Topic Model (SDTM)  Based on probabilistic topic modeling 49 2014-10-23
  70. 70. Graphical model of Self-Disclosure Topic Model Self-Disclosure Topic Model (SDTM)  Based on probabilistic topic modeling  Classifying G and M/H level  Observed first-person pronouns  Using learned maximum entropy classifier 49 2014-10-23
  71. 71. Graphical model of Self-Disclosure Topic Model Self-Disclosure Topic Model (SDTM)  Based on probabilistic topic modeling  Classifying G and M/H level  Observed first-person pronouns  Using learned maximum entropy classifier  Classifying M and H level  Observed words  Using seed words for each level 49 2014-10-23
  72. 72. Self-Disclosure Topic Model (SDTM) 50 2014-10-23 Rough description of how to infer self-disclosure in SDTM Maximum Entropy Classifier Topic Model G level M level H level Topic Model with Seed Words Tweet
  73. 73. Self-Disclosure Topic Model (SDTM) 50 2014-10-23 Rough description of how to infer self-disclosure in SDTM Maximum Entropy Classifier Topic Model G level M level H level Topic Model with Seed Words Tweet
  74. 74. Self-Disclosure Topic Model (SDTM) 50 2014-10-23 Rough description of how to infer self-disclosure in SDTM Maximum Entropy Classifier Topic Model G level M level H level Topic Model with Seed Words Tweet
  75. 75. Self-Disclosure Topic Model (SDTM) 50 2014-10-23 Rough description of how to infer self-disclosure in SDTM Maximum Entropy Classifier Topic Model G level M level H level Topic Model with Seed Words Tweet
  76. 76. Self-Disclosure Topic Model (SDTM) 50 2014-10-23 Rough description of how to infer self-disclosure in SDTM Maximum Entropy Classifier Topic Model G level M level H level Topic Model with Seed Words Tweet
  77. 77. Self-Disclosure Topic Model (SDTM) 50 2014-10-23 Rough description of how to infer self-disclosure in SDTM Maximum Entropy Classifier Topic Model G level M level H level Topic Model with Seed Words Tweet
  78. 78. Maximum Entropy Classifier 51 2014-10-23  Learned from annotated dataset  Works better than others (C4.5, Naïve Bayes, SVM with linear kernel, polynomial kernel and radial basis)  Used to identify aspect and opinions in topic model [Zhao2010]
  79. 79. Seed Words Seed words are prior knowledge for each level  G level  No seed words (symmetric prior)  M level  Data-driven approach in Twitter conversation  H level  Data-driven approach from external dataset 52 2014-10-23
  80. 80. Seed Words  M level  Data-driven approach  Use Twitter conversation dataset  Get frequently occurred trigram that begin with ‘I’ and ‘my’ 53 2014-10-23
  81. 81. Seed Words  M level  Data-driven approach  Use Twitter conversation dataset  Get frequently occurred trigram that begin with ‘I’ and ‘my’  Example seed words 53 Name Birthday Location Occupation My nameis My birthday is Ilive in My jobis My last name Mybirthday party Ilived in My new job My realname My bdayis I live on My high school 2014-10-23
  82. 82. Seed Words  H level  Data-driven approach  Use external dataset (Six Billion Secrets) http://www.sixbillionsecrets.com  Users write and share his/her secrets  26,523 posts  Extract high ranked word features 54 2014-10-23 Example of secret posts in Six Billion Secrets
  83. 83. Seed Words  H level  Data-driven approach  Use external dataset (Six Billion Secrets) http://www.sixbillionsecrets.com  Users write and share his/her secrets  26,523 posts  Extract high ranked word features  Example seed words 54 Physical appearance Health condition Death chubby addicted dead fat surgery died scar syndrome suicide acne disorder funeral 2014-10-23 Example of secret posts in Six Billion Secrets
  84. 84. Classifying Performance  Data  Annotated Twitter conversation  Random shuffled 80/20 train/test 55 2014-10-23
  85. 85. Classifying Performance  Data  Annotated Twitter conversation  Random shuffled 80/20 train/test  Methods  BOW+ Bag of Words + Bigrams + Trigrams features, Maximum entropy  FirstP Occurrence of first-person pronouns features, Maximum entropy  SEED Seed words and trigrams features, Maximum entropy  FirstP+SEED FirstP and SEED feature, Two stage Maximum entropy  SDTM Self-disclosure Topic Model 55 2014-10-23
  86. 86. Classifying Performance 56 2014-10-23
  87. 87. Classifying Performance 56 2014-10-23
  88. 88. Classifying Performance 57 2014-10-23
  89. 89. Classifying Performance 57 2014-10-23
  90. 90. Classifying Performance 57 2014-10-23
  91. 91. Classifying Performance 57 2014-10-23
  92. 92. 2014-10-23 Self-disclosure & Social features EMNLP 2014 long paper
  93. 93. Self-disclosure & Social features What are relations between self-disclosure and social features in Twitter conversations?  Research questions 1.Does high self-disclosure lead to longer conversations? 2.Is there difference in conversation length patterns over time depending on overall self-disclosure level? 3.Does high self-disclosure users have many conversation partners? 4.Does high self-disclosure users have more conversations frequently? 59
  94. 94. Research Questions Q1) Does high self-disclosure lead to longer conversations? 60 2014-10-23
  95. 95. Research Questions Q2) Is there difference in conversation length patterns over time depending on overall self-disclosure level? 61 2014-10-23 High SD level dyad Low SD level dyad
  96. 96. Research Questions 62 2014-10-23 Q3) Does high self-disclosure users have many conversation partners? High SD level user Low SD level user
  97. 97. Research Questions 63 2014-10-23 Q4) Does high self-disclosure users have more conversations frequently? High SD level user Low SD level user
  98. 98. Results High ranked topics in each level (G, M, H levels) Shown by high probability words in each topic G 1 G 2 M 1 M 2 H 1 H 2 obama league send going better ass he’s win email party sick bitch romney game i’ll weekend feel fuck vote season sent day throat yo right team dm night cold shit president cup address dinner hope fucking 64 2014-10-23
  99. 99. Results Q1) Does high self-disclosure lead to longer conversations? Ans) Positive relations between initial SD level and changes CL 65 2014-10-23
  100. 100. Results Q2) Is there difference in CL patterns over time by overall SD level? Ans) ‘high’ and ‘mid’ groups increase CL over time, not ‘low’ ‘high’ groups talk more in a conversation than ‘mid’ & ‘low’ groups 66 2014-10-23
  101. 101. Results 67 2014-10-23 Q3) Does high self-disclosure users have many conversation partners? Ans) ‘mid’ self-disclosure users have more conversation partners than others #Partners # Conv/ Day Words / Conv ConvLength low 3.33 0.46 59.17 4.13 mid 3.55 0.52 61.17 4.28 high 3.47 0.54 63.26 4.45 p-value <0.001 <0.001 <0.1 <0.001
  102. 102. Results 68 2014-10-23 Q4) Does high self-disclosure users have more conversations frequently? Ans) ‘high’ self-disclosure users have more conversations per day than others #Partners # Conv/ Day Words / Conv ConvLength low 3.33 0.46 59.17 4.13 mid 3.55 0.52 61.17 4.28 high 3.47 0.54 63.26 4.45 p-value <0.001 <0.001 <0.1 <0.001
  103. 103. Results 69 2014-10-23 Finding)  Researchers often look at the number of words in a conversation for relation with self-disclosure  Conversation length is more significant than # words #Partners # Conv/ Day Words / Conv ConvLength low 3.33 0.46 59.17 4.13 mid 3.55 0.52 61.17 4.28 high 3.47 0.54 63.26 4.45 p-value <0.001 <0.001 <0.1 <0.001
  104. 104. Summary 70 2014-10-23  Self-disclosure (SD) Definition from social psychology Limitations inprevious research  Computational approaches for self-disclosure Twitter conversation dataset Self-disclosure topic model (SDTM)  Self-disclosure & Social features Relationship strength over time Conversation partners and frequency
  105. 105. Future Work 71 2014-10-23  Self-disclosure for a user timeline tweets Have positive relations with Loneliness [Al-Saggaf.2014] Online social network usage[Trepte.2013] Predict user’s Loneliness and give a social support Usage patterns in online social network and give feedback  Self-disclosure by machine Looks like human in dialogue system Can increase satisfaction in talking cure dialogue system
  106. 106. Reference 72 2014-10-23  [Jourard1971b] Sidney M Jourard. 1971b. The transparent self (rev. ed.). Princeton, NJ: VanNostrand.  [Jourard1958] Sidney M Jourard and Paul Lasakow. 1958. Some factors in self-disclosure. The Journal of Abnormal and Social Psychology, 56(1):91.  [Dunbar et al.1997] Robin IM Dunbar, Anna Marriott, and Neil DC Duncan. 1997. Human conversational behavior. Human Nature, 8(3):231–246.  [Vondracek and Vondracek1971] Sarah I Vondracek and Fred W Vondracek. 1971. The manipulation and measurement of self-disclosure in preadolescents. Merrill- Palmer Quarterly of Behavior and Development, 17(1):51–58.  [Chelune and others1979] Gordon J Chelune et al. 1979. Self-disclosure: Origins, patterns, and implications of openness in interpersonal relationships. Jossey-Bass San Francisco.  [Barak&Gluck-Ofri2007] Azy Barak and Orit Gluck-Ofri. 2007. Degree and reciprocity of self-disclosure in online forums. CyberPsychology & Behavior, 10(3):407–417.  [Jo2011] Jo, Yohan, and Alice H. Oh. "Aspect and sentiment unification model for online review analysis." Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011.
  107. 107. Reference 73 2014-10-23  [Tamir and Mitchell2012] Diana I Tamir and Jason P Mitchell. 2012. Disclosing information about the self is intrinsically rewarding. roceedings of the National Academy of Sciences, 109(21):8038–8043.  [Duck2007] Steve Duck. 2007. Human relationships. Sage.  [Bak et al.2012] JinYeong Bak, Suin Kim, and Alice Oh. 2012. Self-disclosure and relationship strength in twitter conversations. In Proceedings of the 50thAnnual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, pages 60–64. Association for Computational Linguistics.  [Derlega et al.1993] Valerian J. Derlega, Sandra Metts, Sandra Petronio, and Stephen T. Margulis. 1993. Self-Disclosure, volume 5 of SAGE Series on Close Relationships. SAGE Publications, Inc.  [Wills1985] Thomas Ashby Wills. 1985. Supportive functions of interpersonal relationships.  [Trepte.2013] Sabine Trepte and Leonard Reinecke. 2013. The reciprocal effects of social network site use and the disposition for selfdisclosure: A longitudinal study. Computers in Human Behavior, 29(3):1102 – 1112.  [Harris, J, 2009] Kamvar, Sep, and Jonathan Harris. We feel fine: An almanac of human emotion. Simon and Schuster, 2009.
  108. 108. Reference 74 2014-10-23  [Houghton and Joinson2012] David J Houghton and Adam N Joinson. 2012. Linguistic markers of secrets and sensitive self-disclosure in twitter. In System Science (HICSS), 2012 45th Hawaii International Conference on, pages 3480–3489. IEEE.  [Steinfield et al.2008] Charles Steinfield, Nicole B Ellison, and Cliff Lampe. 2008. Social capital, selfesteem, and use of online social network sites: A longitudinal analysis. Journal of Applied Developmental Psychology, 29(6):434–445.  [Petronio2002] Petronio, S. 2002. Boundaries of privacy: Dialectics of disclosure. 29. Albany, NY  [Valkenburg2011] Valkenburg, Patti M and Sumter. 2011. Sindy R and Peter, Jochen, Gender differences in online and offline self-disclosure in pre-adolescence and adolescence. British journal of developmental psychology  [Sprecher2012] Susan Sprecher, Stanislav Treger and Joshua D. Wondra. 2012. Effects of self-disclosure role on liking, closeness, and other impressions in get-acquainted interactions. Journal of Social and Personal Relationships.  [Zhao2010] Wayne Xin Zhao, Jing Jiang, HongfeiYan, and Xiaoming Li. 2010. Jointly modeling aspects and opinions with a maxent-lda hybrid. In Proceedings of EMNLP.
  109. 109. Thank you! Any questions or comments? JinYeongBakjy.bak@kaist.ac.krDepartment of Computer Science, KAIST 75 2014-10-23

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