CSN11-Johan Bollen- Indiana University of Informatics and Computing

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CSN11-Johan Bollen- Indiana University of Informatics and Computing

  1. 1. Introduction Methods Results Ongoing research: Mood contagion Conclusions Twitter sentiment voorspelt aandelenkoersen Huina Mao (IU) and Johan Bollen (IU) Acknowledgement: Bruno Goncalves, Alex Vespignani, Eliot Smith, Peter Todd, and Ishani Banerji jbollen@indiana.edu School of Informatics and Computing Center for Complex Networks and Systems Research Indiana University February 9, 2011Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  2. 2. Introduction Methods Results Ongoing research: Mood contagion ConclusionsObjective Public mood states and the markets Do societies experience varying mood states like individuals? If so, can we assess such mood states from online materials and determine its socio-economic correlates?Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  3. 3. Introduction Methods Results Ongoing research: Mood contagion ConclusionsOutline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  4. 4. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Outline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  5. 5. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Crowds and mobs: reasons to be pessimistic H. L. Mencken “Democracy is the art of running the circus from the monkey cage.” Assumption: large groups = random = bad decisionsHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  6. 6. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Condorcet jury theorem: large groups can make betterdecisions 1785, jury votes, by majority rule, on binary decision: Definition (Condorcet theorem) PN = N N! i i=m (N−i)!i! p (1 − p) N−i 1 where N = the number of jurors p = the probability of an individual juror being right m = the number of jurors required for a majority 1 First and last equation.Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  7. 7. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Condorcet theorem: bigger crowds do better! Conclusion: large groups make better decisions! BUT: Same individual probability {right, wrong } Optimization problems Independent judgements: often not true!Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  8. 8. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:From collective intelligence to crowd sourcing Emergence Complex systems and behavior can emerge from multiplicity of interactions between simple units Large groups of networked individuals can exhibit collective intelligence: ant hills, fish schooling, bees, routing, humans... Open Source software Mechanical Turk Folksonomies Wikipedia and... web optimization (Bollen, 1996)Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  9. 9. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Not even trying? The Naughts: Rise of social networking and micro-blogging Stigmergy: medium coordinates collective intelligence Facebook: +500M users Twitter: +150M users BUT! Not trying to solve particular problems. Objective: recreation, socializing, sharingHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  10. 10. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Twitter! tweets and updates users broadcast brief text updates to the public or to a limited group of contacts: 140 characters or less Twitter, Facebook, Myspace Examples “Our Rights from Creator (h/t @JLocke). Life, Liberty, PoH FTW! Your transgressions = FAIL. GTFO, @GeorgeIII. -HANCOCK et al.” “at work feeling lousy”Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  11. 11. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Analyzing the chatter Predicting the present from side-effects Mapping online traffic to real-world outcomes +70M tweets per day +20GB of text per day the EEG of the global brain Box office receipts from Twitter chatter: Asur (2010) Google trends: flu (verbal autopsies) Predicting consumer behavior from search query volume (Goel, 2010) Contagion of “Loneliness” and happiness in social networks (Cacioppo, 2010 - Bollen, 2011)Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  12. 12. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Link between sentiment, mood and behavior Behavior is shaped not just by rational, conscious considerations Emotion plays a significant role in human decision-making (behavioral economics, behavioral finance, social psychology). Emotion plays a tremendously important role online and consequently in the “real” world, cf. Tunesia, Egypt. Extract indicators of individual and collective sentiment from online media feeds? Predict not just the present, but the future?Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  13. 13. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Extracting sentiment indicators from text Happy tweets. So...nothing quite feels like a good shower, shave and haircut...love it My beautiful friend. i love you sweet smile and your amazing soul i am very happy. People in Chicago loved my conference. Love you, my sweet friends @anonymous thanks for your follow I am following you back, great group amazing people Unhappy tweets. She doesn’t deserve the tears but i cry them anyway I’m sick and my body decides to attack my face and make me break out!! WTF :( I think my headphones are electrocuting me. My mom almost killed me this morning. I don’t know how much longer i can be here. Different Approaches: Natural Language processing (n-grams) for reviews (Nasukawa, 2003), topics (Yi, 2003), Support Vector Machines: text classification (positive vs. negative) using pre-classified learning sets: Gamon (2004), Pang (2008), Blogs, web sites: mixed approaches. Mishne (2006), Balog (2006), Gruhl (2005),...Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  14. 14. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:Sentiment and mood analysis is difficult for tweets Individual tweets Length: 140 characters, lack of text content Diversity:no standardized training sets, dimensions of mood? Lack of topic specificity Public mood from tweet collections and other microblog contents? We Feel Fine http://www.wefeelfine.org/ Moodviews http://moodviews.com Myspace: Thelwall (2009), FB: United States Gross National Happiness http://apps.facebook.com/usa_gnh/, Michael Jackson (Kim, 2009)Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  15. 15. Introduction Methods Results Ongoing research: Mood contagion Conclusions Collective Intelligence: from mobs to crowds Sentiment analysis:What we did: Trends in general public mood from a large-scale collection of tweets Each tweet= patient taking psychometric instrument (POMS) Large-scale collection of tweets: 10M, 2006-2008 Daily public mood assessment: Time series depicting fluctuations of public mood Correlations to socio-economic indicators? Contagion of mood in social network, cf. Haiti disaster?Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  16. 16. Introduction Methods Results Ongoing research: Mood contagion Conclusions Data Sentiment tracking instrumentOutline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  17. 17. Introduction Methods Results Ongoing research: Mood contagion Conclusions Data Sentiment tracking instrumentData sets Collection of tweets: April 29, 2006 to December 20, 2008 2.7M users Subset: August 1, 2008 to December 2008 - 9,664,952 tweets 1e+05 2e+04 log(n tweets) 5e+03 1e+03 2e+02 Aug 1 Sep 1 Oct 1 Nov 1 Dec 1 Dec 20 2008Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  18. 18. Introduction Methods Results Ongoing research: Mood contagion Conclusions Data Sentiment tracking instrument Each tweet: ID date-time type text 1 2008- web Getting ready for Black Friday. Sleep- 11-28 ing out at Circuit City or Walmart not 02:35:48 sure which. So cold out. 2 2008- web @dane I didn’t know I had an un- 11-28 cle named Bob :-P I am going to be 02:35:48 checking out the new Flip sometime soon ···Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  19. 19. Introduction Methods Results Ongoing research: Mood contagion Conclusions Data Sentiment tracking instrumentCollective mood: Profile of Mood States (POMS-bi) Definition 72 term questionnaire (McNair, 1971) to assess individual mood states. Each mood term maps into one of 6 mood dimensions: composed/anxious composed (+1), tense (-1), untroubled (+1), ... clearheaded/confused confused (-1), clearheaded (+1), mixed-up (-1), ... confident/unsure weak (-1), strong (+1), timid (-1), ... energetic/tired vigorous (+1), fatigued (-1), energetic (+1), ... agreeable/hostile sympathetic (+1), bad tempered (-1), agreeable (+1), ... elated/depressed mad (-1), good-natured (+1), annoyed (-1), ... About 6 minutes to complete by individual. Designed for repeated within-subjects testing.Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  20. 20. Introduction Methods Results Ongoing research: Mood contagion Conclusions Data Sentiment tracking instrument Tweet: I am so not bored. way too busy! I feel really great!   composed/anxious 0.01725 clearheaded/confused  0.05125    confident/unsure 0.725625   energetic/tired 0.666625   agreeable/hostile  0.361  elated/depressed 0.53175Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  21. 21. Introduction Methods Results Ongoing research: Mood contagion Conclusions Data Sentiment tracking instrumentAggregating daily tweets into a mood time series Mood indicators (daily) Calm text analysis Happy Twitter ~ feed Confident ...Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  22. 22. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesOutline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  23. 23. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesRatio of emotional tweets, over time. ratio of # tweets with mood expressions over all tweets 9 8 7 % mood expressions 6 0.0 1.0 5 0.8 residual (%) probability 0.4 4 −1.5 0.0 3 Aug 08 Oct 08 Dec 08 −1.5 −0.5 0.5 residual (%) 2 Aug 08 Sep 08 Oct 08 Nov 08 Dec 08 Ratio of tweets containing mood expressions vs. all tweets on a given day, including residuals from trendline.Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  24. 24. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesPublic mood trends: overview +2sd elated/depressed −2sd +2sd agreeable/hostile −2sd +2sd energetic/tired −2sd +2sd confident/unsure −2sd +2sd clearheaded/confused −2sd +2sd composed/anxious −2sd Election08 Thanksgiving08 08/01 09/01 10/01 11/01 12/01 12/20Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  25. 25. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesCase study 1: November 4th, 2008 - the presidentialelection +2sd elated/depressed −2sd +2sd agreeable/hostile −2sd +2sd energetic/tired −2sd +2sd confident/unsure −2sd +2sd clearheaded/confused −2sd +2sd composed/anxious −2sd Election08 10/20 11/04 11/19Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  26. 26. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesTFIDF scoring of tweet terms 2008 U.S. Presidential Election Nov 03 Nov 04 Nov 05 robocal poll histori business plumber won voter result barack cleanser absente prop grandmoth ballot speech russert turnout result socialist barack president-elect halloween citizen hologram acknowledg joe victori race thoughtfulli ecstat Table: Top 10 TF-IDF ranking terms 1 day before, on and 1 day after election day.Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  27. 27. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesCase study 2: November 27th, 2008 - Thanksgiving +2sd elated/depressed −2sd +2sd agreeable/hostile −2sd +2sd energetic/tired −2sd +2sd confident/unsure −2sd +2sd clearheaded/confused −2sd +2sd composed/anxious −2sd Thanksgiving08 11/12 11/27 12/12 Figure: Sparklines for public mood before, during and after ThanksgivingHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  28. 28. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesLong-term changes in public mood: statistical significance Mood dimension Period 1 Period 2 p-value Agreeable/Hostile 08/01-20 12/01-20 0.0001338 Mean 1= Mean 2= Difference -0.007sd 1.286sd 1.292sd Confident/Unsure 08/01-20 12/01-20 0.002381 Mean 1= Mean 2= Difference -0.120sd 0.785sd 0.905sd Composed/Anxious 08/01-20 12/01-20 0.0272 Mean 1= Mean 2= Difference 0.162 0.897 0.736 Table: T-tests to compare mood levels in two 20-day periods (August 1-20 and December 1-20, 2008) show statistically significant elevated z-scores for Agreeable, Confident and Composed mood.Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  29. 29. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesOutline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  30. 30. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesComparison to existing sentiment tracking tools:OpinionFinder OpinionFinder day after1.75 election Thanksgiving1.25 CALM1!1 pre!election anxiety ALERT http://www.cs.pitt.edu/mpqa/1 Theresa Wilson, Janyce Wiebe, and Paul!1 SURE election results Hoffmann (2005). Recognizing Contextual1 Polarity in Phrase-Level Sentiment!1 VITAL pre!election energy Analysis. Proc. of HLT-EMNLP-2005.1!1 KIND1!1 HAPPY Thanksgiving happiness1!1 Oct 22 Oct 29 Nov 05 Nov 12 Nov 19 Nov 26Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  31. 31. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studies Table: Multiple Regression Results for OpinionFinder vs. GPOMS dimensions. Parameters Coeff. Std.Err. t p Calm (X1 ) 1.731 1.348 1.284 0.20460 Alert (X2 ) 0.199 2.319 0.086 0.932 Sure (X3 ) 3.897 0.613 6.356 4.25e-08 Vital (X4 ) 1.763 0.595 2.965 0.004 Kind (X5 ) 1.687 1.377 1.226 0.226 Happy (X6 ) 2.770 0.578 4.790 1.30e-05 Summary Residual Std.Err Adj.R2 F6,55 p 0.078 0.683 22.93 2.382e-13Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  32. 32. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesComparison to DJIA DJIA daily closing value (March 2008−December 2008 13000 12000 11000 10000 9000 8000 Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2008Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  33. 33. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesComparison to DJIA 1 -n (lag) text Mood indicators (daily) analysis (1) OpinionFinder Granger F-statistic causality p-value 2 Twitter feed ~ (2) G-POMS (6 dim.) predicted (3) DJIA value MAPE t-1 SOFNN 3 DJIA ~ Direction % t-2 t-3 normalization Stock market (daily) t=0 value Figure: Methodological diagram outlining use of Granger causality analysis and Self-Organizing Fuzzy Neural Network to predict daily DJIA values from (1) past DJIA values at t − 1, t − 2, t − 3, and various permutations of Twitter mood values (OpinionFinder and GPOMS).Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  34. 34. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesbivariate-causal analysis: DJIA vs. public mood Table: Calm (X1 ), Alert (X2 ),Sure (X3 ), Vital (X4 ), Kind (X5 ), Happy (X6 ) lag XOF X1 X2 X3 X4 X5 X6 1 0.703 0.080 0.521 0.422 0.679 0.712 0.300 2 0.633 0.004 0.777 0.828 0.996 0.935 0.697 3 0.928 0.009 0.920 0.563 0.897 0.995 0.652 4 0.657 0.03 0.54 0.61 0.87 0.78 0.68 5 0.235 0.053 0.753 0.703 0.246 0.837 0.05Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  35. 35. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studiesCalm vs. DJIA bank 2 2 DJIA z-score bail-out Calm z-score 1 1 0 0 -1 -1 -2 -2 2 1 DJIA z-score 0 -1 -2 2 Calm z-score 1 0 -1 -2 A ug 09 A ug 29 Sep 18 Oct 08 Oct 28Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  36. 36. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studies Table: DJIA Daily Prediction Using SOFNN Evaluation IOF I0 I1 I1,2 I1,3 I1,4 I1,5 I1,6 MAPE (%) 1.95 1.94 1.83 2.03 2.13 2.05 1.85 1.79 Direction (%) 73.3 73.3 86.7 60.0 46.7 60.0 73.3 80.0Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  37. 37. Introduction Methods Results Ongoing research: Mood contagion ConclusionsCross-validation Case-studies Citation: Johan Bollen, Huina Mao, and Xiao-Jun Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2010, http://arxiv.org/abs/1010.3003.Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  38. 38. Introduction Methods Results Ongoing research: Mood contagion ConclusionsOutline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  39. 39. Introduction Methods Results Ongoing research: Mood contagion ConclusionsData set to study mood contagion in Twitter socialnetwork Nov. 20, 2008 until May 29, 2009 4,844,430 users 129M tweets Each tweet: ID, date:time, user ID, text, type, referredHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  40. 40. Introduction Methods Results Ongoing research: Mood contagion ConclusionsTurning followers into friends Twitter 2: Nov08-May09, 129M tweets 1 undirected network: Friend= A follows B, B follows A 2 sufficient tweet coverage: More than 1 tweet per day on average. edge weight Jaccard index of node’s friend list: Ci ∩ Cj wij = Ci ∪ Cj where Ci is k=1 neighborhood of iHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  41. 41. Introduction Methods Results Ongoing research: Mood contagion ConclusionsTwitter social network: users=130,636, edges=7,586,895Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  42. 42. Introduction Methods Results Ongoing research: Mood contagion ConclusionsPreliminary sentiment tracking via emoticons Emoticon ratio tn En = T tp Ep = T where tn and tp number of negative or positive tweets, and T total number of tweets submitted in 188 days N=189 user 1 ;-) ;-) ;-) ;-{ :- ;-/ ;-( ;-( ;-{ :- :- :- N=210 user 2 http://en.wikipedia.org/wiki/EmoticonHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  43. 43. Introduction Methods Results Ongoing research: Mood contagion ConclusionsIdentifying sour and happy users On the way home. Yay. Having pizza tonight. I think I got a cold. Poof! Just like that! :-/ - http://bkite.com/02yfl Morning so far: woke up to awful smell - 2 doggie ’presents’ in living room. :-/ Then a nearly flat rear tire. :-/ (squared) - http://... Well, I should be in bed ... so I guess I should try that. Work at 10 in the morning. :-/ - http://bkite.com/02Q2L Chance of Snow this weekend. :-/ - http://bkite.com/02V98 I’ve been hearing the words ”last” and ”final” a lot lately. :-) @HipMamaB what? I don’t understand that. why not?? ;-) ScarfWatch’09 - the theater lost found has it!! Things are looking up. ;-) It’s Crafty Mama’s night! Yay for CM night. :-) cast-iron skillets are the best. And having a pre-loved one is even better - already seasoned. :-)Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  44. 44. Introduction Methods Results Ongoing research: Mood contagion ConclusionsAssortativity vs. disassortativity From: Sid Redner (2008) Networks: Teasing out the missing links, Nature 453, 47-48 See also: M. E. J. Newman (2003) Mixing patterns in networks, Phys. Rev. E 67, 026126Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  45. 45. Introduction Methods Results Ongoing research: Mood contagion ConclusionsAssortativity vs. disassortativity Not me  ✎☞ ③  ✍✌ PS Bearman (2004). ”Chains of affection”, American Journal of Sociology, 100(1)Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  46. 46. Introduction Methods Results Ongoing research: Mood contagion ConclusionsExample twitter mood assortativity low SWB high SWB Red=happy, blue=sad white=neutralHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  47. 47. Introduction Methods Results Ongoing research: Mood contagion ConclusionsAssortativity of positive and negative sentimentHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  48. 48. Introduction Methods Results Ongoing research: Mood contagion Conclusionsego network of world’s most depressed userHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  49. 49. Introduction Methods Results Ongoing research: Mood contagion Conclusions Discussion LiteratureOutline 1 Introduction Collective Intelligence: from mobs to crowds Sentiment analysis: from mood to behavior 2 Methods Data Sentiment tracking instrument 3 Results Case-studies Cross-validation 4 Ongoing research: Mood contagion 5 Conclusions Discussion LiteratureHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  50. 50. Introduction Methods Results Ongoing research: Mood contagion Conclusions Discussion LiteratureDiscussion Power of collective intelligence: Wisdom of crowds extends to their mood state? Predictive power? Research front: growing support Business Can present models be translated to business applications? Prediction → control? Issues: User privacy, rights Dependencies on proprietary social networking systems Business vs. scienceHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  51. 51. Introduction Methods Results Ongoing research: Mood contagion Conclusions Discussion Literature References Johan Bollen, Huina Mao, and Xiao-Jun Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2010, http://arxiv.org/abs/1010.3003. (featured on CNBC, CNN International and Bloomberg News!) Johan Bollen, Huina Mao, and Alberto Pepe. Determining the public mood state by analysis of microblogging posts. Proceedings of the Proc. of the Alife XII Conference, Odense, Denmark, MIT Press, August 2010. Huina Mao, Alberto Pepe, and Johan Bollen. Structure and evolution of mood contagion in the Twitter social network. Proceedings of the International Sunbelt Social Network Conference XXX, Riva del Garda, Italy, July 2010 Johan Bollen, Alberto Pepe, and Huina Mao. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. arXiv:0911.1583 (November 2009)Huina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter
  52. 52. Introduction Methods Results Ongoing research: Mood contagion Conclusions Discussion Literature THANK YOU! jbollen@indiana.eduHuina Mao (IU) and Johan Bollen (IU)Acknowledgement: Bruno Goncalves, sentiment voorspelt aandelenkoersen Todd, and Ishani Ba Twitter Alex Vespignani, Eliot Smith, Peter

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