April 19, Big data and human behavior. Pantelis P. Analytis. Manifestos. Click prediction. Information latent in the environment.

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1 April 19, / 46

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3 Potential uses of big data and naturally occurring datasets External validation of experiments. Demonstrate phenomena that motivate follow-on research. Discover patterns of information latent in s processed, and analyzed? Create stimuli for experiments. Construct and test computational models of cognition. 3 / 46

4 The four V s of big data Volume: How much data are included? Variety: How many different kinds or sources of data are included? Velocity: How quickly are the data able to be gathered, processed, and analyzed? Veracity: How faithfully do the data capture what they are believed to capture? 4 / 46

5 The first click model zn D. H. KRAFT and T. LEE Table 2. Expected search length formulas Rule (Condition) E(X) -!a?.- E(Z) satiation Rule (Ix+) R I N Disgust Rule (i>i) Combinaeion Rule (r>r and i>i) Satiation Rule (6=R) Combination Rule (rcr and VI) z Disgust Rule (il1) i R/(1+1) - Combination Rule (OR and 1~1) r I r I/(R+l) i r (N+l),'(R+lf i fn+l)/(i+l) r-l R I r-l R I r-l R I Combination Rule X ( (k)(i)/(k~iflik/(k+l) C { (k)li)/(k~l)}12/(k+l) 'r;r and 41) k-0 k=o k=o = i-1ri N i-l i-l R I N 2 R I N + E (rffk)/(k+rfr/wk) + ~t(r)(k)/(r+kf3rk/(r+kf + r Z (r)(kf,'k+r) k=o k=o k-0 Kraft, Donald H., and T. Lee. Stopping rules and their effect on expected search length. Processing Management 15.1 (1979): / 46

6 foraging theory Pirolli, Peter, and Stuart Card. foraging. Psychological review (1999): / 46

7 The position based model 7 / 46

8 The cascade model 8 / 46

9 Measures of retrieval effectiveness Cooper, William S. On selecting a measure of retrieval effectiveness. Journal of the American Society for Science 24.2 (1973): Carbonell, J., Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. SIGIR Järvelin, Kalervo, and Jaana Kekäläinen. (2002) Cumulated gain-based evaluation of IR. TOIS Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P. (2009). Expected reciprocal rank for graded relevance. CIKM 9 / 46

10 Dynamic Bayesian model 10 / 46

11 Prediction rates of different models 11 / 46

12 Reflections on memory Ebbinghaus was a pioneer of the experimental study of memory. He studied the effect of repetition on learning using non-sense syllabus doing experiments on himself. He spend 4 hours a day for his experiments and he controlled his life for a period longer than 2 years. 12 / 46

13 Laws of retention 13 / 46

14 Environment reflecting memory (Anderson and Schooler, 1991) 14 / 46

15 Spacing effect 15 / 46

16 Spacing effect (Anderson and Schooler, 1991) 16 / 46

17 The induction problem: how do people know so much with so little experience. Plato s Phaedon dialogue. Chomsky s linguistics and nativism. Shepard s law of generalization: placing the problem at the center of psychology. All of machine learning is about induction. 17 / 46

18 Latent semantic analysis (Landauer and Dumais, 1997) Landauer and Dumais assume that conceptual similarity is based on co-occurrence in natural language. They trained the model using 4.6m words from Grolier s Academic American Encyclopedia. They either used the first 2000 words or the entire article 18 / 46

19 Latent semantic analysis (Landauer and Dumais, 1997) They tested the performance of the model on the Toefl test (80 synonym items). The model achieved performance comparable to the average foreigner student taking the test. 19 / 46

20 Biases in the (Lacetera et al., 2012) cars from 2002 to Average car is 4 years old with in the meter. 82 % of the cars were sold. 20 / 46

21 Games and (Stafford and Dewar, 2014) Spacing practice leads to better results than cramming. Learning curves are characterized by the power law of practice. It takes 10000h of practice to completely master a skill. (Ericsson,2006). 21 / 46

22 Games and (Stafford and Dewar, 2014) 22 / 46

23 Games and (Stafford and Dewar, 2014) 23 / 46

24 Games and (Stafford and Dewar, 2014) 24 / 46

25 Assessing personalities 25 / 46

26 Using likes to predict personality (Youyou et al., 2015) 26 / 46

27 Using likes to predict personality (Youyou et al., 2015) 27 / 46

28 Psychological targeting (Matz et al., 2017) 28 / 46

29 Psychological targeting (Matz et al., 2017) 29 / 46

30 Predicting sexual orientation (Kosinski and Wang, 2017) 30 / 46

31 Predicting sexual orientation (Kosinski and Wang, 2017) 31 / 46

32 To vote or not to vote? Two world-class economists who run into each other at the voting booth. What are you doing here? one asks. My wife made me come, the other says. The first economist gives a confirming nod. The same. After a mutually sheepish moment, one of them hatches a plan: If you promise never to tell anyone you saw me here, I ll never tell anyone I saw you. They shake hands, finish their polling business and scurry off. 32 / 46

33 Political mobilization (Bond et al., 2012) 33 / 46

34 Political mobilization (Bond et al., 2012) The introduction of the button might have caused a 0.60 % increase in the 2010 congressional election. Most of this effect can be attributed to the impact of strong ties. 34 / 46

35 Social influence (Muchnik et al. 2013) experimental comments, 4049 were uptreated, 1942 downtreated. Viewed 10 7 times and rated times. 35 / 46

36 Emotion contagion the tendency to automatically mimic and synchronize expressions, vocalizations, postures, and movements with those of another person s and, consequently, to converge emotionally. 36 / 46

37 Emotion contagion over the internet (Kramer et al., 2014) Scientists at Facebook manipulated the newsfeed of users. Some of them were exposed to linguistically more positive content. While others where exposed to more negative content. 37 / 46

38 Pushback to the emotion contagion study The data did not violate Facebook s consent policy. Debate: was it ethical for facebook to conduct such an experiment? 38 / 46

39 Using searched terms for (Choi and Varian, 2012) 39 / 46

40 Using searched terms for (Choi and Varian, 2012) 40 / 46

41 Predicting movies, video games and music (Goel et al., 2010) 41 / 46

42 Predicting movies, video games and music (Goel et al., 2010) 42 / 46

43 Predicting movies, video games and music (Goel et al., 2010) 43 / 46

44 Beating the market (Preis et al., 2013) 44 / 46

45 Beating the market (Preis et al., 2013) 45 / 46

46 The parable of the flu (Lazer et al., 2014) 46 / 46