Quantifying human behaviour using online data. Suzy Moat Data Science Lab Behavioural Science, WBS

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Ethelbert McDaniel
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1 Quantifying human behaviour using online data Suzy Moat Data Science Lab Behavioural Science, WBS

2 Data Science Lab Behavioural Science, WBS

3 1 The advantage of looking forward

4 more Google searches for 2009 more Google searches for 2011 Future Orientation Index 2012 Suzy Moat & Tobias Preis Based on Preis, Moat, Stanley and Bishop (2012) Ratio of Google searches for 2011 to searches for 2009 during 2010 for 45 countries Future Orientation Index 2010 Ratio of Google searches for 2013 to searches for 2011 during 2012 for 45 countries

5 Richer countries look forward 4 GDP / Capita [10 USD] B Preis, Moat, Stanley & Bishop (2012) Featured by: Future-Orientation Index

6 Photo: Perpetual Tourist 2 Anticipating market moves

7 Hypothetical strategy number of Wikipedia page views week t Moat et al. (2013); Preis et al. (2013)

8 Hypothetical strategy number of Wikipedia page views week t-3 t-2 t-1 t Moat et al. (2013); Preis et al. (2013)

9 Hypothetical strategy number of Wikipedia page views Page views decreased: BUY stock in week t+1 week t-3 t-2 t-1 t Moat et al. (2013); Preis et al. (2013)

10 Hypothetical strategy number of Wikipedia page views Page views decreased: BUY stock in week t+1 week t-3 t-2 t-1 t Moat et al. (2013); Preis et al. (2013) Page views increased: SELL stock in week t+1

11 Wikipedia: Dow Jones companies Wikipedia Views DJIA Companies 0.6 Density Wikipedia Edits DJIA Companies Moat, Curme, Avakian, Kenett, Stanley & Preis (2013) Random Strategy Return [Std. Dev. of Random Strategies] Featured by: Views data: significant diﬀerence

12 Wikipedia: Financial topics 1.00 Density 0.75 Wikipedia Edits Financial Topics Wikipedia Views Financial Topics Random Strategy Return [Std. Dev. of Random Strategies] Moat, Curme, Avakian, Kenett, Stanley & Preis (2013) Featured by: Views data: significant diﬀerence

13 Wikipedia: Actors and filmmakers? 0.4 Density 0.3 Wikipedia Views Actors & Filmmakers Random Strategy Return [Std. Dev. of Random Strategies] Moat, Curme, Avakian, Kenett, Stanley & Preis (2013) Featured by: Views data: no significant diﬀerence

14 How Google keywords perform return (random strategy sds) 2 Random strategy mean + 2 sds 1 Random strategy mean + 1 sd 0-1 debt Preis, Moat & Stanley (2013) culture

15 How Google keywords perform return (random strategy sds) 2 Random strategy mean + 2 sds 1 Random strategy mean + 1 sd 0-1 debt credit stocks Preis, Moat & Stanley (2013) culture train garden

16 How Google keywords perform return (random strategy sds) 2 Random strategy mean + 2 sds 1 Random strategy mean + 1 sd 0-1 Financial relevance # occurrences in FT # hits on Google Returns significantly correlated with indicator of financial relevance Preis, Moat & Stanley (2013)

17 What is searched for before falls? debt internet money money crisis technology debt Curme, Preis, Stanley & Moat (2014)

18 What is searched for before falls? Cumulative Returns (%) Random Strategy Politics I Business 55 groups of search terms Business and politics most related Curme, Preis, Stanley & Moat (2014)

19 3 Photographers as sensors

20 Flickr and tourist numbers Barchiesi, Moat, Alis, Bishop & Preis (under review)

21 Scenicness and wellbeing Photo: Tom Richardson Seresinhe, Preis & Moat (under review)

22 Scenicness and wellbeing A POOR HEALTH Liverpool Newcastle Manchester Sheffield Birmingham London Average rates of poor health (SMR) B C Seresinhe, Preis & Moat (under review)

23 London es of (SMR) Scenicness and wellbeing C SCENICNESS Average scenic rating Seresinhe, Preis & Moat (under review)

24 London es of (SMR) Scenicness and wellbeing C SCENICNESS People report better health in more scenic Average scenic rating locations Seresinhe, Preis & Moat (under review)

25 PO London Scenicness Average and rates of wellbeing poor health (SMR) B C GREENSPACE SCENICNESS Average percentage of greenspace Average scenic rating D del ) 1.00 Seresinhe, Preis & Moat (under review)

26 PO PO London Scenicness Average and rates of of wellbeing poor health (SMR) B C GREENSPACE GREENSPACE SCENICNESS SCENICNESS Average percentage of of greenspace Average scenic rating D del del ) Seresinhe, Preis & Moat (under review)

27 GRE Scenicness and wellbeing Average percentage of greenspace SCE Average scenic rating D 1.00 Probablity of the model given the data (AICw) All areas Urban Suburban Rural Model Scenicness only Greenspace only Scenicness and Greenspace Seresinhe, Preis & Moat (under review)

28 4 Measuring flu with Google

29 Flu data Google Trends estimate Ginsberg et al., Nature 368, 1012 (2009)

30 The press reports may have triggered many flurelated searches by people who were not ill. Butler, Nature 494, 155 (2013)

31 Training Period Influenza Like Illness [%] Out-of-Sample Nowcast 6 Predicted Value Observed Value 80% Prediction Interval 95% Prediction Interval Time [Weeks] 2013 Flu estimate errors significantly reduced by between 16% and 53%. Preis & Moat (2014)

32 @suzymoat To what extent can Internet data help us measure and even predict human behaviour? computer science statistics physics mathematics psychology economics geomatics finance crime science health

T he increasing volumes of big data reflecting various aspects of our everyday activities represent a vital new

T he increasing volumes of big data reflecting various aspects of our everyday activities represent a vital new SUBJECT AREAS: STATISTICAL PHYSICS, THERMODYNAMICS AND NONLINEAR DYNAMICS APPLIED PHYSICS COMPUTATIONAL SCIENCE INFORMATION THEORY AND COMPUTATION Received 25 February 213 Accepted 3 April 213 Published