The Language that Gets People to Give: Phrases that Predict Success on Kickstarter

1. The Language that Gets People to Give:Phrases that Predict Success on Kickstarter Tanushree Mitra & Eric Gilbert

5. What makes some projects succeed while others fail ?

6. Predictive Features of success and failure ?

7. QUANTITATIVE APPROACH Independent Variables Predictive Features Statistical Model Dependent Variable: Project outcome (Funded or Not)

8. QUANTITATIVE APPROACH Independent Variables Predictive Features (?) Statistical Model Dependent Variable: Project outcome (Funded or Not)

9. Category Goal Video Present Duration Facebook Connected Pitch

10. DATA 45,815K Kickstarter projects all projects as of June 2012 51.53% funded 48.47% not funded

11. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project description Scrape control variables Lowercase text Penalized Logistic Regression Remove stop words

12. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch Scrape control variables Lowercase text Statistical Model Remove stop words

13. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch Scrape control variables Lowercase text Statistical Model Remove stop words

14. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch Scrape control variables Lowercase text Statistical Model Remove stop words

15. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch Scrape control variables Lowercase text Statistical Model Remove stop words

16. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch Scrape control variables Lowercase text Statistical Model Remove stop words

17. ~20K unigrams, bigrams, trigrams Pitch

18. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch 59 control variables Scrape control variables Lowercase text Statistical Model Remove stop words

19. Category Goal Video Present Duration Facebook Connected

20. 45K Kickstarter project URLs [uni,bi,tri]-grams Fetch project end date phrase frequency > 50? Project reached end date? phrase in all 13 categories? Scrape project pitch 59 control variables Scrape control variables Lowercase text Statistical Model Remove stop words

21. STATISTICAL TECHNIQUE Independent Variables Phrases (20K) + Controls (59) Statistical Model Dependent Variable: Project outcome (Funded or Not)

22. STATISTICAL TECHNIQUE Independent Variables Phrases (20K) + Controls (59) Penalized Logistic Regression Dependent Variable: Project outcome (Funded or Not) Friedman et al. 2010

23. Results: MODEL FITS Baseline Model | Error | 48.47 % Controls Only Model Explanatory Power | Error 40.8 %| 17.03% Explanatory Power | Error Phrases + Controls Model 58.56 %| 2.24%

24. (NF) Predictors not been able( β = − 4.07 ) “I have not been able to finish the film because none of my editors will see the project through to the end.”

25. (NF) Predictors later i( β = − 3.04 ) hope to get( β = − 2.39 ) “I can’t take size orders and possibly hope to getthem all made in time for christmas.”

26. (NF) Predictors even a dollar ( β = − 3.10 )

27. Wattenberg&Viégas, 2008

28. (F) Predictors mention your( β =2.69 ) also receive( β = 1.83 ) add $40 and you will also receive two viptickets to the premiere screening.

29. (F) Predictors next step is ( β =1.07 ) Recording is pretty much done, next step is production.

30. (F) Predictors cats( β =2.64 )

31. UNDERSTANDING CONTEXT A closer look at predictive phrases

32. Principles of Persuasion Cialdini, R. B. 1993

33. Principles of Persuasion • Reciprocity • Scarcity • Authority • Social Proof • Social Identity • Liking Cialdini, R. B. 1993

34. Principles of Persuasion • Reciprocity • Scarcity • Authority • Social Proof • Social Identity • Liking Cialdini, R. B. 1993

35. RECIPROCITY Brehm & Cole 1966, Goranson & Berkowitz, 1966, Ciladini 2001

36. RECIPROCITY we’ll mention your (β= 2.69) name in the sleeve of our full length album

37. RECIPROCITY I will thank you on my website, send you goodkarmaand(β = 2.04)..

38. SCARCITY Ciladini 2001, Ciladini & Goldstein 2004

39. SCARCITY also, you will be given the chance (β= 2.69) to purchase our small batch pieces before the public domain

40. AUTHORITY Ciladini 2001, Ciladini & Goldstein 2004

41. AUTHORITY the project will be (β= 18.48) produced by dove award winning producer

42. SOCIAL PROOF Ciladini 2001

43. SOCIAL PROOF [name] has pledged (β = 5.42) some money..… so, you can see that i already have people willing to support my art.

44. Language is a reliable signal of success of crowd-funded projects So are some controls…. … …

45. IMPLICATIONS

46. IMPLICATIONS

47. http://www.cc.gatech.edu/~tmitra3/data/KS.predicts Also at: http://b.gatech.edu/1mf1C6E

48. The Language that Gets People to Give:Phrases that Predict Success on Kickstarter Tanushree Mitra & Eric Gilbert @tanmit | @eegilbert DATA: http://b.gatech.edu/1mf1C6E

50. Fancy Stats. Huh! Google 1T Corpus phrases Search for phrases with significantly higher difference + Membership higher in Ggle 1T χ2 test between phrase frequencies + Bonferroni Correction Scan presence of KS phrases in Google 1T 54K Kickstarter phrases Non-zero β weights • GENERAL PHRASES: • 494 positive predictors • 453 negative predictors next step is in the upcoming to announce …. provide us need one ….