Peoplehavebeentellingstoriesforthousandsofyears

Whatmakessomemoreengaging?

NarrativeArcsandCulturalSuccess

Whydosomeculturalitemssucceedwhileothersfail?

Twopossibilities

#1:Successisrandom

Evendomainexpertshavedifficultypredictingsuccess(Bielby &Bielby,1994;Hirsch,1972)

Drivenbypatternsofsocialinfluence(Hahn&Bentley,2003;Salganik,Dodds,&Watts,2006)

#2:Individuallevelpsychologicalprocessesshapecollectiveoutcomes

Cultureshapespsychologicalprocesses(Markus&Kitiyama,1991).Reverseisalsotrue:

Psychologicalprocessesshapenormsandpractices

CulturalSelection

• Similartobiologicalnotions(Dawkins1976)

• Successdependsonfitbetweenitemcharacteristicsandsharedhumanpsychology– Emotions,memory,etc.(Heath,Bell,&Sternberg2001;Kashima

2008;Schaller&Crandall2004)

Problem:Howtoquantify“fit”withpeople?Featuresofitems?

Solution:NaturalLanguageProcessing

KeyQuestions

• Whatbestcapturesnarrativetrajectory?– Emotionalvalence?Arousal?Somethingelse?

• Howtobreakupculturalitemintochunks– Scenes?Similarlengths?

• Whatfeaturestocapture?– Micro-level/period-to-period-emotionalvolatility– Macro-level- #ofupsanddownsandsize

OriginalStarWars

Emotionsshapehowweevaluateexperiences

Positive>negativePeakandend(Frederickson2000;Redelmeier &Kahneman,1996)

Lessattentiontoemotionaldynamics(Kuppens &Verduyn 2017)

Particularlyvolatility

-2

-1

0

1

2

1 2 3 4

LowVolatility HighVolatility

VolatilityShouldIncreaseEvaluation

• Volatilitycreatesuncertaintyandsurprisewhichshouldincreaseemotionalimpact(Mellers,etal.,1997)

• Forpositiveexperiences(e.g.,booksandmovies),thisshouldboostevaluations

• Volatilityshouldprovidestimulationandreducehedonicadaptation(NelsonandMeyvis 2008),bothofwhichshouldleadpositiveexperiencestobeevaluatedmorefavorably

NoteaboutVolatility

• Focusonvolatilitybetweensizablechunksofamovie,likescenes,orportionsofthem.

• Moregranular(e.g.,second-to-second)volatilitylesslikelytoleaveanenduringimpressionandmorelikelytobemeasuredwitherror

• Further,ifamovierepeatedlyoscillatedbackandforthbetweenhighlynegativeandpositiveinamatterofseconds,itwouldexhausttheviewer.

Methods

1) Collectscriptsforthousandsofmovies– 4125moviesfromOpenSubtitles

• Scorepositivity/negativityofchunksoftext– Hedonometer(Dodds andDanforth2010)– 100segmentsof500words

1) Plotemotionaltrajectory2) Measureemotionalvolatility

– Std ofdifferencebetweensegments3) Testrelationshipwithsuccess(IMDBratings)

Results• Moreemotionallyvolatilemoviesreceivehigherratings(b=

9.40,s.e. =0.788, p <0.001)• 10%increaseinvolatilitylinkedto+1%inratings

Robustness• Movielength• Genre• YearofRelease• Amountofemotion– somemoviescontainmoreemotion• PeakandEnd• AverageSentiment– somemoviesaremorepos orneg• Complexity– morecharactersorcomplexaction• Extremity- howmuchatrajectorydivergesfromthemean• Resultsremainthesamecontrollingforallthesefactors

• Alternatesegmentstructures– Numberofsegments(e.g.,50)– Differentlength(e.g.,1000words)– Fixingoverlapbetweensegments(e.g.,1000words,900overlap)– Nooverlapbetweensegments

Canweisolateimpactofemotionalvolatility?

BestapproachwouldmanipulateONLYvolatility,keepeverythingelsethesame

Solution:movieswithsequels

IsolatingEmotionalVolatility

• Moviesinaseries(N=175)shouldperformbetterwhenthey’remoreemotionallyvolatile

• Result:Evenwithinthesameseries,moreemotionallyvolatilemoviesaremoresuccessful(b=8.86,s.e. =4.32,p=0.04)

• Originalseffect?– No.lookingwithinsequels(2nd vs.3rd movie),moreemotionally

volatilemoviesaremoresuccessful(b=19.37,s.e. =6.19, p =0.04)

VariationbyGenre

• Ifrelationshipisdrivenbysurprise/stimulation,shouldbestrongeringenreswherethoseaspectscontributemoretoevaluation

• E.g.,ThrillersandMysteries

“Macro”featuresmatteraswell

• Take100wordchunks• Smooththecurvetodropsmallhitches• Controlforlength

• (1)More“runs”(i.e.,upsanddowns),(2)Largeraverageruns,and(3)theirinteractionallpositivelylinkedtoratings

• Howcloseare“peaks”?

• Howdotheychangeovertime?– Dotheygetprogressivelylarger?– Morefrequent?

Summary

1. Narrativearcsmayhelpexplainculturalsuccess.

2. Individual-levelpsychologicalprocessescanshapecollectiveoutcomes(i.e.,culture)

3. NLPcanhelplinkmicro(psychological)andmacro(cultural)processes.

Thankyou.

Data

• DownloadedallmoviescriptsfromOpenSubtitles• MatchedwithIMDb.comprofilestoacquiregenreanduserrating

• Removemovieswithnomatchorveryfewwords• N=4125movies(Meanwords=7387,SD=2558)

CalculateEmotionalVolatility• Usehedonometer (Dodds andDanforth2010),toscore

sentimentofeachwordinthescript

• Followingpriorwork(Reaganetal2016),focusonwordswithclearemotionalcontent(i.e.,≥6or≤4)

• Dividemoviesinto100segmentsof500words– Resultsarerobusttodifferentsegmentnumbersandsizes

• Calculateemotionalscoreforeachsegment

• Volatility=standarddeviationofdifferencebetweensegments– Simplestd ofemotionalscoresdoesn’tindicateifchangeoccurs