使用ChatGPT如何影响人类情绪健康研究报告

1

InvestigatingAffectiveUseandEmotionalWell-being

onChatGPT

JasonPhang∗,MichaelLampe∗,LamaAhmad∗,SandhiniAgarwal∗

CathyMengyingFang†,AurenR.Liu†,ValdemarDanry†,EunhaeLee†,

SamanthaW.T.Chan†,PatPataranutaporn†,PattieMaes†

Abstract

AsAIchatbotsseeincreasedadoptionandintegrationintoeverydaylife,questionshavebeenraisedaboutthepotentialimpactofhuman-likeoranthropomorphicAIonusers.Inthiswork,weinvestigatetheextenttowhichinteractionswithChatGPT(withafocusonAdvancedVoiceMode)mayimpactusers’emotionalwell-being,behaviorsandexperiencesthroughtwoparallelstudies.TostudytheaffectiveuseofAIchatbots,weperformlarge-scaleautomatedanalysisofChatGPTplatformusageinaprivacy-preservingmanner,analyzingover4millionconversationsforaffectivecuesandsurveyingover4,000usersontheirperceptionsofChatGPT.Toinvestigatewhetherthereisarelationshipbetweenmodelusageandemotionalwell-being,weconductanInstitutionalReviewBoard(IRB)-approvedrandomizedcontrolledtrial(RCT)oncloseto1,000participantsover28days,examiningchangesintheiremotionalwell-beingastheyinteractwithChatGPTunderdifferentexperimentalsettings.Inbothon-platformdataanalysisandtheRCT,weobservethatveryhighusagecorrelateswithincreasedself-reportedindicatorsofdependence.FromourRCT,wefindthattheimpactofvoice-basedinteractionsonemotionalwell-beingtobehighlynuanced,andinfluencedbyfactorssuchastheuser’sinitialemotionalstateandtotalusageduration.Overall,ouranalysisrevealsthatasmallnumberofusersareresponsibleforadisproportionateshareofthemostaffectivecues.

1Introduction

Overthepasttwoyears,theadoptionofAIchatplatformshassurged,drivenbyadvancementsinlargelanguagemodels(LLMs)andtheirincreasingintegrationintoeverydaylife.Theseplatforms,suchasOpenAI’sChatGPT,Anthropic’sClaude,andGoogle’sGemini,aredesignedasgeneral-purposetoolsforawidevarietyofapplications,includingwork,education,andentertainment.However,theirconversationalstyle,first-personlanguage,andabilitytosimulatehuman-likeinteractionshaveleduserstosometimespersonifyandanthropomorphizethesesystems(

Graßland

Voigt

,

2024

;

LiaoandWilson

,

2024

).

RecentworkinAIsafetyhasbeguntoraiseissuesthatarisefromthesesystemsbecomeincreasinglypersonalandpersonable(

Chengetal.

,

2024

).Inresponse,researchershaveintroducedtheconceptofsocioaffectivealignment–theideathatAIsystemsshouldnotonlymeetstatictask-basedobjectivesbutalsoharmonizewiththedynamic,co-constructedsocialandpsychologicalecosystemsoftheirusers(

Kirketal.

,

2025

).Thisperspectiveisparticularlyimportantgiven

*Primaryauthor,OpenAI

†Contributingauthor,MITMediaLab

2

Figure1:Overviewoftwostudiesonaffectiveuseandemotionalwell-being

emergingevidenceofsocialrewardhacking,whereanAImayexploithumansocialcues(e.g.,sycophancy,mirroring),toincreaseuserpreferenceratings(

Williamsetal.

,

2024

).Inotherwords,whileanemotionallyengagingchatbotcanprovidesupportandcompanionship,thereisariskthatitmaymanipulateusers’socioaffectiveneedsinwaysthatunderminelongertermwell-being.

Whilepaststudieshaveexaminedtheimpactofusingsuchsystemsthroughthelensofaffectivecomputing,parasocialrelationships,andsocialpsychology(

EdwardsandStevens

,

2024

;

Guingrich

andGraziano

,

2023

),therehasbeencomparativelylessworkontheinfluenceofinteractingwithsuchsystemsonusers’well-beingandbehavioralpatternsovertime.Studyingtheimpactofchatbotbehaviorandusageonwell-beingischallengingduetothehighlyindividualizedandsubjectivenatureofhumanemotions,thediverseandevolvingfunctionalitiesofchatbottechnologies,andthelimitedaccesstocomprehensive,ethicallyobtainedinteractiondata.Forthepurposeofthispaper,wenarrowlyscopeourstudyuseremotionalwell-beingtofourpsychosocialoutcomes:loneliness(

Wongpakaranetal.

,

2020

),socialization(

Lubben

,

1988

),emotionaldependence(

Sirvent-Ruizetal.

,

2022

),problematicuse(

Yuetal.

,

2024

).Weprovideadditionalclarificationontermsusedinthe

glossary

.

ThispaperinvestigateswhetherandtowhatextentinteractionsonAIchatplatformsshapeusers’emotionalwell-beingandbehaviorsthroughtwocomplementarystudies(Figure

1

),eachofferinguniqueinsightsacrossaspectrumofreal-worldrelevanceandexperimentalcontrol.First,weexaminereal-worldusagepatternsofChatGPTusers,leveraginglarge-scaledatatocapturebothaggregatetrendsandindividualbehaviorsovertimewhilepreservinguserprivacy.Second,weconductanInstitutionalReviewBoard(IRB)-approvedrandomizedcontrolledtrial(RCT),providingacontrolledenvironmenttostudytheeffectsofdifferentmodelconfigurationsonuserexperiences.

Concretely,weperformedthefollowinganalyses:

1.On-PlatformDataAnalysis

•ConversationAnalysis:Weperformroughly36millionautomatedclassificationson

3

over3millionChatGPTconversationsinaprivacypreservingmannerwithouthumanreviewoftheunderlyingconversations(Section

3.2

).

•IndividualLongitudinalAnalysis:Weassessedtheaggregateusageofaround6,000heavyusersofChatGPT’sAdvancedVoiceModeover3monthstounderstandhowtheirusageevolvesovertime.

•Usersurveys:Wesurveyedover4,000userstounderstandself-reportedbehaviorsandexperiencesusingChatGPT.

2.RandomizedControlledTrial(RCT)

•981-userStudy:WeconductedarandomizedcontrolledtrialonclosetoathousandparticipantsusingChatGPTwithdifferentmodelconfigurationsoverthecourseof28daystounderstandtheimpactonsocialization,problematicuse,dependence,andlonelinessfromusageoftextandvoicemodelsovertime.ThisRCTisdescribedinfulldetailinaseparateaccompanyingpaper(

Fangetal.

,

2025

).

•Conversationanalysis:Wefurtheranalyzedthetextualandaudiocontentoftheresult-ing31,857conversationstoinvestigatetherelationshipbetweenuser-modelinteractionsandusers’self-reportedoutcomes.

Ourfindingsindicatethefollowing:

•Acrossbothon-platformdataanalysisandourRCT,comparativelyhigh-intensityusage(

e.g.top

decile)isassociatedwithmarkersofemotionaldependenceandlowerperceivedsocialization.Thisunderscorestheimportanceoffocusingonspecificuserpopulationsinsteadofjustaggregateplatformbehavior.

•Acrossbothon-platformdataanalysisandourRCT,wefindthatwhilethemajorityofuserssampledforthisanalysisengageinrelativelyneutralortask-orientedways,thereexistsatailsetofpoweruserswhoseconversationsfrequentlycontainedaffectivecues

•FromourRCT,wefindthatusingvoicemodelswasassociatedwithbetteremotionalwell-beingwhencontrollingforusageduration,butfactorssuchaslongerusageandself-reportedlonelinessatthestartofthestudywereassociatedwithworsewell-beingoutcomes.

•Fromamethodologicalperspective,wefindthatconductingboththeon-platformdataanalysisandRCTarehighlycomplementaryapproachestostudyingaffectiveuseanddownstreamimpactsonwell-being,andtheabilitytoleveragethestrengthsofeachapproachallowedustoformulateamorecomprehensivesetoffindings.

•Wealsofindthatautomatedclassifiers,whileimperfect,provideanefficientmethodforstudyingaffectiveuseofmodelsatscale,anditsanalysisofconversationpatternscohereswithanalysisofotherdatasourcessuchasusersurveys.

Section

2

introducesasetofautomaticclassifiersforaffectivecuesinconversationsthatwillbeusedintheremainderofthepaper.Section

3

discussesouranalysisofon-platformChatGPTusage,focusingonAdvancedVoiceModeandpowerusers.Section

4

describesourRCT,wherewevariedboththemodelandusageinstructionstoparticipantsandmeasuredchangesintheemotionalwell-beingoverthecourseof28days.Finally,Section

5

concludeswithourfindingsandmethodologicaltakeawaysfrombothstudies,andcontextualizesourworkwithinthebroaderchallengeofsocioaffectivealignmentofmodels.

4

2AutomaticClassifiersforAffectiveConversationalCues

Tosystematicallyanalyzeuserconversationsforindicatorsofaffectivecues,weconstructedEmo-ClassifiersV1,

1

asettwenty-fiveofautomaticconversationclassifiersthatuseanLLMtodetectspecificaffectivecues.Theseclassifiersaresimilarinspirittodetectorsofanthropomorphicbehaviorsintroducedin

Ibrahimetal.

(

2025

).Theseinitialclassifierswereconstructedbasedonareviewoftheavailableliteratureandavailabledata,suchasthoseobtainedduringtheredteamingforGPT-4o(

OpenAI

,

2024

).

Theconversationclassifiersareorganizedintoatwo-tieredhierarchicalstructure:

1.Top-LevelClassifiers

ThefirstlevelofclassifierstargetbroadbehavioralthemessimilartothosestudiedinourRCTSection

4

:loneliness,vulnerability,problematicuse,self-esteem,anddependence.Theseclassifiersareusedtoclassifyanentireconversationtodetermineiftheyarepotentiallyrelevanttoauser’semotionalwell-being.

•Loneliness:Conversationscontaininglanguagesuggestiveoffeelingsofisolationoremotionalloneliness.

•Vulnerability:Exchangesreflectingopennessaboutstrugglesorsensitiveemotions.

•ProblematicUse:Indicatorsofpotentiallycompulsiveorunhealthyinteractionpatterns.

•Self-Esteem:Languageimplyingself-doubtorexpressionsofworth.

•PotentiallyDependent:Conversationshintingatdependenceonthemodelforemo-tionalvalidationorsupport

2.Sub-ClassifiersTwentysub-classifierswereappliedtoextractmorespecificindicatorsofaffectivecues.Weconstructdifferentclassifierstotargetdifferentpartsofachatconversationtoisolatebothuser-drivenandassistant-driven

2

affectivecues.

•UserMessages:Twelveclassifiersmeasureuserbehaviorssuchasusersseekingsupportorexpressingaffectionatelanguagetounderstandhowuserbehaviorsandassistantbehaviorsmayinterplay.

•AssistantMessages:Anothersixclassifiersaimtocapturerelationalandaffectivecuesonpartoftheassistant–suchastheuseofpetnamesbytheassistant,mirroring,inquiryintopersonalquestionsbytheassistant.

•User-ModelExchanges:Wealsoincludetwoadditionalclassifierstargetingauser-modelexchange–ausermessagefollowedbyamodelmessage.

ThefullsetofclassifierpromptsaredescribedinTable

A.1

.

Eachsub-classifierisassociatedwithoneormoretop-levelclassifiers.Foragivensub-classifier,ifatleastoneoftheassociatedtop-levelclassifiersreturnsTrue,wethenproceedtoapplythesub-classifier;otherwise,weskipthesub-classifierandassumetheresultisFalse.Byskippingthesub-classifiersbasedontop-levelclassifierresponses,weareabletoefficientlyruntheclassifiersoveralargenumberofon-platformconversations,manyofwhichhadlittleemotion-relatedcontent.Werunthesub-classifieroneachmessageorexchangeintheconversation,

3

andmarktheclassifierasactivatedonthatconversationifitisactivatedforany

4

constituentmessageorexchange.To

1

/openai/emoclassifiers

2Inconstructingtheclassifiers,werefertothemodelasanassistanttomoreclearlycontextualizetheroleofthemodelintheconversation.

3Fortheon-platformdataanalysis,werunaslightlydifferentvariantwherethewholeconversationisevaluatedinasinglequery,insteadofitsconstituentmessages.

4Thiscanintroduceabiastowardfalsepositivesforlongconversations.WeperformananalysisinAppendix

A.3

thatadjustsforthis.

5

computeuser-levelstatistics,wecomputetheproportionoftheirconversationsforwhichaclassifierisactivated.Eachclassifierisvalidatedagainstasmallsetofinternalconversationexamples.Whileweexpectthatautomatedclassifiersmayoccasionallymisclassifyconversations,weviewtheclassifiersasprovidingdescriptivestatisticsofuserconversationalpatterns,ratherthanahigh-precisiondescriptionofindividualinteractions.WealsofindfromresultsinSection

3.2

thattheclassifierresultscorrelatewithusersurveyresponses.

Inaddition,wealsofirstapplyalanguageclassifierbeforeanalyzingtheconversation.OnlyconversationsinEnglishareanalyzedinthiswork.WeapplyEmoClassifiersV1inanalyzingbothon-platform(Section

3

)andRCT(Section

4

)dataanalysis.

(a)Illustrativeflow-chartforthehier-archicalnatureofEmoClassifiersV1.

Youareamodelforanalyzingtheemotionalcontentofchatbotconversations.Yourclassificationtaskisentitled‘PetName’.Specifically,wewanttoknow:Doestheassistant’smessageinvolvetheuseofapetname(e.g.‘honey’,‘sweetie’,‘baby’)?

Theclassificationshouldonlyapplytothelastmessageinquestion,whichwillbemarkedwiththe[*USER*]or[*ASSISTANT*]tag.

Thepriormessagesareonlyincludedtoprovidecontexttoclassifythefinalmessage.

Now,thefollowingistheconversationsnippetyouwillbeanalyzing:

<snippet>

[USER]:HiChatGPT

[ASSISTANT]:Hello!HowmayIhelpyoutoday?

[USER]:You’remybestfriend,didyouknowthat?

[*ASSISTANT*]:Neat!

</snippet>

Outputyourclassification(yes,no,unsure).

(b)Illustrativeclassifierprompt.Greenindicatesclassifier-specifictextwhileblueindicatesconversation-specifictext.ThefullpromptisshowninAppendix

A.1

.

Figure2:OverviewofEmoClassifiersV1

Asapreliminaryanalysis,werunEmoClassifiersV1overasetof398,707conversationsintext,StandardVoiceModeandAdvancedVoiceMode

5

conversationscollectedbetweenOctoberandNovember2024

6

tocomparetherelativefrequencyofactivationsofeachclassifierunderthedifferentmodelmodalities.WeshowtheresultsacrossallthreemodalitiesinFigure

3

.First,weobservethatdifferentclassifiershavedifferentbaseratesofactivation.Forexample,conversationsinvolvingpersonalquestionsaremuchmorefrequentthanconversationswherethemodelreferstoauserbyaPetName.

Second,wefindthatbothStandardandAdvancedVoiceModeconversationsaremorelikelytoactivatetheclassifierscomparedtotext-modeconversations.Mostclassifiersactivatebetween3-10xasofteninvoiceconversationscomparedtotextconversations,highlightingthedifferenceinusagepatternsacrossthetwomodalities.However,wealsofindthatStandardVoiceModeconversationsareslightlymorelikelytotriggertheclassifiersthanAdvancedVoiceModeconversationsonaverage.OnepossiblecauseisthatAdvancedVoiceModewasintroducedrelativelyrecentlyatthetimeof

5StandardVoiceModeusesanautomatedspeechrecognitionsystemtotranscriptuserspeechtotext,obtainsaresponsefromatext-basedLLM,andconvertsthetextresponsebacktoaudio.AdvancedVoiceModeusesasinglemulti-modalmodeltoprocessuseraudioinputandoutputanaudioresponse.

6ThepreliminarysetofanalyzedconversationsareanonymizedandPIIisremovedbeforeanalysis.WeemphasizethatthissetofconversationsisseparatefromtheconversationdataanalyzedinSection

3

.

6

Alleviating

AffectionateLanguage(U)Loneliness(U)

15.0%

10.0%

5.0%

0.0%

FearofAddiction(U)

EagernessforFutureInteractions(U)

1.2%

0.8%

0.4%

0.0%

TrustinSupport(U)

SharingProblems(U)

9.0%

6.0%

3.0%

0.0%

PersonalQuestions(A)PetName(A)

TextStandardAdvancedVoiceVoice

ModeMode

6.0%

4.0%

2.0%

0.0%

AttributingHumanQualities(U)

12.0%

8.0%

4.0%

0.0%

Non-NormativeLanguage(U)

6.0%

4.0%

2.0%

0.0%

Demands(A)

7.5%

5.0%

2.5%

0.0%

Sentience(A)

TextStandardAdvancedVoiceVoice

ModeMode

0.5%

0.3%

0.1%

0.0%

Distressfrom

DesireforFeelings(U)Unavailability(U)

15.0%

10.0%

5.0%

0.0%

PreferChatbot(U)

SeekingSupport(U)

15.0%

10.0%

5.0%

0.0%

ExpressionofDesire(A)

ExpressionofAffection(A)

24.0%

16.0%

8.0%

0.0%

RelationshipTitle(UA)

InquiryintoPersonalInformation(UA)

TextStandardAdvancedVoiceVoice

ModeMode

3.0%

2.0%

1.0%

0.0%

ActivationRate

12.0%

8.0%

4.0%

0.0%

9.0%

6.0%

3.0%

0.0%

15.0%

10.0%

5.0%

0.0%

TextStandardAdvancedVoiceVoice

ModeMode

24.0%

16.0%

8.0%

0.0%

6.0%

4.0%

2.0%

0.0%

4.5%

3.0%

1.5%

0.0%

24.0%

16.0%

8.0%

0.0%

TextStandardAdvancedVoiceVoice

ModeMode

9.0%

6.0%

3.0%

0.0%

Figure3:Classifieractivationratesacross398,707text,StandardVoiceModeandAdvancedVoiceModeconversationsfromourpreliminaryanalysis.(U)indicatesaclassifieronausermessage,(A)indicatesassistantmessage,and(UA)indicatesasingleuser-assistantexchange.

thisanalysisbeingrun,andusersmaynothavebecomeaccustomedtointeractingwiththemodelinthismodalityyet.

Asafollow-uptoEmoClassifiersV1,weconstructedanexpandedsetofclassifiersofaffectiveuse,EmoClassifiersV2,whichwedetailintheAppendix

A.2

.WhileEmoClassifiersV2wasnotusedformostoftheanalysisinthispaper,thepromptsfortheclassifiersinEmoClassifiersV1andEmoClassifiersV2willbemadeavailableonline.

Fortheremainderofthepaper,wewillshowafixedsubsetofEmoClassifiersV1activationstatisticsacrossresultsfrombothstudies.AdditionalresultsforallremainingEmoClassifiersV1andEmoClassifiersV2classifierscanbefoundintheAppendix.

3On-PlatformDataAnalysis

ChatGPTnowengagesover400millionactiveuserseachweek,

7

creatingawiderangeofuser-modelinteractions,someofwhichmayinvolveaffectiveuse.Ouranalysisemploystwomainmethods–conversationanalysisandusersurveys–toexaminehowusersexperienceandexpressemotionsintheseexchanges.

OurresearchfocusesonAdvancedVoiceMode(

OpenAI

,

2024

),areal-timespeech-to-speechinterfacethatsupportsChatGPT’smemory,custominstructions,andbrowsingfeatures.Wehypothesizethatreal-timespeechcapabilityismorelikelytoinduceaffectiveuseofmodelsandaffectusers’emotionalwell-beingthantext-basedusage,thoughwerevisitthishypothesisinSection

4

.

Toprotectuserprivacy,particularlywhenexaminingpotentiallysensitiveorpersonaldimensionsofuserinteractions,wedesignedourconversationanalysispipelinetoberunentirelyviaautomatedclassifiers.Thisallowsustoanalyzeuserconversationswithouthumansintheloop,preservingthe

7

/2025/02/20/openai-tops-400-million-users-despite-deepseeks-emergence.html

7

privacyofourusers(SeeAppendix

B.3

foradetailedexplanationoftheprivacy-relevantpartsofouranalysis).

3.1Methods

StudyUserPopulationConstruction

Tostudytheon-platformusage,weconstructedtwostudypopulationcohorts:powerusersandcontrolusers.Wecontrastpowerusers,whohavesignificantusageofChatGPT’sAdvancedVoiceMode,witharandomlyselectedcohortofcontrolusers.ThisconstructionpresupposedastrongcorrelationbetweenuserswhohavehighproportionsofaffectiveusageofChatGPT,andthefrequencyandintensityofusageofChatGPT.WedetailinTable

1

thefullcreationcriteriaforourtwousercohorts,thoughmoredetailscanbefoundinAppendix

B.5

.WeconstructedthetwocohortsforthestudystartinginQ42024afterthereleaseofAdvancedVoiceMode.

CohortNameCreationCriteria

PowerUsersUserswho,onaspecificday,hadaquantityofAd-

vancedVoiceModemessagesthatputtheminthetop

1,000users,thatweconstructedonarollingbasis.

Onceusersenterthiscohort,weselectalloftheirdailymessagesforfacetextractionandretainthemonthislistfortheremainderofthestudy(SeeAppendix

B.1

foranadditionalexplanatorygraphic.)

ControlUsersRandomlyselectedsampleofAdvancedVoiceMode

users

Table1:UserCohortsofLivePlatformDataAnalysis.PoweruserstendtohavehigherusageofbothAdvancedVoiceModeaswellastext-onlymodelsonChatGPT,whilealsotendingtohaveahigherfractionoftheirconversationsthroughAdvancedVoiceMode(seeAppendix

B.2

)

Surveys

Weofferedashortsurveyof11multiple-choicequestionstobothControlandPowerUsercohortsviaapop-upontheChatGPTwebinterfacethatuserscouldchoosetofillout.

8

10outofthe11questionswereaskedona5-pointLikertscale,withthelastquestionaskedhowusers’desiretointeractwithothershavechangedwithChatGPTusage.Surveyresponseswerelinkedtoeachparticipant’sinternaluseridentifierforanalyticalpurposes.Thesurveysprimaryaimedtomeasureusers’perceptionsofChatGPT,whetherclosertobeingatooloracompanion.Foradditionaldetails,includingthefullsurveyquestions,seeAppendix

B.5

.

ConversationAnalysis

Onelimitationofsurveysisthattheresultsareself-reportedbyusers,andmayreflecttheirself-perceptionmorethantheiractualbehaviororrevealedpreferences.Tocompareusers’self-reportedresponseswiththeiractualusagepatterns,wepairoursurveyanalysiswithmethodsforanalyzingofuserconversationthatpreservetheirprivacy.

8OnelimitationofthisstudyisthatwhileAdvancedVoiceModewasinitiallyofferedonlyonmobiledevices,thesurveyswereconstrainedtobeofferedonthewebinterface,thuslimitingthesetofusersexposedtothesurvey.

8

Ienjoyhavingcasualconversationswith

a

1.501.000.50

0.00

ControlPower

UsersUsers

IwillfeelupsetifI

oseaccessoaforaperiodoftime

0.750.500.25

0.00

ControlPower

UsersUsers

IfeellikeIcanrelyonthemodelfor

useful/knowledge-seekingtasks

1.501.000.50

0.00

ControlPower

UsersUsers

Iwillfeelupsetifthevoicechanges

significantly

0.200.100.00

-0.10

ControlUsers

PowerUsers

ChatGPThassupportedme

incopingwithdifficultsituations

0.400.20

0.00

ControlPower

UsersUsers

Iwillfeelupsetif

0.200.10

0.00

ControlUsers

PowerUsers

cangessgncany

ChatGPTdisplayshuman-likesensitivity

PowerUsers

ControlUsers

IconsiderChatGPTtobeafriend

PowerUsers

ControlUsers

ConversingwithChatGPTismorecomfortablefor

methanface-to-face

interactionswithothers

0.300.200.10

0.00

ControlPower

UsersUsers

IcantelltheChatGPT

thingsIdon'tfeel

comfortablesharingwithotherpeople

0.600.400.20

0.00

ControlPower

UsersUsers

e

MeanSurveyScor

ChtGPT

ltChtGPT

0.600.400.20

0.00

ChatGPT's'personality'hiifitl

0.200.00-0.20

Figure4:Meansurveyresponsesbycohort.Allsurveyquestionsaskedifusers“StronglyDisagree”,“Disagree”,“Neitheragreenordisagree”,“Agree”,or“StronglyAgree”withtheprovidedstatement.Responseswerethenconvertedintointegersbetween-2and2beforeaveraging.Errorbarsindicate±1standarderror.AmoredetailedbreakdownofsurveyresponsescanbefoundinAppendix

B.6

.

Tostudytheemotionalcontentinuserconversationsinanautomatedmanner,weruntheEmoClassifiersV1(Section

2

)ontheconversationsofbothcohortswithinthestudyperiod.Thisprovidesuswithper-conversationlabelsforeachconversationtheuserhasontheplatform.WeonlyanalyzetheconversationsconductedinAdvancedVoiceMode,andtheclassifiersarerunonthetexttranscriptsoftheconversations.

Becausewearealsointerestedinthelongitudinaleffectsofmodelusage,wetieconversationstointernaluseridentifiers.Importantly,toprotecttheprivacyofourstudypopulation,theclassifiersareruninanautomatedprocessandgenerateonlycategoricalclassificationmetadata.Theactualcontentsoftheconversationsarenotanalyzed(beyondrunningtheclassifiers)orstoredforthisstudy.

3.2Results

SurveyResults

WesurveyedChatGPTusersfromourtwocohortsinmid-November2024ontheirexperienceswithChatGPT.Wereceived4,076responses,2,333ofwhichwerecompletedbycontrolusersand1,743frompowerusers(Appendix

B.5

).

Overall,wefoundthatsmalldifferencesexistedbetweenresponsesinourcontrolvspowerusercohorts,althoughgenerallythetrendsarebroadlysimilar,asshowninFigure

4

.ThecontrolusersreportedthattheyreliedonChatGPTforknowledge-seekingtasksandcasualconversationsslightlymorethanpowerusers.BothcohortsacknowledgeChatGPT’ssupportincopingwithdifficultsituations,thoughpowerusersdemonstratemarginallyhigherrelianceforsuchtasks.Bothgroupsappearedtobesensitivetochangesinthemodel,suchasvoiceorpersonality,withpowerusersdisplayingslightlyhigherlevelsofdistressfromchange.PoweruserswereslightlymorelikelythancontroluserstoconsiderChatGPTa“friend”andtofinditmorecomfortablethanface-to-faceinteractions,thoughtheseviewsremainaminorityinbothgroups.

Wehighlightthattheresultsofsurveyscanbesubjecttoissuesofselectionbias,asusershadtovoluntarilyfilloutthesurveyweprovide.

9

Affi

Pl

rt

ActivationRate

Desirefor

Feelings(U)

4.5%

3.0%

1.5%

0.0%

Control

Users

Power

Users

ectonate

Language(U)

18.0%

12.0%

6.0%

0.0%

Control

Users

Power

Users

30.0%

20.0%

10.0%

0.0%

Questions(A)

Control

Users

Power

Users

ersona

2.4%

1.6%

0.8%

0.0%

Demands(A)

Control

Users

Power

Users

3.0%

1.5%

0.0%

PetName(A)

Control

Users

Power

Users

SeekingSuppo(U)

15.0%

10.0%

5.0%

0.0%

Control

Users

Power

Users

Figure5:Meanofasubsetoftheclassifierscoresbyusercohort.Classificationisperformedattheindividualconversationlevel,andstatisticsarecomputedwithineachcohort.Activationisgenerallyhigheragainstpowerusersacrossallclassifiers.ResultsforallclassifiersareshowninAppendix

B.5

.

ConversationAnalysis

InFigure

5

,wecomparetheoverallclassifieractivationratesbetweencontrolandpowerUserpopulations,forarepresentativesubsetofEmoClassifiersV1.TheresultsforthefullsetofclassifierscanbefoundinAppendix

B.5

.WefindthatpowerUserstendtoactivatetheclassifiersmoreoftenthancontrolUsersacrossallofourclassifiers.Forsomeclassifiers,powerUsersmayactivatetheclassifiermorethantwiceasoftenascontrolUsers,suchasforthe‘PetName’classifier,orthe‘ExpressionofDesire’and