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–IndependentWorkReportSpring,2017–

HowDoPeopleUseFacebook?A“Comment”OnModernSocialMedia

Interaction

JelaniDenisAdviser: ArvindNarayanan

Abstract

Thegoalofthisresearchpaperistobetterunderstandhowsocialmediauseimpacts

people’sthoughts,emotions,andexpressions,particularlyamongstyoungadults.By

aggregatingdatafromseveralsocialmediaaccounts,andperformingcategoricalanalyses

onthedata,thispaperaimstodrawinferencesaboutthreethings:howpeopleusesocial

mediatoexpressthemselves,howpeopleconsumecontentfromsocialmediaplatforms,

andwhateffecttheseonlineinteractionsmighthaveonthementalandemotionalstateof

thoseengaged.

1.Introduction

Sinceitscreationinthelate90’s,theInternethasgrownexponentially,andithas

changedpeople’slivesdramatically.Inparticular,theongoinguseofonlinesocialmedia

accounts has become a nearly ubiquitous phenomenon among people living in urban

areas anddeveloped countries. The interactionbetweenhumansonline is a relatively

newfrontierandourexistingsocietalnormsandinstitutionallawsareracingtocatchup

withthefastgrowthofthistechnologicalera.

This projects aims to understand the complex relationship between people and

their social media accounts with regard to social norms and reinforcement learning.

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Specifically,thispaperoutlinestheanalysisofFacebookposthistoriestakenfromasmall

sampleofparticipantswiththegoalofsheddinglightontheimpactofsocialmediaonthe

humanexperience,andviceversa.Thefindingsfromthispapercanandshouldbeused

as fertile grounds for establishing debate and further research on the topic, since it

introducesvaluableandrelevantideasconcerningthesocialmediaspaceespeciallyasit

relatestoFacebook.

Since the motivation for this project was to better understand and explore the

relationship between people and social media, there weremultiple platforms through

whichwecouldhaveanalyzedthesephenomena.Twitter,LinkedIn,Reddit,Google+,and

evenYouTubeareallopenandavailablespacesthat involvesocial interactionbetween

people throughonlineaccounts. However,access todata fromactualusers foreachof

theseplatformsislimitedtovaryingdegrees. TwitterandFacebookbothofferpublicly

availableRESTAPIstoreadandwritedatafromtheirnetworks,butwechooseFacebook

becauseofthegreatercomplexityofuser-userinteractionanduser-producedcontent.

OnFacebook,userscancreateadetailedprofilewithpersonalinformation,create

andsharepages,groups,andevents,andinteractwiththeNewsfeed.Ultimatelyitisthe

user timeline where we decided to focus all of our effort, namely because it can be

centrallyaccessedbytheFacebookGraphAPI,permissionswithstanding,andbecauseit

is a living and growing historical record of socialmedia interaction for any particular

userovertime.

Ultimately completing thisproject camedown to twophases:data collectionand

dataanalysis. Theformerinvolvedthecreationofawebsitetohostauthenticateusers

andretrievetheirdatatostoreinabackend.Thelatterinvolvedcategoricalanalysesand

graphcreationtovisualizethedataandreasonaboutitintelligently.

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2.ProblemBackgroundandRelatedWork

Theimpactofsocialmediaonthehumanexperiencehasbeenexplored,notably,by

Facebookitselfina2012studyinwhichthesocialmediagianthadreportedly

“manipulatedthefeedsofoverhalfamillionrandomlyselecteduserstochangethe

numberofpositiveandnegativepoststheysaw”[3].Thepurposeofthestudywasto

observethepropagation,ifany,ofemotionoverasocialnetwork.Overall689,003

randomlyselecteduserswereselectedforthe“experiment,”andindeedthestudyfound

thatusersexposedtomorepositivecontentmademorepositiveposts,andthoseexposed

tomorenegativecontentmademorenegativeposts.Facebookpublishedtheirfindingsin

anacademicpaperentitled“ExperimentalEvidenceofMassive-ScaleEmotionalContagion

ThroughSocialNetworks”,inwhichresearchersAdamKramer,JamieGuillory,andJeffrey

Hancockwrotearguedthat“in-personinteractionandnon-verbalcuesarenotstrictly

necessaryforemotionalcontagion,andthattheobservationofothers’positive

experiencesconstitutesapositiveexperienceforpeople”[4].

Facebookasafluidmediumforemotionalcontagionisaprimeexampleofsocial

media’simpactonthehumanemotionalandsocialexperience.Itleavesmanyopen-ended

questionsthatmypaperwilltrytoaddress,suchasthefollowing:

• Whatdoessomeone'sbehaviorinanonlinesocialmediaenvironmentsay

aboutherrealvalues,thoughts,orfeelings?

• Howdoesbeingaparticipantintheseenvironmentsaffectone’semotional

ormentalwellbeing?

• Whatcanpatternsofonlineactivitytellusaboutsocialdevelopmentand

potentiallyreinforcementlearning?

AnarticlebyJuliaCottle,Ph.D.formentalhelp.netasksthequestionplainly:“Is

socialmediahurtingorhelping?”[2].Accordingtothearticletherearepotentialbenefits

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tosocialengagementusageincluding“reducinganxietyanddepression,numerousmutual

supportgroupsforpeoplestrugglingwithdifficultcircumstances,andstress-reduced

socializing”[2].Howeverthearticlealsowarnsagainstthepotentialdownsides,which

include“depressioniftheusermakesnegativecomparisonsbetweenherselfandothers,”

and“eatingdisordercausedbybodyexposureonFacebook”[2].Cottlealsowriteshow

the“limitednumberoflikesorviewscanbediscouraging”andcanexacerbateexisting

self-esteemconflicts[2].

Alongthesamevein,IgorPantic,M.D.PhD,citesinhispaperfortheJournalof

Cyberpsychology,Behavior,andNetworkingastudyatastateuniversityinUtah“carried

outon425undergraduatestudents…thatreportedthatFacebookuseislinkedto

participants'impressionthatotherusersarehappier,aswellasthefeelingthat“lifeisnot

fair.”[6].Ironically,interactingwithFacebookinevitablyallowsusersampleopportunity

toscrutinizetheirownsocialmedia“image”andaccordingto“objectiveself-awareness

theory”thisprolongedscrutinyislikelytoresultina“diminishedimpressionofself”[6].

Clearlysocialmediaasaplatformandmediumforhumaninteractionhasaplethora

ofconsequencesthatarelargelycase-dependent,butwecancertainlyuseempiricaldata

frominstitutionslikeFacebooktobegintogleanmeaningfultheoriesregardingthe

social/emotionalexperienceofusersonsocialmedia.

3.Approach

Our approach is to collect Facebook post histories of willing participants, and

perform categorical analysis to unearth behavioral trends and pick out statistically

notablefeaturesofusagethatcanbereasonedabouttosaysomethingmeaningfulwith

respecttotheabovequestions.

We will generate visual graphs from each participants data, and analyze and

comparethesegraphstosaysomethingsubstantiveaboutthesocialmedia’sinfluenceon

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people’s values, thoughts, and emotions, and conversely, how socialmedia activity can

shedlightonthegrowthanddevelopmenttheseinternalattributes.

For example: one of our data extraction techniques looks at the types of posts a

usermakesover time.Thegoal is toexpose thepossibility thatpeoplemightalter the

waytheypresentthemselvesonlineinordertofita"model"or"image"thatispositively

receivedandreinforcedwithintheirfriendshipnetworks.

4.Implementation4.1FacebookGraphAPI/JavascriptSDK

In order to retrieve the Facebook post histories of participating users, we

implemented a website integrating the Facebook login button to authenticate and

requestpermissionfromusers. Thewebpage’sJavascriptusestheFacebookLoginSDK

for control flow to store a temporary access token upon user login that enables the

webpagetomakeiterativesynchronoushttprequeststotheFacebookGraphAPI,which

returns post histories in a JSON format. The website can still be accessed at

https://fbemotion.club.

4.2FirebaseBackend

All data is stored in a secure, Google cloud hosted database known as Firebase.

This database is NoSQL, and information is stored in a JSON collection/document

structure. Since AJAX calls to the Graph API can parse responses as JSON, a NoSQL

backendseemednatural. AlsoFirebasehasaveryeasytouse JavascriptSDK,andalso

allowedustoimplementsomeimportantsecurityprotocolsaswediscussbelow.

4.3Collection

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Uponsuccessfulcompletionofdatacollection,alltheuserspostsareprintedoutin

anice,easytoreadformatinatable.Thisisdonemainlytoprovidetransparencytothe

datacollectionprocess,andmostparticipantswereinterestedinseeingwhattheirpost

historieslookedlike.Thecollectionprocesstakessometime,andisvariabledepending

onthenumberofpostsauserhasmade,thelengthofthoseposts,andthemagnitudeof

responses. Instructions on the webpage therefore instruct participants to wait on

average10minutesfordatacollection,andthealertsnotifytheuserofwhentheprocess

isbeginsandwhenithassuccessfullycompleted.

4.4SecurityMeasures

Thereare fourcomponentsof thewebapp thatarepotential targets forattackerswho

eitherwanttointercept/readparticipant’sdataorcompromisemyprojectbytampering

the data. We detail these four component below and themeasureswe have taken to

mitigatethem.

1.FacebookAuthentication:

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The login button is a direct plugin from Facebook and cannot be

compromisedfrommywebpagedirectly. Thatmeansnooneelsecanlogin

asauserunlesstheyhaveFacebookcredentials. (Thebuttonissecuredby

Facebook,whichimplementsindustry-gradesecurityprotocols.)

The onlypossible risk of someone stealinguser credentials is bymaking a

phishingattempt.Thiswouldentailsomeonebuildingawebpagethatlooks

exactlylikefbemotion.clubandre-creatingtheFacebookloginandpop-ups

tocoaxauserintogivinguphercreds.

2.FacebookGraphAPIcalls:

TechnicallysomeonecouldrewritethecallstotheGraphAPIsinceitisclient

facingJavascript,butunlesstheycompromisetheFacebookloginwhichwe

havealreadynotedisquitesecure,thentheycannotusetheAPItoaccessthe

informationofanystudyparticipants. Therefore, thiswebpage’suseof the

Graph API poses nomore threat to the privacy of study participants than

theywerealreadyexposedtobetheexistenceofthefreeandavailableGraph

APIexplorer(https://developers.facebook.com/tools/explorer/).

3.FirebaseAuthentication:

We used the Firebase web SDK to write authentication code to sign in

participants via email and unique password when they get to the landing

pageforourwebsite.Thisauthenticationenablesthewebpagetoposttheir

datatothebackendsecurelyonceitisretrieved. Toaddanewparticipant,

firstwemanually add a new user/password pair to the Firebase database

console.Thenwegenerateasecurepasswordandemailthepasswordtothe

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participant.Thenweaskthatparticipantsvisitthewebsitewithin24hours

tosubmittheirdata.

All form credentials are sent via http post to a PHP page for processing,

whichupon completion, renders thenextpage forFacebook login, so as to

prevent anyone on the client side from recording that authentication

information.

Participantsareautomaticallysignedoutaftertheirdatahasbeenuploaded,

orelsewearenotifiedviaemailthattheyhavenotbeensignedoutsowecan

dealwiththeproblemmanually.

4.FirebaseAPIcalls:

We have set authorization rules so the data can never be read via the

Firebase API from any remote source. Period. Security measures to

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guaranteethisrestwithFirebaseitself.Inordertoaccessthedata,therefore,

we must sign in to Firebase and make a download locally to our secure

personallaptop.AlldataistobedeletedfromFirebaseandfromthelaptop

uponsubmissionofthisresearchpaper.

In addition, we carefully restrict who is allowed to write anything to the

backend.ThroughFirebasesecuritysettings,wehavesettheonlyaccepted

http refererrer to our proprietary domain (fbemotion.club). No other

websitecanmakehttprequeststothedatabasethatway.

4.5Disclaimer

Finally, the followingdisclaimer ispostedon thewebsite forusers to readbefore they

agreetosharingtheirdata:

ThisappwilldisplayyourFacebookposthistory.Itwillremoveany

andallnamesfromthedata(thatincludesyourname)toprotectthe

privacyofyourselfandyourfriends.OnlytheFacebookgeneratedid

stringswillbekept,andthesearerandomlygeneratedforeach

applicationthatusesFacebookGraphAPI.

Thedatawillbestoredinacloud-hosteddatabasemanagedby

Google.Thesecurityrulesofthedatabasearesetsothatnoonecan

readfromitremotely.Period.AlsoIhavetakenextraprecautionsto

makesurethisdomain(fbemotion.club)istheonlyonethatcanissue

httprequeststothedatabase.

YoucanexpectyourdatatobedeletedentirelyafterMay5th,2017.

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4.6HTTPS

Ontopoftheproceduresdescribedinsection4.4,wehavealsopurchasedanSSL

CertificatefromGoDaddytoenableHTTPSonourwebsite,sothatalltrafficgoingtoand

fromtheserverisencrypted.Certificatesareusedbythebrowsertobothauthenticatea

server to an end host (client) and encrypt data flowing to and from the server. Only

trusted Certificate Authorities can issue signatures to validate certificates, and these

certificatesareused toencrypta randomkey thatwill serveas thebasis for symmetric

encryption during an HTTPS connection. Following a handshake, the server proves its

identitybyprovidingthecertificate,andthenthekeyexchangeoccursbetweenserverand

client.

5.Evaluation

Havingcollectedalargeamountofdatafromeachof10youngadultsonFacebook,

wedecidedtobreakaparttheanalysis intoseparateframesofreference,orcategorical

perspectives.Inpickingapartthedata,wedeterminedthatcertainmetricsconcerninga

user’sposthistorycouldbestatisticallyanalyzedandvisuallyportrayedtoreasonabout

thatuser’sonlinesocialexperience.Inaddition,trendsforthatuser’sposthistoryreflect

not only the opinions and emotional state of that user, but of that user’s audience, or

friendnetwork,tosomedegree.

Therefore,we approached the analysis through six different attribute categories:

MessageLength,ListenerRank,Story,Time,Emotion,andTopic. Messagelengthrefers

tothelengthofapost’smessage,ifitcontainsone.Listenerrankcorrespondstotherank

ofauser’sfriendwithrespecttoallthatuser’sotherfriendsonthesocialnetwork,where

higherrank isallocated to those friendswhoreactorcomment themost to thatuser’s

posts on the newsfeed. Story refers to the story type of the post, which has several

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differentversionsaswewillsoonexplore.Timehastodowiththetimeausermakesa

post,andevaluatedoverthecourseofauser’sposthistory,timecanpotentiallyspeakto

a user’s changing values anddesire to garnermore “likes”. Emotion refers to a post’s

emotional stateaseither fear, anger, sadness, surprise,or joy. In conjunctionwith the

othercategoricallenses,emotionprovidesauniqueinsightintoauser’smentalstateand

potentialthoughts.Lastly,topicreferstothesubjectmatterofapost,andplottingtrends

inanetwork’sreactionstopostsacrossvarioustopicscanhelpusunderstandwhichof

thosetopicspeoplecareabout,andwhichelicitsspecifictypesofresponses.

5.1Length

First,letusexaminethechartsthatweredevelopedfromuserdatawithspecialattention

tothelengthofpostmessages.ThesecorrespondtoFigures1through5intheAppendix.

Figure1wascreatedwithmatplotlib’spyplotandnumpy’spolyfitonpythonbyfinding

thebestlinearfittothescatterplotofmessagelengthovertimeforeachuser,andthen

averagingthecoefficientsofeachlinetogiveonewithanaverageslopeacrossalldata.

The resulting line clearly shows an upward trend, although the Pearson correlation

coefficientacrossalldataisweak.(Weacceptthislineasagoodmodelforrepresenting

trendsinthedata).Butwhatdoesthistrendactuallymean?Forourpurposes,itcould

suggestthatonaverage,theparticipantswhoprovideddataforthisstudytendedtopost

longermessages the longer they had active Facebook accounts. This could imply that

theysimplyhadmoretosayastheiraccountsmatured,butitismorelikelythatthisisan

indicationthattheyderivedmoreutilityfromtheirsocialmediaactivityovertime.

Naturally, the next step of analysis is to ascertain what are the effects of this

messagelengthincreaseovertime?Figures1through4plotthenumberofreactionsand

comments, separately, for all data thatwas collected,with respect to the length of the

postmessage.Thesentimentanalysistoolfromindico.iowasusedtoclassifypostsbased

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onwhetherornotthathadanoverallnegative,oroverallpositivesentiment. Threeof

theplotsshowageneralupwardtrendindicatingthat longermessages,onaverage,get

morereactionsandcommentsfromauser’ssocialnetwork. However, ineachcasethe

trendislesspronouncedforauser’spostsclassifiedas“negative”.Infactthelineofbest

fit is slightly downward sloping for the case of reactions to longer, negative sentiment

messages. Again, this is aggregatedata from thepost histories of all participantswho

volunteeredtheirdata,sothesetrendssaysomethingmeaningful.

The takeaway here is that it seems, at least for the social network populations

surrounding our participants, that Facebook users respond more to longer messages

thanshorterones,andmoresotopositivemessagesthannegativeones. Moreover,the

message length of a post ismore strongly correlatedwith the number of comments it

receives than the number of reactions it receives. This suggests that if friends in the

networkdecidetoconsumea longmessageoffofthenewsfeed,theyaremorelikelyto

respondwithacomment,whichstimulatesaconversation.Effectively,theeffortonthe

part of the principal user to express herself, is reciprocated by those friends in the

networkwhorespondwithwordsandnotreactions.Theimagebeingpaintedhereputs

socialmediainamorepositivelight,whereinuserswhodevotetimetocomposingand

releasinglongpostmessagesaregivenaproportionalamountoffeedbackandattention

fromtheirsocialnetwork.Thisisapositivefeedbackloop,andmighthelpexplainwhy,

inFigure1,wesawthatparticipantscreatedlongermessagesovertimethelongerthey

held an active Facebook account. One point of further exploration for this particular

category of data would be to map out the correlation between negative and positive

posts,andtheircounterpartreactionsorcomments.Thequestioniswhethernegativeor

positivepostselicitnegativeorpositivesentimentfromthesocialnetworkinrealtime,

andthisquestionhasprofoundimplicationstousersofsocialmedia,andrelatesbackto

the Facebook 2014 empirical study in Related Work that resolved in a positive

correlationbetweenthetwofactorsathand.

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5.2Story

Nowweturnourattentiontotheattributeofpost‘story’andhowfilteringonthis

attributecanshedlightonauser’svalues,opinions,andperceptionofherselfandothers

especiallyinsidehercompletesocialnetwork.

Figure6showstheaveragedistributionofpostsbystorytypeacrossallparticipant

data for this study. Almost immediately, the most startling piece of the pie is that

relatively large chunk that goes towards “Profile” posts. Indeed, these posts make a

surprisingly large appearance in the chart, which reveals that a large portion (nearly a

third)ofparticipantFacebookactivitythatcanbetaggedwithastoryinvolvestheprofile

andmostoftentheprofilepicture.

Changingorupdatingtheprofilepictureismorecommonthanotheractivities,asis

sharingorpostingphotos,asthiscategoryisalsoslightlylargelythateachoftherest.The

othercategoriesasfairlyevenlydistributedacrosstheremainingportionsofthepie.

Figure7,therefore,mightnotbeasshockingtoanobservergivenFigure6,butit

still shows a rather noteworthy domination of “Profile” post in both the comment and

reaction spaces. This figure plots the average number of comments and reactions per

story type of post, and it is clear that “Profile” posts control the majority of response

activityaveragingjustabout55reactionsperpostand8commentsperpost.

What is the significance of the Facebook “Profile” to regular users and to their

audiences?Typicallytheprofilepictureisthe“face”ofsomeone’sonlineaccount,sinceit

appearsunderyournamewhenFacebooksuggestsyouasafriend,rendersyourpagetoa

sitevisitor,anditappearsineverysingleoneofauser’spoststohertimelineintheupper

left hand corner. Because the profile picture gets so much traffic, and is viewed so

frequentlywithin a user’s social network, itmakes sense that a usermight put special

attention into crafting a good profile pic. However, it does not explainwhy setting the

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profilepicturedominatesFacebookactivity.Thereareplentyofotheravenuestoengage

withthesocialnetwork,includingsharinginterestingcontentlikearticles,links,orvideos

andpostingmessagesconveyingideasorfeelings.Theremustbeareason,then,whythe

mostactivityonFacebookwithinthisdatasetisthe“profile”post.

We cannot make broad assumptions considering the small sample size of our

participant pool, but considering the secondmost popular story category re-affirms an

initial hunch. Indeed, the second most popular type of post among our dataset is the

“Activities”story. Activitiesaredefinedbyactionsandusually involvetaggingofoneor

moreotherfriendsinthesocialnetwork.Forexample,userAmightpostthatsheisdoing

“X”activity“AT”aparticularlocation.Thesearetypicallyusedtoannounceauser’ssocial

activities,andlike“Profile”posts,theycontributetotheoutwardfacingimageoftheuser

tothesocialnetwork.This“outwardfacingimage”couldpotentiallybeabigdriverbehind

people’susageofsocialnetworks,asitislinkedtotheirdesiretocontrolthewayothers

perceivethem.Thisdesire,whilecompletelynatural,canhaveadverseeffectsonauser’s

mental health since it involves public scrutiny and judgment that can be detrimental to

self-esteemifappearancesarenotgivenpositivefeedbackbythesocialnetwork.

Howdoagents in thesocialnetworkrespond, therefore, to “appearance” centric

posts by the principal user? Figures 8 and 9 explore that topic. Figure 8 shows the

relative percentages of reaction type amongst responses to posts categorized by their

“story” type. The figure is not very exciting, since it is dominated by “Like” activity.

Indeed,the“Like”buttonwasthefirstreactiontoexistonFacebook,anditismosteasily

selected(theothersrequireanextendedclick),sothereisnosurprisehere.Whatismore

interesting is the relative proportion of non-“Like” reactions that each story type

generates. “Media” stories, for example, generate the widest variety of reactions in the

largestproportion.Whatisitaboutvideos,images,andnewscontentthatcausethemost

colorful rangeof reactions fromasocialnetwork? Also, it isworthnoting that theonly

categorywithamorethaninsignificantportionof“Love”reactionsisthe“Ideas”category.

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Ifyourecall,“Ideas”areclassifiedaswhenausersharesapage,link,orpost,indicatinga

desiretospreadsometypeofdigestiblecontenttotheirnetworks.Wewouldliketothink

that these results are an indication of stronger agent approval and interaction of

substantiveuser-producedcontent,aswassuggestedbythepositivecorrelationbetween

messagelengthandresponsecountasdiscussedintheprevioussection.

Figure9, conversely, shows thedistributionof comments topostscategorizedby

storytype.Interestinglyenough,the“Ideas”categoryhasthelargestportionofcomments

classifiedasexpressing“fear”bytheindico.iotrainedemotiontextAPItool. Takinginto

accountthelimitationsofthis5-categoryemotionscheme,wewouldliketoentertainthe

ideathat“Ideas”basedpostsgeneratethemost“eye-opening”or“surprising”reactionson

Facebook, whereas posts like “Photos” and “Media” are dominated by simply “happy”

reactions.Thedataagainsupportsourcontinuedthemeofuserexpressionandnetwork

digestion, wherein the user puts enough effort into formulating a post or sharing a

thoughtful idea, so that her network consumes it and reciprocates that effort. The

distributionofcommentsversusstorytypeinFigure9underscoresthistrend.

Giventhatthestorytypeofauser’spostseemstorelatetotheamountoftrafficand

attentionthatthepostreceivesfromthesocialnetwork,itisnowofinteresttodetermine

ifusers themselvesactivelymonitor thesecorrelationsandadapt theircontent tobetter

craftan“outwardappearance”totheironlineaudience.Figure10showstheanalysisthat

is the first step inexploring thispossibility. ThisFigureplots, foranarbitraryuser, the

cumulativesumofpostsofaparticularstorytypeovertime.Theplotclearlyshowshowa

user’spostdistributionchangesovertime.Forthisarbitraryuser,“Media”postsdominate

his/her early activity on Facebook,while “Photos” soon taken over followedby “Ideas”.

The fact that each story type accumulates is trivial, but the relative rates atwhich they

accumulate,andsustaineddifferencesintheseratesovertime,telluswhenausermight

beactivelychangingthetypeofpoststheirdistribute.

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Tomakegeneralizableinferences,weexaminethecumulativepostcountperstory

typeoverallusersbycomputingthe4thdegreepolynomialthatbestfitsthesecurvesfor

eachuser(usingnumpy’splotfit)andthenaveragetheweights(coefficients)togenerate

trendlinesthatconveythebulkofthedata. Figure11showshowrunningplotfitonthe

scatterplotforasingleusergeneratessmoothapproximationlineswithintheintervalof

interest for each story category. To average over all users however, we implement a

techniquewherebywe firstsqueeze thedata foreachuserhorizontally to theminimum

posthistorylengthsothatallthedataliesonthesametimeinterval.Thenwecomputethe

polynomial of best fit by individually computing for each user, and averaging the

coefficients fora single story type. Figure12shows theprocess for “Media” story type.

Theresultingtrendlinewellapproximateseachusers’data.

Figure 13 shows the final result of this iterative fitting, transformation and

combining procedure, whereby the posting trends for all participants in the study are

summarized for each story type over a common time interval. The results are quite

interesting.The“Profiles”trendlineistheoutlierhere,increasingwithgreaterspeedand

magnitude than any other cumulative trend line. This would suggest that the average

participantprioritizes“Profile”typepostsearlyonintheirFacebookcareers,and“Profile”

postscontinuetodominatetheiractivitythroughouttheiractiveaccountlifecycles.

The other trend lines are closer together and therefore a bit harder to say

definitivelywhichgrowslargerthantherest.However,itisatleastvisuallyapparentthat

the“Photos”trendlinestartsoffrelativelyweak,thenoutstripstheothers,incontrastto

the“Media”and“Ideas”trend-linesthatstartout increasingfasterbutbegintoevenout

their slope towards the end of the time interval. This phenomenon introduces an

interestingquestionaboutoutsmallparticipantpool:Whydo“Media”and“Ideas”become

less importantover time,whenpostsof these type start offwithmoremomentum, and

“Photos”and“Activities”becomemoreimportant?Thedatasuggeststhatuserscaremore

about spreading ideas and expressing what is important to them at the start of their

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Facebook careers, but “learn” over time to prioritize posts that help to maintain their

“outwardappearance”onthesocialnetwork. Asstated inprevioussections, “Activities”

and“Photos”tendtoannounceauser’srecentsocialengagements.Prioritizingtheseposts

couldmeanthatusersarebeinginfluencedbytheirsocialmedianetworkstofocusmore

ontheiroutwardappearanceandsocialstatus,andlessontheirpersonalideas,emotions,

oropinions.

5.3Time

The previous section’s discussion of the “Story” category brought to the fore the

potentialthatsocialmediausagehasoninfluencingauser’sprioritiesofpersonalbeliefs.

The trend seems to be more towards “conformity” and maintenance of an “outward

facingpublic image”within thenetwork. Other attributesof post activity support this

idea,includingthetimeatwhichuserslearntomaketheirposts.

Figure14showsthedistributionofnetworkresponses(commentsandreactons)

topostswithrespecttothetimeofdaytheirwerecreated,downtothehalfhour. The

Figure was created for an arbitrary user. The distribution appears Gaussian at first

glance,butuponcloserscrutinythereappeartobedefinitepeaksandvalleys,whichisto

say,multiple timesofdayatwhichposts receivegreater thanaverage responses. The

next step for our analysiswas to determine if users actively adjust the times atwhich

they post, once they learn that a time is “peak” for response activity. If the trend is

confirmed, itwould add to our growinghypothesis that Facebookusers learn to value

attentionandapproval fromtheirsocialnetworksover time. Theneed togetasmany

“likes” and “comments” as possible could drive users to alter their posting habits and

thereforetheirself-expression,inordertoreapthebenefitsofasocialnetworkfollowing.

Figures15and16plotthedistributionofresponsestopostsmadeovertimefora

particularuser.Theresponsesarecolor-codedtoindicatepoststhatweremadeatpeak

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timeversuspoststhatwenotmadeapeaktime. Thefactthatpostsmadeapeaktime

garner the most responses is trivial and a result of their construction. However, the

frequencyatwhichauserpostsduringpeakhoursovertime isapotential indicatorof

theiractiveself-monitoringanduseofthesocialnetworktodrivetheirpostingbehavior.

Figure17showsasimplemovingaveragethatplots,foranarbitraryuser,thefrequency

(asapercentage)ofpeak-timepostsoverafixedwindowsizeovertime. Forthisuser,

thetrendispositive,butwealsocreateFigure18todisplayallparticipants’information

inasinglegraph.Thelineofbestfitalsohaspositiveslope.Figure18,therefore,shows

that users in our study did indeed post during peak times more frequently as their

Facebookaccountsmatured.Whetherornotthisisanindicationofanactiveadjustment

in response to network feedback cannot be definitively ascertained, but it is a likely a

possibleexplanation.

5.4Emotion

Theprevioussectionsdetailhowcategoricalanalysisof thesampleposthistories

contribute to a hypothesis of “social media influence” whereby a user learns to adjust

his/herpostingactivityinordertogainmoreattentionandmorepositiveattentionfrom

theirsocialnetworks. Ratherthanservingasaplatformforself-expression,whichcould

be therapeutic, therefore, social media accounts might be reinforcing negative social

normsorstereotypesbyaugmenting“mobmentality”amongstitsusers.Eachuserstrives

togaintheapprovalandadmirationofhersocialnetwork,andyeteachuserisalsoapart

of the network that defines social norms. The result of this interaction can be a fluid

ideologicalspace,wherebyusersandtheirnetworksareinaconstantebbandflowwith

regardtothevaluesandopinionsthataretoutedonedayversusanother.

Thispushandpullbetweenauserandhernetworkbegsthequestion:whichone

influences the othermore? To answer this question, we take the lens of a categorical

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analysisbasedontheemotionalcontentofauser’sposts. Figure19,forexample,shows

theaverageemotionaldistributionofpostsoverallusersintheparticipantdataset.For

poststhatcontainmessages,indico.io’ssentimentanalyzerfindsthattheemotionof“joy”

ismorefrequentthan“fear”,“anger”,“sadness”or“surprise”.Notsurprisingly,Figure20

reveals that postswhosemessage are “joyful” garner on averagemore than double the

reactionsofanyotheremotionaltype. Sothetakeawayhere isthatusers inoursample

makemorepositiveor“joyful”postsandtheirsocialnetworksrespondinlargernumbers

tothesetypesofposts.Butwhichendpointisdrivingwhich?Dousersmakehappyposts

becausethenetworkprefersthem,ordoesthenetworkpreferhappypostsbecauseusers

makethemmorefrequently?Figure21,whichshowstheaverageemotionaldistribution

ofcommentsovertheemotiontypeofpostsdoesnothelpusanswerthatquestionmuch,

even though it was intended to. The comment distribution is, curiously, largely

uncorrelatedwiththeemotionalcontentofposts.Thatistosaybasedonthefigureitdoes

not seem that a user’s “sad” posts generate any more “sad” comments than a user’s

“surprised”posts,orlikewiseforanyotherpairofemotions.Wethinkthishasmoretodo

withthelimitationsoftheindico.iotextualanalyzer,whichwastrainedonalargecorpus

ofdatabutnotspecificallyFacebookdata.Facebookcommentscanbenotoriouslyshort,

andwithoutalargefeaturevectorindico’stextanalyzermightjustendupmakingroughly

equalratesofclassificationforeachemotionaltype.

5.5Topic

Figures22and23complementFigures20and21 in that theyplot theemotional

distribution of comments and reactions. However, the distribution is plotted over post

topicandnotpostemotion.Thisprovidesadifferentanglethroughwhichtoapproachthe

questionof“pushandpull”betweenauserandaudience.Indico.io’s“text-tags”analyzer

takesapieceof textas inputandreturnsadictionarymappingof111possibletopicsto

20

the relative likelihood that they pertain to the submitted text. We used this tool to

determine the topic of each post in the dataset, and mapped them to the respective

averagecountofreactionorcomment.

Figure22isnotableinthatthetopicof“school”completelydominatesthereaction

spaceincomparisontoothertopics.Becauseourparticipantsareallattendinguniversity,

wedonotfindthisobservationabnormal. Othertopicsthatcommandagoodportionof

the reaction space include “parenting”, “gaming”, ”nutrition”, “dieting”, and “personal”.

Thesetopicsdonotseemtoberelatedtoanysignificantdegree,asidefromthefactthat

they seem to be reasonable topics one might expect college-age students to discuss

amongst their friends. Peculiarly, Figure 23 shows how topics like “parenting” and

“dieting”havearelativelyhigherpercentageof“joyful”commentsincomparisontoother

topics,whichdoesnotappeartofollowcommonsense.Someofthedistributionsdoseem

to follow logically from the topic of the post, however. For instance, the topic of

“nostalgia” is fairly popular and has a reasonably higher proportion of “sad” comments

that other topics. Figure 23, therefore, does suggest to some degree that user content

drivesresponsesfromthesocialnetwork,buttheresultsarenotfullytenable.

5.6Rank

Finally, we introduce the category of “rank” as our final attribute for analyzing post

histories in the context of social, mental, and emotional expression by users in the

network.Weattributeranktothe“audience”ofeachsocialnetworkinourdataset.That

is to say we rank each member of each user’s audience based on how actively they

respondtoauser’sposts. Figure24showstherankdistributionof“reactors”whomake

reactions to posts, and Figure 25 shows the distribution for “commenters” who make

commentstoposts.

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Immediately it jumps out at us that the plot looks remarkably like a negative

exponentialfunction.Thisrepresentationimpliesthatoutofthoseagentswhorespondto

auser’scontentwithinthesocialnetwork,thereisadiminishingmarginofresponserate

foragentsthatrespondlessand less frequently. Inotherwords,onlyasmallportionof

the total number of agents who respond to a user’s content do so frequently. This

“ranking” allows us to separate a user’s audience into “active” listeners and “passive”

listeners. Dividingalongtheattributeofrankthenallowsustogleannewinsights from

theattributeswehavealreadyconsideredsuchasemotionandtopic.

Figures26and27presentaratheroppositepicturetowhatonemightexpectwith

regardtotheactivityof“highranking”listenersversus“lowranking”listeners.Figure26

shows that as far as comments are concerned, peoplewho actively respond to a user’s

posts actually do not generate the majority of comments those posts. Most of active

responder activity is concentrated in the reaction space, as displayed by Figure 27.

Notably,therelativenumberofreactionspertopicdoesnotchangemuchforhighranking

responders vs low ranking ones (orange and blue make up roughly 50% each of each

column), which ironically suggests that people who engage frequently with a user’s

contentarenotanymoreorlessscrupulousthanthosewhohardlyengageatall.

6.Summary

Thispaper’sgoalwastobetterunderstandhowsocialmediauseimpactspeople’s

thoughts,emotions,andexpressions,particularlyamongstyoungadults.Itapproachedthis

aimthroughtheuseofstatisticalandcategoricalanalysesoncollectedFacebookpost

historiesusingpythonlibrariesandtheindico.iotext-analysisAPI.Byconsidering

differentattributesofaFacebookpost,thispaperwalkedthroughvariousplotstoreason

aboutthesocialmediaenvironmentandtheagentswithinit.

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Plottingaverageresponserateagainstthelengthofpostmessagesrevealedhow

longermessagesattractmoreattention,andallowedustobegintomodelthenewsfeedasa

two-wayconversation,wherecontent-creators(users)putinefforttoexpressthemselves,

andcontent-consumers(network)respondwithcommentsandreactionsaccordingly.

Breakingdowntheposthistoriesbystorytyperevealeddifferencesintheposting

frequenciesof“Profiles”and“Activities”whichincreasedovertime,incontrastto“Ideas”

and“Media”whichdecreasedovertime.Theseobservationsledustoformulatea

hypothesisaboutsocialmediainfluenceand“mobmentality,”asanexplanationtowhy

usersmightlearntovaluetheiroutwardappearanceandsocialstatusmorethantheideas

andopinionsthatsharedfrequentlyasearlyFacebookusers.

Extractingpeakpostingtimesforeachuser,anddeterminingtheoverallupward

trendinpeakpostingactivityfurthersupportedourdevelopingtheoryof“learnedsocial

norms”bythenetwork.Moreover,analysisofpostsonthelevelofemotionaldistributions

gavewaytoimportantquestionsconcerningthe“pushandpull”ofusersandtheir

audiencesinthetesttodefinesocialnormsintheonlinemedium.Ananalysisofpostsby

topicsuggested,toasmalldegree,thatusersgeneratingcontentdeterminewhat

consumers(therestofthenetwork)seeandfeelatanygiventimescrollingthroughthe

newsfeed.

7.HonorCodeI pledgemy honor that I have not violated the Honor Code during thewriting of this

paper./s/JelaniDenis

8.References

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