Between conjecture and memento: shaping a collective emotional perception of the future

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by  Alberto Pepe

Between conjecture and memento:
shaping a collective emotional perception of the future
AlbertoPepe JohanBollen
GraduateSchoolofInformation DigitalLibraryResearchandPrototyping
UniversityofCalifonia,LosAngeles LosAlamosNationalLaboratory
[email protected] [email protected]
Abstract thisfield.ResearchersattheInformationandLanguagePro-
8
0 cessing Systems group at the Informatics Institute of the
0 Largescalesurveysofpublicmoodarecostlyandof- University of Amsterdam have used blog data to deduct
tenimpracticaltoperform. However,thewebisawash
2 trends and seasonality (Balog & De Rijke 2006), forecast
withmaterialindicativeofpublicmoodsuchasblogs,
mood (Mishne & De Rijke 2006), and predict movie sales
n emails,andwebqueries. Inexpensivecontentanalysis
(Mishne&Glance2006). Investigatingasimilaronlinecor-
a on such extensive corpora can be used to assess pub-
J licmoodfluctuations. Theworkpresentedhereiscon- pus, Mihalcea & Liu (2006) have performed a “linguistic
5 cernedwiththeanalysisofthepublicmoodtowardsthe ethnography” to find out where happiness lies in everyday
2 future.UsinganextensionoftheProfileofMoodStates lives. Somesimilaranalyticaltoolsfunctionentirelyonthe
questionnaire,wehaveextractedmoodindicatorsfrom web:WeFeelFine(http://www.wefeelfine.org/)

] 10,741emailssubmittedin2006tofutureme.org,aweb constantly harvests blog posts for occurrences of the
L servicethatallowsitsuserstosendthemselvesemails phrases ”I feel” and ”I am feeling” and matches it to a
C tobedeliveredatalaterdate.Ourresultsindicatelong- pre-identified database of feelings; similarly, Moodviews
. termoptimismtowardthefuture,butmedium-termap- (http://moodviews.com) constantly tracks a stream
s prehensionandconfusion.
c of Livejournal weblogs that are user-annotated with pre-
[ defined moods. The results generated via the analysis of

IntroductionandScope such collective mood aggregators are compelling and indi-
1
cate that accurate public mood indicators can be extracted
v
Theongoingpopularizationandgrowingavailabilityofthe
from online materials. However, such indicators are lim-
4
Internet has led, in recent years, to a sharp increase in the
6 ited to near-present observations due to their source mate-
amount of personal, social and emotional information that
8 rial, i.e.moodindicatorsextractedfrompresentlyavailable
ispubliclyandopenlyshared. Manifestationsofmoodcan
3 material pertain to past and present moods. They need to
. be found in personal blogs, online diaries, social network- bestatisticallyextrapolatedtoassessfuturepublicmoodor
1
ing websites, message boards and chat rooms. Many of
sentiment.
0
these online services offer ad hoc tools to let their users
8 In this paper, we investigate the content of emails sub-
annotate their writings or webpages with a description of
0 mitted by anonymous users to the futureme web site
their current mood. For example, popular services such as
: (http://www.futureme.org). The futureme service
v Livejournal (http://www.livejournal.com/), an
allows users to compose email messages to themselves for
i online blogging software and community, and myspace
X future delivery; emails are held on the server for a certain
(http://www.myspace.com/), a social networking
r time (from 1 month up to 30 years in the future) and then
a website, allow users to add a mood indicator to their blog
sent to the specified recipients at the specified dates. The
entriesandtheirpersonalprofiles,respectively.
content of futureme emails is thus explicitly directed to-
Besides user-contributed mood descriptions, online tex-
wards a future date. In fact, the most common use of this
tual content of various kinds can be aggregated and ana-
serviceisforuserstoaddresstheirfutureselvesintheform
lyzed to infer public mood levels. Natural language pro-
ofapersonalmementoorconjectureabouttheirfuture,e.g.
cessing,textminingandmachinelearningcanbeemployed
”I am sure your AAAI paper was accepted for publication
to assess the mood and opinions of groups of users in
andyouarenowhappilymarried.”Ananalysisofthemood
an automated fashion. Such analytic methods, often re-
oftheseemailswillthusyieldadirect,ratherthanextrapo-
ferredtoassentimentanalysis(Nasukawa&Yi2003), by-
lated,assessmentofpublicmoodtowardthefuture.
pass the costs of repeated, large-scale public surveys and
Futuremecurrentlyhostsnearly500,000emailstothefu-
mayyieldaccurateresultswhenusedonextensivecorpora.
ture. Although a user might send more than one email, it
There exists a considerable amount of experimentation in
isreasonabletoestimatethatthetotalpopulationofthefu-
Copyright(cid:13)c 2008,AssociationfortheAdvancementofArtificial turemecommunityisintherangeoffewhundredthousands.
Intelligence(www.aaai.org).Allrightsreserved. Itspolicyoflimitlessaccessandthebreadthofattentionthat
futureme has recently received from the press (Moehringer wellastheaveragelagbetweenoriginanddestinationyear
2007; Associated Press 2005) has contributed to highly di- areshowninFigure1.
versify its user community. Nevertheless, the findings pre-
sentedinthispaperarebasedonasampleofthepopulation
thatislimitedtothecommunityoffuturemeusers,i.e.those
thatsubmittedtheiremailstothefuturemewebsite.
Although the size and characteristics of the futureme 4000
community are unknown due its focus on user anonymity,
it clearly abides by some of the key criteria required by 3000
tahned”pwreisddioctmionofmcraorkwedts” p(Shuernoowmieecnkaia2s0o0b4s)e.rvIendpinarfitincaunlacre, emails 2000
the futureme crowd manifests: a) independence (users are
notinfluencedbyotheruserswhilecomposingtheiremails), 1000
b) decentralization (futureme is an online, thus distributed,
community),andfinallyc)diversityofopinion(userswrite 0 2006
privatelytothemselves). 2005 2010 2015 2020 2025 2030 2035
Insteadofrelyingonuser-providedmoodannotationsand destination year
ad hoc models of mood states, we used a popular psycho-
metricinstrumenttoextractmoodindicatorsfromthebody Figure 1: Email delivery date distribution for the period
offuturemeemailssubmittedin2006,namelyanexpanded 2006to2036
versionoftheProfileofMoodStatesquestionnaire(POMS)
(McNair,Loor,&Droppleman1971).Moodindicatorswere
Thegraphshowsasharpdecreaseinthenumberofemails
thusextractedfrom10,741emailssubmittedtofuturemein
senttothedistantfuture. Itisverylikelythatthisfindingre-
2006andaggregatedtoquerythe“wise”futuremecrowdto
flects public pessimism about the durability of present-day
computecollectivesentimenttowardsforthcomingyears.
email addresses, as well as services and technology. Yet,
Theremainderofthepaperisorganizedasfollows. Inthe abasicwordoccurrenceanalysisoftheemailcorpusunder
nextsectionwediscussthedatacollectionprocess,thecon- studyrevealsthattheaggregatedemailcontentisoverallop-
tentofthedataandsomestatisticalfactsaboutthedataem- timistic. Table1displaysthe20mostfrequenttermsusedin
ployed in the study. In the following section, we introduce theemailswrittenin2006.
themethodology,inparticularwithrespecttotheadaptation
ofthePOMStesttoextensiveemailcorpora. Inthelasttwo count word count word
sections,wepresentanddiscussourresults.
21373 dear 8074 right
21318 hope 7931 future
Datacollectionandprocessing. 15589 love 7184 really
12747 know 6625 things
Thedatausedinthestudywasobtainedfromthearchivesof
11438 life 6103 better
futureme, a web service that allows its users to send them-
10960 time 5974 think
selves emails to be delivered at a later date, up to 30 years
9075 happy 5299 email
in the future. Upon submission of the email, users can de-
8669 remember 5290 past
cide whether to make the content of their email publicly
8643 good 4880 birthday
available. Subsequently, futureme publishes the following
8401 year 4679 today
information for public emails without revealing the user’s
identity: a) date of email dispatch (when the message was
written),b)textualcontentofemailandc)intendeddateof Table 1: Twenty most recurrent terms in emails composed
delivery(whenthemessageisduetoarrive). in2006,sortedbyoccurrence
Futuremehasexperiencedsignificantgrowthinthe2004
to 2007 period. Whereas in the early years of the service Using a combination of manual and automated proce-
(2002), no more than 500 to 1000 public messages were dures, we identified and removed emails written (a) in lan-
sent at a yearly basis, this number peaked at about 70,000 guagesotherthanEnglishand(b)inunknowncharacteren-
in2006. Atthetimethisanalysiswasconducted,complete codings.Beforeproceedingwiththecontentanalysis,wevi-
2007 data was not yet available although extrapolation in- suallyinspectedtheemailcorpustoqualitativelydetermine
dicatesacontinuedyetlesshyperexponentialgrowthofthe whetherwecoulddetectsimilaritiesorclearpatternsinthe
service. For this reason, only messages submitted in 2006 the nature of the messages. We could identify two broad
wereselectedforfurtheranalysis. Allpublicemailswritten categories:
between January 1 and December 31, 2006 were harvested
• conjectures: forecasts and predictions about the future,
from the futureme archive and stored in a format suitable
especiallywithrespecttopersonallifeaspects
fortextualanalysis. Atotalof10,741emailswerecollected
for the year 2006, with delivery dates ranging from 2006 • mementos: purposefulremindersandrecollectionsabout
to2036. Thedistributionsofemailsperdestinationyearas personaleventsthatmighthavebeenforgottenwithtime
As the study presented here involves a large scale mood WeextendedtheintialsetofPOMS’s65moodadjectives
analysis,thedichotomypresentedaboveraisedanimportant by793synonymsextractedfromthePrincetonUniversity’s
issue: does an email sent to the future convey the mood of WordNet (version 3.0) and Roget’s New Millennium The-
the user at the time of writing, or the expected perception saurus(FirstEdition).Forexample,theoriginalPOMSterm
of mood toward the delivery date? Clearly, emails to the discouragedwasassignedasetofextendedtermsasfollows:
future may contain conjectures or mementos and thus con-
discouraged,beatdown,cavedin,crestfallen,daunted,de-
veymoodstatesrelatingtoeithertheemailcompositionor
terred, dispirited, downbeat, downcast, glum, lost mo-
delivery date. The distinction between a “conjecture” and
mentum,pessimistic,putoff
a “memento” email is sometimes too subtle to be detected
via manual inspection, let alone via automated techniques. Assuch,eachoriginalPOMSmoodadjective,referredto
To overcome this obstacle, we dropped this dichotomy in as”main”,wasnowassociatedwithasetofsynonymswhich
favor of a more holistic approach. In particular, we treated werefertoas”extended”. Wewillrefertotheresultingex-
theemailsas“confessional”timecapsules:messageswhose tended version of the POMS test, as POMS-l. All POMS-l
contentdoesnotnecessarilyreflectpresentnorfuturemood mood adjectives, both main and extended, were stemmed
states, but rather a combination of the two. The results of to a common lexical root using the Porter stemmer (Porter
the study presented here must therefore be interpreted in 1980) so that they would match different versions of the
thislight. Themoodoftheemailsisintendedastheuser’s sametermoccurringinafuturemeemails. Forexample,the
present mood state toward the future, similar to how the email term ”angrily” whose Porter stem is ”angri” would
yieldcurveineconomicsreflectspresentexpectationsoffu- match the POMS adjective ”angry” since the latter would
tureyields(Rebonatoetal. 2005). alsobePorter-stemmedtotheircommonroot”angri”.
To assess the mood of a futureme email, its content was
Methodology matched against the POMS-l’s main and its associated ex-
tended set of mood adjectives. If an email term matched
There exist numerous psychometric instruments to assess
either a main term or its extended set of mood adjectives
individual mood states and monitor their fluctuations over
thisleadtothecorrespondingmainterm’sscoretobeincre-
time. The Profile of Mood States (POMS) questionnaire
mented by 1. For example, if an email contained the term
(McNair, Loor, & Droppleman 1971) in particular has
”daunted”,thistermmatchwouldoccurwithintheextended
enjoyed widespread adoption. POMS measures six di-
setforthemainterm”discouraged”. Consequently,therate
mensions of mood, namely tension-anxiety, depression-
of the POMS-l mood adjective ”discouraged” would be in-
dejection,anger-hostility,vigor-activity,fatigue-inertia,and
crementedby1. Followingthisprocedure, thecontentofa
confusion-bewilderment. It does so by having respondents
particularfuturemeemailcouldbemappedtoasetofscores
indicateonafive-pointintensityscalehowwelleachoneof
for each of the original POMS mood adjectives, as if a hu-
the65POMSadjectivesdescribestheirpresentmoodstate.
mansubjecthadindividuallyratedeachofthem. Theresult-
The respondent’s ratings for each mood adjective are then
ing scores were then converted to a six-dimensional mood
transformed by means of a scoring key to a 6-dimensional
vector. Thisvectorwasnormalizedtounit-length.
mood vector. For example, the user-indicated ratings for
As a result, each email was mapped to a normalized
terms ”angry”, ”peeved”, ”grouchy”, ”spiteful”, and ”an-
six-dimensional POMS mood vector representing its level
noyed”allcontributetotherespondent’sscoreonthePOMS
of tension, depression, anger, vigor, fatigue and confusion.
”anger”scale.ThePOMSisaneasy-to-use,low-costinstru-
These mood vectors were grouped according to the deliv-
ment whose factor-analytical structure has been repeatedly
ery date of the original email, resulting in a set of mood
validatedandrecreated(Norcross,Guadagnoli,&Prochaska
statevectorsforeachyearbetween2006and2036. Atwo-
2006). It has been used in hundreds of studies since its in-
sided Kolmogorov-Smirnov test of significance was then
ception (McNair, Heuchert, & Shilony 2003) and has been
conductedbetweenthesetsofmoodvectorsforeachpairof
normedforawidevarietyofpopulations.
yearsbetween2006and2036.Statisticallysignificantmood
To reduce the time and effort on the part of human
changesbetweenanytwoparticularyears(p < 0.05)could
subjects to complete the POMS, modifications have been
thusbedetected.
proposed that have a reduced number of mood adjectives
The trend lines for each of the mood dimensions were
(Shacham1983;Cheung&Lam2005). Forthepurposesof
plotted over time. Mean mood scores were converted to z-
thisstudy, however, weseektomodifythePOMStoapply
scoresaccordingtothetime-seriesmeanandstandarddevi-
to open-ended textual material, i.e. the futureme collection
ation to establish an equal scaling for all mood dimension
ofemails. Sincethevocabularyofthefuturemeemailscan-
trend lines and to be able to interpret deviations from the
notbecontrolled,thePOMSwasthenexpandedbeyondits
temporalmean. A2-degreepolynomialsmoothingwasap-
original set of 65 mood adjectives to match the numerous
pliedtotherawdatatoelucidatelong-termtrends.
synonyms and wordings that users may choose to express
theirmood.Forexample,whereastheoriginalPOMSwould
Results
requirehumansubjectstoratetheterm”peeved”,theauthor
of a futureme email could have used the term ”mad”. The All measured dimensions indicated mood fluctuations over
occurrenceoftheterm”mad”neverthelessservesasanindi- time,however,statisticallysignificantdifferencesatthesig-
cationoftheuser’sangerlevel. Itwasthereforemappedto nificance level (p < 0.05) were found in the trend lines of
thePOMSmoodadjective”angry”. onlytwoscales,namelydepressionandvigor.
Fatigue, tension and anger. These three mood dimen- Confusion. Theconfusiontrendlines(Figure3)showed
sionsdonotdisplaymajorfluctuationsovertime(Figure2). onlytwomarginallysignificantdifferencesinthenearterm.
Somespikescanbeobservedinallthreedatasets,butsince In particular, confusion scores trended downward between
theyarenotstatisticallysignificant,theyhavebeenexcluded 2009and2011(p=0.088),furtherconfirmedbyanoverlap-
frompresentdiscussion. pingdecreasebetween2009and2016(p=0.055).
+3sd
+2sd
confusion (z!score) 0+1sd
! 2011 *
!1sd ! 2016 *
!2sd
2010 2015 2020 2025 2030 2035
year
(a) Fatigue
Figure3: Measuredfluctuationofmoodindicatorconfusion
(*p<0.1)
Depression. We observed a statistically significant de-
crease in depression (Figure 4) for the near future, namely
the period 2007 to 2012 (p=0.035), followed by a sharp
uptick between 2012 and 2018 (p=0.025). Afterward, the
depression profile trends downward, however the observed
decline between 2018 and 2031 is only marginally signifi-
cant(p=0.090).
( 201) ’’
(b) Tension
(1s+
depression (z!score) !1s+0 ! 2012 ’’
!2s+
! 2031 ’
2010 2015 2020 2025 2030 2035
year
Figure 4: Measured fluctuation of mood indicator depres-
sion(*p<0.1,**p<0.05)
(c) Anger
Vigor. Vigor (Figure 5) follows, as expected, an in-
Figure 2: Measured fluctuation of mood indicators fatigue,
versepatterntodepression. Ahighlysignificantincreasein
tensionandanger.
vigorscoreswasobservedbetweentheperiod2007to2016
(p=0.0042) and a marginally significant decrease between the period between 2012 and 2018. The ”vigor” indicator
2016and2026(p=0.085). seemstofollowaninversepatternwherevigorincreasesin
theshorttermbutdecreasesinthelongterm.
Thepublicmoodof30,000futuremeusersthusindicates
amixedpicture. Thecollectivemoodofthisusercrowdto-
+3sd wards the near term future is optimistic, whereas the long
term picture is one that seems to indicate a moderate in-
crease in ”depression” and a moderate decrease in ”vigor”.
+2sd Thiseffectcouldbetiedtotheintrinsicallypersonalandinti-
matenatureofthefuturememessages. Whereasindividuals
vigor (z−score) +1sd + 2016 ** fitnheeetlihrienownceonanlrtorfnouglt-uatrneerd,mothppetriyomsmpiseatciyctseaaxbnpodeurttihethenecwieroparlpedrp,sroiennhaeglnepsnireoornasplo.evcetsr
0
Theseresultspointtothepossibilityofextendingatried
and true psychometric instrument such as the POMS to as-
−1sd − 2026 * sess the public mood from plubic collections of messages
directedtowardsfuturedates. Moreeffortsneedtobededi-
catedtofurthervalidatingtheproposedPOMS-landincreas-
2010 2015 2020 2025 2030 2035
year ingitssensitivity. Thiswouldhowevernecessitateeitherthe
availabilityofmoreextensivecollectionssuchastheentire
Figure 5: Measured fluctuation of mood indicator vigor (* futuremebodyofmessages,oravalidationeffortinvolving
p<0.1,**p<0.05) traditional psychometric evaluation measures. Another in-
teresting venue of research would be the validation of our
results to other ”wisdom of crowds” indicators such as the
yieldcurveineconomicsandlarge-scalepublicsurveys.
Conclusions
Whathavewelearnedfromtheanalysisofasetofpersonal Acknowledgments
emails intended to be sent to the future? No statistically WewouldliketothankJayPatrikiosandMattSlyforletting
significant mood trends have been detected for the fatigue, us harvest and use the futureme.org public email archive.
tension and anger dimensions of the POMS. This could be Also, many thanks to Jean-Franc¸ois Blanchette and Mark
attributedtoeitherthefailureofthePOMS-ltodetectsuch HansenofUniversityofCalifornia,LosAngelesfortheirad-
changes or to the stability of these mood indicators over viceandassistancethroughouttheearlyphaseofthiswork.
time. Itcouldinfactbearguedthatmooddimensionssuch
as”fatigue”arenotappropriateindicatorsofpublicmoodto- References
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