Community Question Answering Platforms vs. Twitter for Predicting Characteristics of Urban Neighbourhoods

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by  Marzieh Saeidi

Community Question Answering Platforms vs. Twitter for Predicting
Characteristics of Urban Neighbourhoods
MarziehSaeidi LiciaCapra
UniversityCollegeLondon UniversityCollegeLondon
[email protected] [email protected]
AlessandroVenerandi SebastianRiedel
UniversityCollegeLondon UniversityCollegeLondon
[email protected] [email protected]
7 Abstract Predicting demographics of individual users using their
1 languageonsocialmediaplatforms,especiallyTwitter,has
0 Inthispaper,weinvestigatewhethertextfromaCommunity been the focus of many research works: text from blogs
2 Question Answering (QA) platform can be used to predict and on-line forum posts are utilised to predict user’s age
anddescribereal-worldattributes.Weexperimentwithpre-
n throughtheanalysisoflinguisticfeatures.Resultsshowthat
dictingawiderangeof62demographicattributesforneigh-
a the age of users can be predicted where the predicted and
J bourhoodsofLondon.WeusethetextfromQAplatformof
observed values reach a Pearson correlation coefficient of
Yahoo!Answersandcompareourresultstotheonesobtained
7 almost 0.7. Sociolinguistic associations using geo-tagged
fromTwittermicroblogs.Outcomesshowthatthecorrelation
1 betweenthepredicteddemographicattributesusingtextfrom Twitter data have been discovered (Eisenstein, Smith, and

Yahoo!Answersdiscussionsandtheobserveddemographic Xing2011)andtheresultsindicatethatthedemographicin-
]
L attributescanreachanaveragePearsoncorrelationcoefficient formationofuserssuchasfirstlanguage,race,andethnicity
ofρ=0.54,slightlyhigherthanthepredictionsobtainedus- can be predicted by using text from Twitter with a correla-
C
ingTwitterdata.Ourqualitativeanalysisindicatesthatthere tionupto0.3.Otherresearchshowsthatusers’incomecan
s. issemanticrelatednessbetweenthehighestcorrelatedterms alsobepredictedusingtweetswithagoodpredictionaccu-
c extracted from both datasets and their relative demographic
racy(Preo¸tiuc-Pietro,Lampos,andAletras2015).Textfrom
[ attributes.Furthermore,thecorrelationshighlightthediffer-
Twitter microblogs has also been used to discover the rela-
entnaturesoftheinformationcontainedinYahoo!Answers
1 tionbetweenthelanguageofusersandthedeprivationindex
andTwitter.Whiletheformerseemstoofferamoreencyclo-
v ofneighbourhoods.Thecollectivesentimentextractedfrom
pediccontent,thelatterprovidesinformationrelatedtocur-
3 rentsocioculturalaspects. thetweetsofusershasbeenshown(Querciaetal.2012)to
5 havesignificantcorrelation(0.35)withthedeprivationindex
6
ofthecommunitiestheusersbelongto.
4
Introduction Data generated on QA platforms have not been used in
0
the past for predicting real-world attributes. Most research
. Recent years have seen a huge boom in the number of dif-
1 workthatutiliseQAdataaimtoincreasetheperformanceof
0 ferentsocialmediaplatformsavailabletousers.Peopleare
suchplatformsinanalysingquestionquality(Lietal.2012),
7 increasinglyusingtheseplatformstovoicetheiropinionsor
predictingthebestanswers(Liu,Liu,andYang2010;Tian
1 letothersknowabouttheirwhereaboutsandactivities.Each
etal.2013)orthebestresponder(Zhaoetal.2012).
: oftheseplatformshasitsowncharacteristicsandisusedfor
v differentpurposes.Theavailabilityofahugeamountofdata Inthispaper,weusethetextfromthediscussionsonthe
i
X from many social media platforms has inspired researchers QA platform of Yahoo! Answers about neighbourhoods of
r tostudytherelationbetweenthedatageneratedthroughthe LondontoshowthattheQAtextcanbeusedtopredictthe
a useoftheseplatformsandreal-worldattributes. demographicattributesofthepopulationofthoseneighbour-
hoods. We compare the performance of Yahoo! Answers
Manyrecentstudiesinthisfieldareparticularlyinspired
data to the performance of data from Twitter, a platform
bytheavailabilityoftext-basedsocialmediaplatformssuch
thathasbeenwidelyusedforpredictingmanyreal-worldat-
as blogs and Twitter. Text from Twitter microblogs, in par-
tributes.Unlikemanycurrentworksthatfocusonpredicting
ticular, has been widely used as data source to make pre-
one or few selected attributes (e.g. deprivation, race or in-
dictionsinmanydomains.Forexample,box-officerevenues
come)usingsocialmediadata,westudyawiderangeof62
arepredictedusingtextfromTwitter(Asur,Huberman,and
demographicattributes.Furthermore,wetestwhetherterms
others 2010). Twitter data has also been used to find cor-
extractedfrombothYahoo!AnswersandTwitterareseman-
relations between the mood stated in tweets and the value
tically related to these attributes and provide examples of
ofDowJonesIndustrialAverage(DJIA)(Bollen,Mao,and
sociocultural profiles of neighbourhoods through the inter-
Zeng2011).
pretationofthecoefficientsofthepredictivemodels.
Copyright(cid:13)c 2017,AssociationfortheAdvancementofArtificial Thecontributionsofthispapercanbesummarisedasfol-
Intelligence(www.aaai.org).Allrightsreserved. lows:
Table1:ExamplesofYahoo!AnswersdiscussionsandTwittermicroblogsaboutneighbourhoodsthatcontaintheterm“Jewish”.
Yahoo!Answers
Q:Wherecanifindajewishshopinlondon?
A:ThemainJewishCommunitiesinLondonareStamfordHillandGoldersGreen,plusHendonandEdgeware.Allhavemany
KosherandJudaicastoresontheirhighstreets.
Q:JewishneighborhoodsinLondon?
A:ThelargestisinGantsHill.TheyarepredominantlyReformistJews.ThenyouhavethelargestHasidicJewishCommunity
inEuropeinStamfordHill.ThenthereisalargeOrthodoxJewishCommunityinHendon,andaround14%ofSwissCottage
isJewish.
Twitter
-MeanwhileinCamden.@JewishMuseumLondon[tweetedfromCamden]
-Challahmakesmehappy.Braided,proofedandeggwashed#Shabbatshalom#dough#sesame#Jewishfood... [tweetedfrom
EastFinchley]
- Jewish crouton crack. For when you just need that boost #osem #mondaymorning #whoneedschickensoup [tweeted from
GoldersGreen]
• We show that text from QA discussions can be used Yahoo! Answers vs. Twitter. When it comes to neigh-
to predict real-world attributes such as demographic at- bourhoods,Yahoo!Answershavebeenusedbymanyusers
tributes of the population of neighbourhoods with a per- toaskortoanswerquestionsaboutdifferentcharacteristics
formancecomparabletoTwitterdata. of many neighbourhoods. While people may not use Twit-
ter in the same way, they may log their tweets while be-
• Ouranalysishighlightsthedifferencesbetweendatafrom
ing in different neighbourhoods. In this paper, we investi-
aQAplatformandTwitter:whileQAdataoffersamore
gatetheextenttowhichthediscussiononYahoo!Answers
encyclopedic content, the latter provides information re-
platformsaboutneighbourhoodsandthemicroblogsthatare
latedtocurrentsocioculturalaspects.
logged from different neighbourhoods can reflect charac-
teristics of those neighbourhoods. Table 1 shows examples
Datasets of Yahoo! Answers discussions that contain the names of
London neighbourhoods and the term “Jewish”. The table
Yahoo! Answers . Community QA platforms help users alsoshowsexamplesoftweetsthathavebeenbloggedfrom
to obtain information from a community – a user can post London neighbourhoods (neighbourhood’s name in brack-
questionswhichmaythenbeansweredbyotherusers.Dis- ets) and contain the term “Jewish”. These examples show
cussionscanbeindepthandinlengthbuttheinteractionsare someofthedifferencesbetweenthediscussionsthatcanbe
notspontaneous.Thetimeframeinwhichuserstakepartin found on Yahoo! Answers and on Twitter. As we can see,
adiscussionthreadcanvaryfromonedaytoseveraldaysor theQAdiscussionsarefocusedonatopic,i.e.Jewishneigh-
eventomonths.Moreover,QAplatforms,unlikeTwitterand bourhoods in London. The answers provide explicit infor-
some of the other social media platforms, are not location- mation on neighbourhoods that have a high population of
based.Yahoo!AnswersisoneofthefewQAplatformsthat Jewish.Ontheotherhand,microblogsofTwitterdonotfo-
haveemergedinthepastdecade.DiscussionsinYahoo!An- cus on providing explicit information about the neighbour-
swers are not domain specific and can cover a broad range hoods. But they contain information on user’s activities or
oftopics. observations (e.g. Jewish Museum, Jewish food) while in
different locations. These activities can implicitly indicate
thatJewishcommunitiesinhibittheneighbourhoodsthatthe
userisbloggingfrom.
Twitter. Twitter is an on-line microblogging social net-
workwhereuserscanpostandreadshort140-charactermes-
sages. Twitter is used mostly to share views, opinions and PopulationDemographicData. Populationdemographic
news in real-time. Unlike QA platforms, Twitter is ubiqui- dataistakenfromtheUKcensusprovidedbytheOfficefor
tous. While some users use Twitter to get updates on the NationalStatistics.1 CensussurveysintheUKarerepeated
news and their social circle, many others use it as part of every10yearsandwerelastconductedin2011.Censusdata
theirdailyroutinetotalkabouttheirthoughts,whereabouts, is provided for specific geographical units that are created
activities or sometimes just to share what is going on in solely for the purpose of census data collection. These are
their lives. This can be because of the strong tie that there called Lower Layer Super Output areas (LSOAs) and are
exists between Twitter and smartphones which are nowa- identified through an alphanumeric ID. Greater London is
daysatthecentreofmanypeople’slives.Foralltheserea- dividedinto4,835LSOAs.Thesearenotnecessarilyequal
sons,hugeamountofdataisconstantlybeingcreatedonthis in size as they have been designed to have a population of
platform.Additionally,Twitterisalocation-basedplatform around 1,500. Census data provides statistical information
where people can tag their locations while blogging their
tweets. 1http://www.ons.gov.uk/
onawiderangeofcategoriessuchastheaveragehouseprice
Number of QAs per Area - London
inaLSOA,populationcount(orpercent)ofareligionoran 400
ethnicbackground,incomelevel,etc.Eachcategorycanbe
350
subdividedintofurtherattributes.Forinstance,thecategory
religion contains the attributes Muslim, Christian, Jewish, 300
Hindu,etc. eas250
Ar
Method er of 200
b
m
SpatialUnitofAnalysis Nu150
100
Thespatialunitofanalysischosenforthisworkistheneigh-
bourhood.Thisisidentifiedwithauniquename(e.g.,Cam- 50
den)andpeoplenormallyusethisnameinQAdiscussionsto
0
refer to specific neighbourhoods. A list of neighbourhoods 0 50 100 150 200
Number of QAs
for London is extracted from the GeoNames gazetteer2, a
dataset containing names of geographic places including Figure1:HistogramofthenumberofQAspereachLondon
placenames.Foreachneighbourhood,GeoNamesprovides neighbourhood.
its name and a set of geographic coordinates (i.e., latitude
andlongitude)whichroughlyrepresentsitscentre.Notethat
geographical boundaries are not provided. GeoNames con- a stemmer will transform the word “presumably” to “pre-
tains589neighbourhoodsthatfallwithintheboundariesof sum”and“provision”to“provis”.Tokeepthemostfrequent
the Greater London metropolitan area. In the remainder of words, we remove any token that has appeared less than 5
the paper, we use the terms “neighbourhood” or “area” to timesinlessthan5uniqueQAs.Thisleavesuswith8kdis-
refertoourspatialunitofanalysis. tincttokens.
Pre-processing,Filtering,andSpatialAggregation TwitterData. TocollectdatafromTwitter,weusethege-
ographical bounding box of London, defined by the north-
Yahoo!AnswersData. Wecollectquestionsandanswers
westandsoutheastpointsoftheGreaterLondonregion.We
(QAs)fromYahoo!AnswersusingitspublicAPI.3Foreach
thenusethisboundingboxtoobtainthetweetsthataregeo-
neighbourhood,thequeryconsistsofthenameoftheneigh-
tagged and are created within this box through the official
bourhoodtogetherwiththekeywords“London”and“area”. TwitterAPI.4 WestreamTwitterdatafor6monthsbetween
This is to prevent obtaining irrelevant QAs for ambiguous
December2015andJuly2016.Attheend,wehavearound
entitynamessuchasVictoria.Foreachneighbourhood,we
2,000,000tweetsinourdataset.
thentakealltheQAsthatarereturnedbytheAPI.EachQA
To assign tweets to different neighbourhoods, for each
consists of a title and a content which is an elaboration on
tweet, we calculate the distance between the location that
thetitle.Thisisfollowedbyanumberofanswers.Intotal,
it was blogged from and the centre points of all the neigh-
wecollect12,947QAsacrossallLondonneighbourhoods.
bourhoodsinourdataset.Notethatthecentrepointforeach
TheseQAsspanoverthelast5years.Itiscommonforusers
neighbourhoodisprovidedinthegazetteer.Wethenassign
to discuss characteristics of several neighbourhoods in the
the tweet to the closest neighbourhood that is not further
same QA thread. This means that the same QA can be as-
than 1 km from the tweet’s geolocation. At the end of this
signedtomorethanoneneighbourhood.Figure1showsthe
process,wehaveacollectionoftweetspereachneighbour-
histogram of the number of QAs for each neighbourhood.
hoodandwecombinethemtocreateasingledocument.Fig-
Asthefigureshows,themajorityofareashavelessthan100
ure2showsthenumberoftweetspereachneighbourhood.
QAs with some areas having less than 10. Only few areas
As we can see, the majority of neighbourhoods have less
haveover100QAs.
than1000tweets.
Foreachneighbourhood,wecreateonesingledocument
We remove all the target words (words starting with @)
by combining all the QA discussions that have been re-
from the documents. The pre-processing is then similar to
trieved using the name of such neighbourhood. This doc-
the QA documents. At the end of this process, we obtain
ument may or may not contain names of other neighbour-
17kdistinctfrequenttokensfortheTwittercorpus.
hoods. We split each document into sentences and remove
thoseneighbourhoodscontaininglessthan40sentences. Population Demographic Data. As we previously ex-
We then remove URLs from each document. The doc- plained, each attribute in census data is assigned to spatial
ument is then converted to tokens and stop words are unitscalledLSOAs.However,theseunitsdonotgeograph-
removed. All the tokens in all the documents are then icallymatchourunitsofanalysiswhicharetheneighbour-
stemmed. The goal of stemming is to reduce the different hoodsdefinedtroughthegazetteer.Amapshowingthespa-
grammaticalformsofawordtoacommonbaseform.Stem- tialmismatchispresentedinFigure3.Toaggregatethedata
ming is a special case of text normalisation. For example, containedintheLSOAsattheneighbourhoodlevel,weuse
thefollowingapproach.
2http://www.geonames.org/
3https://developer.yahoo.com/answers/ 4https://dev.twitter.com/streaming/
DocumentRepresentation
Number of Tweets per Area - London
45 Averypopularmethodforrepresentingadocumentusingits
40 wordsisthetf-idfapproach(Salton,Fox,andWu1983).Tf-
idfisshortfortermfrequency-inversedocumentfrequency
35
where tf indicates the frequency of a term in the document
eas30 andidfisafunctionofthenumberofdocumentsthataterms
of Ar25 has appeared in. In a tf-idf representation, the order of the
ber 20 wordsinthedocumentisnotpreserved.Foreachtermina
m
Nu15 document,thetf-idfvalueiscalculatedasbelow:
10
tf(d,t)
5 tf-idf(d,t)= (1)
log( Totalnumberofdocuments )
0 Numberofdocumentscontainingthetermt
0 5000 10000 15000 20000 25000 30000 35000 40000 45000
Number of Tweets To discount the bias for areas that have a high number of
Figure2:HistogramofthenumberoftweetspereachLon- QAsortweets,wenormalisetfvaluesbythelengthofeach
donneighbourhood. documentasbelow.Thelengthofadocumentisdefinedby
thenumberofitstokens(non-distinctivewords).
Often,whenpeopletalkaboutaneighbourhood,theyre- FrequencyofTermtinDocumentd
Normalisedtf(d,t)= (2)
fer to the area around its centre point. Therefore, the in- NumberofTokensinDocumentd
formation provided for neighbourhoods in QA discussions
Correlation
shouldbeveryrelatedtothisgeographicpoint.Tokeepthis
level of local information, for each demographic attribute, Toinvestigatetheextenttowhichthetextobtainedfromthe
we assign only the values of the nearby LSOAs to the re- twoplatformsofYahoo!AnswersandTwitterreflectthetrue
spective neighbourhood. To do this, we calculate the dis- attributes of neighbourhoods, we first study whether there
tance between each neighbourhood and all the LSOAs in aresignificant,strongandmeaningfulcorrelationsbetween
London.Thedistanceiscalculatedbetweenthecoordinates the terms present in each corpus and the many neighbour-
ofaneighbourhoodandthecoordinatesofeachLSOA’scen- hood attributes through the Pearson correlation coefficient
troid.Foreachneighbourhood,wethenselectthe10closest ρ. For each term in each corpus, we calculate the correla-
LSOAs that are not further than one kilometre away. The tion between the term and all the selected demographic at-
valueofeachdemographicattributeforeachneighbourhood tributes. To do so, for each term, we define a vector with
is then computed by averaging the values associated with thedimensionofthenumberofneighbourhoods.Thevalue
theLSOAsassignedtoit.Weapplythisproceduretoallthe of each cell in this vector represents the normalised tf-idf
demographicattributes. valueofthetermforthecorrespondingneighbourhood.For
eachdemographicattribute,wealsodefineavectorwiththe
dimensionofthenumberofneighbourhoods.Eachcellrep-
resentsthevalueforthedemographicattributeofthecorre-
spondingneighbourhood.WethencalculatethePearsoncor-
relationcoefficient(ρ)betweenthesetwovectorstomeasure
the strength of the association between each term and each
attribute.
Sinceweperformmanycorrelationtestssimultaneously,
weneedtocorrectthesignificancevalues(p-values)formul-
tipletesting.WedosobyimplementingtheBonferronicor-
rection,amultiple-comparisonp-valuecorrection,whichis
usedwhenseveraldependentorindependentstatisticaltests
arebeingperformedsimultaneously.TheBonferroniadjust-
ment ensures an upper bound for the probability of having
an erroneous significant result among all the tests. All the
p-values showed in this paper are adjusted through the use
oftheBonferronicorrection.
Prediction
We investigate how well the demographic attributes can be
Figure 3: The geographic relation between LSOAs and
predictedbyusingusingYahoo!AnsewrsandTwitterdata.
neighbourhoodsidentifiedthroughthegazetteer.LSOAsare
We define the task of predicting a continuous-valued de-
geographical shapes and their centroids are marked with
mographicattributeforunseenneighbourhoodsasaregres-
darkdots.Neighbourhoodsaremarkedwithgreencircles.
sion task given their normalised tf-idf document represen-
tation.Aseparateregressiontaskisdefinedforeachdemo- Correlation
graphic attribute. We choose linear regression for the pre- Number of Correlated Terms. The number of signifi-
dictiontasksasithasbeenwidelyusedforpredictionsfrom cantly correlated terms from both Yahoo! Answers and the
textintheliterature(FosterandStine;Joshietal.2010). Twitter with the selected demographic attributes are shown
Due to the high number of features (size of vocabulary) inTable2.Notethatthenumberofunique(frequent)words
and a small number of training points, over-fitting can oc- inTwitter(17k)isalmosttwiceasinYahoo!Answers(8k).
cur. To avoid this issue, we use elastic net regularisation, a Thefirstcolumnshowsademographicattributeandthesec-
technique that combines the regularisation of the ridge and ondcolumnindicatesthesource,i.e.Yahoo!Answers(Y!A
lasso regressions. The parameters θ are estimated by min- forshort)orTwitter.Thethirdcolumn(“All”)showstheto-
imisingthefollowinglossfunction.Here,yi isthevalueof tal number of terms that have a significant correlation with
anattributeforthei-thneighbourhood,vectorxiisitsdocu- each attribute (p-value < 0.01). The following columns
mentrepresentationandN isthenumberofneighbourhoods show the number of terms that have a significant correla-
inthetrainingset. tionwiththeattributewithaρinthegivenranges.Thelast
columnshowsthenumberoftermsthataresignificantlycor-
1 (cid:88)N relatedwiththeattributewithanegativeρ.Thedatasource
L= N (yi−xTi θ)2+λ1||θ||+λ2||θ||2 (3) that has the highest number of correlated terms with each
i=1 attributeishighlightedinbold.
Asthetableshows,termsextractedfromYahoo!Answers
Evaluation. To measure the performance of a regression
tendtobemorerelated,intermsofthenumberofcorrelated
model, residual-based methods such as mean squared er-
terms,toattributesrelatedtoreligionorethnicitycompared
ror are commonly used. Ranking metrics such as Pearson
totermsfromTwitter.However,fortwoparticularattributes
correlation coefficient have also been used in the litera-
(i.e., Price and Buddhist), the number of correlated terms
ture(Preo¸tiuc-Pietroetal.2015;Eisenstein,Smith,andXing
from Twitter is higher than the ones from Yahoo! Answers
2011).Usingarankingmeasurehassomeadvantagescom-
.Theseresultscollectivelysuggestthatthereisawealthof
paredtoaresidual-basedmeasure.First,rankingevaluation
terms, both in Yahoo! Answers and Twitter, which can be
is more robust against extreme outliers compared to an ad-
usedtopredictthepopulationdemographics.
ditive residual-based evaluation measure. Second, ranking
metricsaremoreinterpretablethanmeasuressuchasmean
Table 2: Number of significantly correlated terms (p-value
squarederror(Rosset,Perlich,andZadrozny2005).Wethus <0.01)frombothYahoo!Answers(“Y!A”)andTwitter.
usethismethodforevaluatingtheperformanceoftheregres-
sionmodelsinthiswork. Attribute Source All >0.4 [0.3,0.4] [0.2,0.3] <0
Asfurtherperformancecheck,weapplya10foldscross- Y!A 115 1 48 66 0
IMD
validationtoeachregressiontask.Ineachfold,weuse75% Twitter 17 0 10 7 0
of the data for training and the remaining 25% for valida- Y!A 50 2 36 12 0
Price
tion. At the end, we report the average performance over Twitter 1120 312 533 275 0
all folds together with the standard deviation. For each de- Jewish% Y!A 48 7 31 10 0
Twitter 6 0 5 1 0
mographic attribute, i.e. target value, training and valida-
Y!A 87 0 59 28 0
tion sets are sampled using Stratified Sampling. This is a Muslim%
Twitter 13 1 8 4 0
samplingmethodfromapopulation,whensub-populations
Y!A 8 2 3 3 0
within this population vary. For instance, in London, there Hindu%
Twitter 5 0 3 2 0
are areas with very high or very low deprivation. In these
Y!A 1 0 1 0 0
cases,itisadvantageoustosampleeachsub-populationin- Buddhist% Twitter 934 18 728 188 0
dependentlyandproportionallytoitssize. Y!A 114 4 59 51 0
Black%
Twitter 2 0 2 0 0
Y!A 8 0 0 0 8
Results White%
Twitter 0 0 0 0 0
Note. There are many attributes across several categories Asian% Y!A 6 0 3 3 0
Twitter 1 0 1 0 0
inthecensusdata.Becauseofspacelimitation,weconduct
mostofourexperimentsonaselectedsetofattributes.These
attributes are taken from religion (population of Jewish%, Semantic Relatedness. In this section, we observe
populationofMuslim%,populationofHindu%,population whether the correlations between terms and attributes are
ofBuddhist%),ethnicity(populationofBlack%,population semantically meaningful. Due to the limited space, we se-
ofWhite%,andpopulationofAsian%ethnicity),Price(av- lect three attributes and their relative top correlated terms
eragehouseprices)anddeprivation(IndexofMultipleDe- extracted from Yahoo! Answers (Table 3) and Twitter (Ta-
privation,IMD5).Forafullbreakdownofresultspleaserefer ble4).WechoosetheattributesPriceandIMDastheyshow
totheAppendix. thehighestnumberofcorrelatedtermsforbothsources.For
eachsource,wethenchooseonemoreattributethathasthe
5https://en.wikipedia.org/wiki/Multiple_ highest number of strongly correlated terms (ρ > 0.4), i.e.
deprivation_index Jewish%forYahoo!AnwsersandBuddhist%forTwitter.
Table3:TermsfromYahoo!AnswerswiththehighestPear- videgeographicalinformationonwheredeprivationismore
son correlation coefficients for the selected demographic concentratedinLondon(i.e.,EastEnd).Othertermsseemto
attributes. Correlations are statistically significant (p-value berelatedtothepresenceofyoungergenerationofcreatives
<0.001). andartistsinmoredeprivedneighbourhoods.“Yeah”,“shit”,
“ass”,mayallbejargonscommonlyusedbythissectionof
Jewish% (High)Price Deprivation population.“Studio”,“craftbeer”,“music”mayinsteadrefer
Term ρ Term ρ Term ρ
totheirmainactivitiesandoccupations.Forwhatconcerns
matzo 0.45 townhouse 0.4 hurt 0.4
(high)“Price”,allthetermsseemtorelatetoaspectsofex-
harmony 0.45 fortune 0.39 poverty 0.36
pensiveareas,e.g.“luxury”,“classy”,and“stylish”.“Tea”,
jewish 0.41 qatar 0.39 drug 0.36
“teatime”,“delight”,“truffle”seemtorelatetosocialactiv-
jew 0.41 diplomat 0.39 cockney 0.35
unfairly 0.42 exclusive 0.37 victim 0.35 ities of the upper class. For the attribute “Buddhist%”, five
flyover 0.41 hectic 0.36 mug 0.34 termsoutoftenare,inourview,associatedwithneighbour-
ark 0.38 desirable 0.35 trouble 0.34 hoods where the majority of people is Buddhist or practise
straw 0.38 celeb 0.34 notorious 0.34 Buddhism.Thesetermsseemstorelatetoaspectsofthisre-
arab 0.39 aristocratic 0.33 rundown 0.33 ligion,e.g.“think”,“learn”,“mind”,etc.
kosher 0.32 fashionable 0.32 slum 0.32 Interestingly, terms extracted from Yahoo! Answers and
Twitterseemtooffertwodifferentkindsofknowledge.On
Table 4: Terms from Twitter with the highest Pearson cor- oneside,termsextractedfromYahoo!Answersaremoreen-
relationcoefficientsfortheselecteddemographicattributes. cyclopedicastheytendtoofferdefinitionsorrenownedas-
Correlationsarestatisticallysignificant(p-value<0.001). pects for each attribute. “Jewish%” is, for example, related
toaspectsoftheJewishculturesuchas“matzo”,“harmony”,
Buddhist% (High)Price Deprivation and“kosher”.“Deprivation”isassociatedwithsocialissues
Term ρ Term ρ Term ρ such as “poverty” and “drug”, but also with a degraded ur-
think 0.44 luxury 0.66 east 0.39 ban environment (e.g., “rundown”, “slum”). On the other,
long 0.42 tea 0.64 eastlondon 0.36 Twitterwordsprovideakindofknowledgemorerelatedto
rainy 0.41 teatime 0.61 eastend 0.36
currentsocioculturalaspects.Thisisthecase,forexample,
learn 0.40 delight 0.60 yeah 0.33
of the jargon associated with “Deprivation” (e.g., “yeah”,
presentation 0.40 truffle 0.60 studio 0.33
“shit”), or of the culinary habits related to “High Prices”
mind 0.40 car 0.60 shit 0.32
(e.g.,“tea”,“truffle”).
para 0.40 classy 0.59 craftbeer 0.30
todo 0.40 stylish 0.59 ass 0.30
thing 0.40 gorgeous 0.59 music 0.30 Prediction
heart 0.40 interiordesign 0.58 neighbour 0.29 The results of the regression tasks performed over the se-
lectedsetofdemographicattributes,intermsofPearsoncor-
relationcoefficient(ρ),arepresentedinTable4.Resultsare
WefirstexamineTable3andprovideexamplesofseman- averagedover10foldsandstandarddeviationsaredisplayed
tic similarity between Yahoo! Answers terms and the se- inparenthesis.
lectedattributes.Wordshighlightedinboldare,inourview, Wecanseethatonaverage,performancesofYahoo!An-
theonesmostassociatedwiththeirrespectiveattribute.For swers and Twitter are very similarly with Yahoo! Answers
the attribute Deprivation, the majority of the terms seem havingaslightlyhigherperformance(4%).Twitterdatacan
to be linked to issues of deprived areas. “Poverty”, “drug”, predictthemajorityofthereligion-relatedattributeswitha
“victim”, all refer to social issues. “Rundown” and “slum” highercorrelationcoefficientwiththeexceptionofpopula-
may be associated with the degradation of the surround- tionofJewish%.Ontheotherhand,Yahoo!Answersissu-
ing environment. “Cockney” is a dialect traditionally spo- periortoTwitterwhenpredictingethnicityrelatedattributes
kenbyworkingclass,andthuslessadvantaged,Londoners. suchaspopulationofWhite%andBlack%.Wehaveseenin
Fortheattribute(High)Price,mosttermsseemtoberelated Table 2 that Twitter has very few correlated terms with the
toaspectsofplaceswhichmayoffermoreexpensivehous- attributesWhite(0)andBlack(2).
ing.Termssuchas“fortune”,“diplomat”,and“aristocratic” We also observe that IMD and Price can be predicted
are often associated with wealth. Others seem to reflect a with a high correlation coefficient using both Yahoo! An-
poshlifestyleandstatussymbol:“townhouse”,“exclusive”, swersandTwitter.Thiscanbeduetothefactthatthereare
“celeb”, “fashionable”, “desirable”. For the attribute Jew- many words in our dataset that can be related to the depri-
ish%,mostofthetermsseemtoreflectaspectsofthisreli- vationofaneighbourhoodortohowexpensiveaneighbour-
gion or be linguistically associated with it (i.e., “Jew” and hood is. This is also evident in Table 2 where the number
“Jewish”). “Matzo” and “Kosher” are associated with the of correlated terms from both Yahoo! Answers and Twitter
traditionalJewishcuisine;theformerisatypeofflat-bread, withtheseattributesareveryhigh.Ontheotherhand,terms
thelatterisawayofpreparingfood.The“ark”isaspecific thatdescribeareligionoranethnicityaremorespecificand
partofthesynagoguewhichcontainssacredtexts. lower in frequency. Therefore attributes that are related to
We now examine Table 4. For the attribute Deprivation, religionorethnicityarepredictedwithaloweraccuracy.
ninewordsoutoftenseemtobelinkedtomoredeprivedar- Table5furthershowstwotermsthathavethehighestco-
eas.“East”,“eastlondon”,and“eastend”,forexample,pro- efficients in the regressions models (across the majority of
Table 5: Prediction results in terms of ρ using Yahoo! Answers and Twitter data. Results are averaged over 10 folds and
standarddeviationsareshowninparenthesis.Correlationsarestatisticallysignificant(p-value<0.01).Termswiththehighest
coefficientsinregressionsmodelsarealsoprovided.
Yahoo!Answers Twitter
Attribute ρ Terms ρ Terms
Muslim% 0.51(0.07) asian,barber 0.54(0.05) mileend,eastlondon
Jewish% 0.42(0.08) jewish,arab 0.13(0.06) rsa,rugby
Hindu% 0.32(0.10) stadium,cemetery 0.46(0.09) smokeyeye,asianbride
Buddhist% 0.24(0.10) minister,tourist 0.44(0.07) theatre,prayforparis
Black% 0.60(0.07) gang,drug 0.44(0.08) southlondon,frank
Asian% 0.40(0.07) asian,barber 0.39(0.05) mileend,gymselfie
White% 0.58(0.06) essex,suburbia 0.45(0.08) essex,golf
HousePrice 0.69(0.05) money,compliment 0.68(0.04) dailyspecial,personaltrainer
IMD 0.69(0.03) notorious,cockney 0.56(0.04) np,eastlondon
folds) for each attribute and source in the column Terms. predicting attributes related to Ethnicity and Employment,
Thesetermsareamongthestrongpredictorsoftheirrespec- Twitter performs better when predicting attributes relating
tive attribute. Many of these terms appear to be related to totheAgeGroup,andCarOwnership.
the given demographic attribute (for both Twitter and Ya-
hoo! Answers ) and are also often amongst the top corre- RelatedWork
lated terms presented in Tables 3 and 4. We follow with
The availability of a huge amount of data from many so-
someexamples.Accordingtotheregressioncoefficientsfor
cial media platforms has inspired researchers to study the
theattributeMuslim%,neighbourhoodsinhabitedbyaMus-
relationbetweenthedataontheseplatformsandmanyreal-
lim majority may be located in Mile End, an East London
worldattributes.Twitterdata,inparticular,hasbeenwidely
district(i.e.,Twitterterms“mileend”and“eastlondon”),see
used as a social media source to make predictions in many
thepresenceofAsianpopulationandbarbershops(i.e.,Ya-
domains.Forexample,box-officerevenuesarepredictedus-
hoo! Answers terms “asian” and “barber”). According to
ingtextfromTwittermicroblogs(Asur,Huberman,andoth-
the terms for Black%, neighbourhoods with a black ma-
ers 2010). Prediction results have been predicted by per-
jority tend to be located in the southern part of London
forming content analysis on tweets (Tumasjan et al. 2010).
(i.e., Twitter term “southlondon”) and experience social is-
It is shown that correlations exist between mood states of
suessuchaspresenceofcriminalgroupsanddruguse(i.e.,
the collective tweets to the value of Dow Jones Industrial
Yahoo! Answers terms “gang” and “drug”). According to
Average(DJIA)(Bollen,Mao,andZeng2011).
the terms for IMD, more deprived areas seem to be lo-
Predicting demographics of individual users using their
cated in the East End of London (i.e., Twitter term “east-
languageonsocialmediaplatforms,especiallyTwitter,has
london”) where the Cockney dialect is dominant (i.e., Ya-
beenthefocusofmanyresearch.Textfromblogs,telephone
hoo!Answersterm“cockney”).Yahoo!AnswersandTwit-
conversations,andforumpostsareutilisedforpredictingau-
ter seem to complement one another in terms of informa-
thor’sage(Nguyen,Smith,andRose´2011)withaPearson’s
tion they provide through the terms associated with each
correlation of 0.7. Geo-tagged Twitter data have been used
attribute which in most cases are different. One noticeable
to predict the demographic information of authors such as
difference is that Twitter tends to offer geographical infor-
first language, race, and ethnicity with correlations up to
mation (e.g., “mileend”, “southlondon”, “essex”). On the
0.3(Eisenstein,Smith,andXing2011).
other hand, terms from Yahoo! Answers sometimes match
One aspect of urban area life that has been the focus of
thenameoftheattribute(i.e.“asian”and“Jewish”).
many research work in urban data mining is finding cor-
In the Appendix, in Tables 6 and 7, we show the predic- relations between different sources of data and the depri-
tionresultsforawiderangeof62demographicattributesus- vation index (IMD), of neighbourhoods across a city or a
ingYahoo!AnswersandTwitter.Foreachattribute,wedis- country (Lathia, Quercia, and Crowcroft 2012; Quercia et
playtwotermswiththehighestcoefficientcommonbetween al.2012).Cellulardata(Smith-Clarke,Mashhadi,andCapra
the majority of the folds. Attributes are divided into cate- 2014)andtheelementspresentinanurbanarea(Venerandi
goriessuchasReligion,Employment,Education,etc.Over- et al. 2015) are among non-textual data sources that are
all,theresultsshowthatYahoo!Answersperformsslightly shown to have correlations with a deprivation index. Also,
better than Twitter with an average 1% increase over all flow of public transport data has been used to find corre-
the attributes. Wilxocon signed rank test shows that their lations (with a correlation coefficient of r = 0.21) with
results are significantly different from each other (p-value IMD of urban areas available in UK census (Lathia, Quer-
< 0.01). Outcomes in these tables show that on average, cia,andCrowcroft2012).Researchshowsthatcorrelations
a wide range of demographic attributes of the population ofr =0.35existsbetweenthesentimentexpressedintweets
ofneighbourhoodscanbepredictedusingbothYahoo!An- of users in a community and the deprivation index of the
swersandTwitterwithhighperformancesof0.54and0.53 community(Querciaetal.2012).
respectively.WhileYahoo!AnswersoutperformsTwitterin Socialmediadatahasbeenusedinmanydomainstofind
linkstothereal-worldattributes.DatageneratedonQAplat- Lathia,N.;Quercia,D.;andCrowcroft,J. 2012. Thehidden
forms,however,hasnotbeenusedinthepastforpredicting imageofthecity:sensingcommunitywell-beingfromurban
suchattributes.Inthispaper,weusediscussionsonYahoo! mobility. InPervasivecomputing.
Answers QA platform to make predictions of demographic Li,B.;Jin,T.;Lyu,M.R.;King,I.;andMak,B. 2012. An-
attribute of city neighbourhoods. Previous work in this do- alyzingandpredictingquestionqualityincommunityques-
main has mainly focused on predicting the deprivation in- tionansweringservices. InProceedingsoftheInternational
dexofareas(Querciaetal.2012).Inthiswork,welookata ConferenceonWorldWideWeb.
wide range of attributes and report prediction results on 62
Liu,M.;Liu,Y.;andYang,Q.2010.Predictingbestanswer-
demographicattributes.Additionally,workinurbanpredic-
ersfornewquestionsincommunityquestionanswering. In
tion uses geolocation-based platforms such as Twitter. QA
ProceedingsoftheInternationalConferenceonWeb-AgeIn-
datathathasbeenutilisedinthispaperdoesnotincludege-
formationManagement.
olocation information. Utilising such data presents its own
Nguyen,D.;Smith,N.A.;andRose´,C.P. 2011. Authorage
challenges.
predictionfromtextusinglinearregression. InProceedings
of the 5th Workshop on Language Technology for Cultural
Discussion
Heritage,SocialSciences,andHumanities:AnnualMeeting
Inthispaper,weinvestigatepredictingvaluesforreal-world
oftheAssociationforComputationalLinguistics.
entities such as demographic attributes of neighbourhoods
Preo¸tiuc-Pietro,D.;Volkova,S.;Lampos,V.;Bachrach,Y.;
usingdiscussionsfromQAplatforms.Weshowthattheseat-
and Aletras, N. 2015. Studying user income through lan-
tributescanbepredictedusingtextfeaturesbasedonYahoo!
guage,behaviourandaffectinsocialmedia. PlOSONE.
Answers discussions about neighbourhoods with a slightly
higher correlation coefficient than predictions made using Preo¸tiuc-Pietro,D.;Lampos,V.;andAletras,N. 2015. An
Twitterdata. analysis of the user occupational class through twitter con-
tent. InProceedingsoftheAnnualMeetingoftheAssocia-
Limitations tionforComputationalLinguistics.
Here,wepresentsomeofthelimitationsofourwork. Quercia, D.; Ellis, J.; Capra, L.; and Crowcroft, J. 2012.
Tracking gross community happiness from tweets. In Pro-
ceedingsoftheInternationalConferenceonComputerSup-
Unification of the units of analysis. To unify the units
portedCooperativeWorkandSocialComputing.
of analysis, we take a heuristic approach. We do not cross-
validateourresultswithotherapproaches.Thisisbecauseof Rosset, S.; Perlich, C.; and Zadrozny, B. 2005. Ranking-
thelackofworkinusingnon-geotaggedtextforpredicting based evaluation of regression models. In Proceedings of
attributesofneighbourhoodsinthecurrentliterature. theInternationalConferenceonDataMining.
Salton,G.;Fox,E.A.;andWu,H. 1983. Extendedboolean
informationretrieval. CommunicationsoftheACM.
Coverage. Ourexperimentsinthispaperislimitedtothe
city of London. London is a cosmopolitan city and a pop- Smith-Clarke, C.; Mashhadi, A.; and Capra, L. 2014.
ular destination for travellers and settlers. Therefore, many Povertyonthecheap:Estimatingpovertymapsusingaggre-
discussions can be found on Yahoo! Answers regarding its gated mobile communication networks. In Proceedings of
neighbourhoods. The coverage of discussions on QA plat- International Conference on Human Factors in Computing
formsmaynotbesufficientforallcitiesofinterest. Systems.
Tian,Y.;Kochhar,P.S.;Lim,E.-P.;Zhu,F.;andLo,D.2013.
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Table6:PredictionresultsfordifferentcategoriesandattributesintermsofρusingYahoo!AnswersandTwitterdata.Results
areaveragedover10foldsandstandarddeviationsareshowninparenthesis.Allcorrelationsarestatisticallysignificantwitha
p-value<0.01.Foreachcategory,thedifferenceinperformancebetweenthetwosourcesarehighlightedinthecolumnrelated
totheoutperforming(i.e.upwardarrow)source.
Yahoo!Answers Twitter
Attribute ρ Terms ρ Terms
Price&Deprivation 0.69 5%↑ 0.64
HousePrice 0.69 money,compliment 0.68 dailyspecial,personaltrainer
IMD 0.69 notorious,cockney 0.56 np,eastlondon
Religion 0.37 0.39 2%↑
Muslim% 0.51 asian,barber 0.54 mileend,eastlondon
Jewish% 0.42 jewish,arab 0.13 rsa,rugby
Hindu% 0.32 stadium,cemetery 0.46 smokeyeye,asianbride
Buddhist% 0.24 minister,tourist 0.44 theatre,prayforparis
Ethnicity 0.49 6%↑ 0.43
Black% 0.6 gang,drug 0.44 southlondon,frank
Asian% 0.40 asian,barber 0.39 mileend,gymselfie
White% 0.58 essex,suburbia 0.45 essex,golf
Mixed% 0.37 reggae,gang 0.45 studio,southlondon
AgeGroup 0.56 0.60 4%↑
0-15 0.53 mortgage,crappy 0.54 uel,ikea
16-29 0.66 student,music 0.66 drum,campus
30-44 0.5 cycle,psychic 0.6 nffc,loyaltylunch
45-64 0.46 temporarily,underrate 0.57 essex,golf
65Plus 0.62 hospital,outskirts 0.62 golf,thearcher
WorkingAge 0.58 foody,triple 0.64 ukjob,delay
HouseholdComposition 0.55 0.58 3%↑
CoupleWithDependentChildren% 0.57 belt,affordability 0.76 blondieblue,xoxo
CoupleWithoutDependentChildren% 0.59 role,essex 0.55 essex,semipermanentmakeup
LoneParentHousehold% 0.61 gang,mortgage 0.38 helpme,ikea
OnePersonHousehold% 0.55 hotel,fashionable 0.7 personaltrainer,wine
AtLeastOnePerson16+English1stLanguage% 0.52 essex,outskirts 0.55 golf,essex
NoPeopleAged16+English1stLanguage% 0.46 asian,foreigner 0.53 tube,edgwareroad
ResidentialStatus 0.58 0.63 5%↑
OwnedOutright% 0.69 chelmsford,outskirts 0.61 starbucks,grand
OwnedWithAMortgageOrLoan% 0.67 belt,scummy 0.74 barbergang,essex
SocialRented% 0.65 cockney,dump 0.56 ikea,studio
PrivateRented% 0.48 hotel,privacy 0.6 tube,edgwareroad
HouseholdOne+UsualResident% 0.49 mortgage,gang 0.59 eastlondon,londonbridge
HouseholdNoUsualResidents% 0.39 hotel,square 0.61 hotel,marblearch
WholeHouseOrDetached% 0.56 underrate,retiree 0.69 instafamily,crochet
WholeHouseOrSemiDetached% 0.65 benefit,suburbia 0.72 essex,semipermanentmakeup
WholeHouseOrTerraced% 0.55 cypriot,value 0.53 followforfollow,brockley
FlatOrApartment% 0.68 location,inexpensive 0.76 nffc,pcm
Sale 0.53 commute,upmarket 0.5 personaltrainer,crochet
Employment 0.54 8%↑ 0.46
NoAdultsEmployed-DependentChildren% 0.52 interchange,cockney 0.33 ikea,gymtime
LoneParentNotInEmploymentPercent 0.55 slum,cockney 0.53 mileend,edgwareroad
EconomicallyActiveTotal 0.46 suite,deprive 0.57 railway,kensalrise
EconomicallyInactiveTotal 0.58 student,triple 0.58 mileend,gymtime
EconomicallyActiveEmployee 0.28 cycle,deprive 0.4 railway,royaltylunch
EconomicallyActiveSelfEmployed 0.65 jewish,affordability 0.47 rugby,northlondon
EconomicallyActiveUnemployed 0.66 cockney,drug 0.5 np,eastlondon
EconomicallyActiveFullTimeStudent 0.57 student,asian 0.49 np,instrumental
EmploymentRate 0.54 commute,suburban 0.36 railway,barbergang
UnemploymentRate 0.59 notorious,cockney 0.40 swap,eastlondon
Table7:cont.
Yahoo!Answers Twitter
Attribute ρ Terms ρ Terms
Education 0.54 0.58 4%↑
NoQualifications 0.62 scummy,cockney 0.55 eastlondon,puregym
HighestLevelOfQualificationLevel1% 0.68 essex,scummy 0.72 eastlondon,hackneywick
HighestLevelOfQualificationLevel2% 0.69 scummy,role 0.77 followforfollow,tattoo
HighestLevelOfQualificationApprenticeship% 0.56 role,truck 0.75 tatemodern,oldstreet
HighestLevelOfQualificationLevel3% 0.16 role,fish 0.23 bttower,fresher
HighestLevelOfQualificationLevel4%AndAbove 0.71 scholarship,affordability 0.62 rugby,cave
HighestLevelOfQualificationOther% 0.38 employer,stadium 0.39 endorphin,tube
SchoolchildrenAndFullTimeStudents18+% 0.53 student,chips 0.59 tube,np
Health 0.42 0.42
DayToDayActivitiesLimitedALot% 0.33 cockney,gang 0.31 eastlondon,shisha
DayToDayActivitiesLimitedALittle% 0.4 gloom,puppy 0.52 cafc,hackneywick
DayToDayActivitiesNotLimited% 0.39 commute,park 0.37 tea,enjoysmilelive
VeryGoodOrGoodHealth% 0.48 commute,park 0.39 rwc,tea
FairHealth% 0.52 scummy,gang 0.59 streetfood,coy
BadOrVeryBadHealth% 0.35 cockney,gang 0.34 eastlondon,east
CarOwnership 0.62 0.71 9%↑
NoCarsOrVansInHousehold% 0.72 brewery,cockney 0.71 tube,groove
1CarOrVanInHousehold% 0.67 suburban,grounds 0.7 onelife,supercar
2CarsOrVansInHousehold% 0.67 hospital,outskirts 0.71 thearcher,golf
3CarsOrVansInHousehold% 0.57 role,belt 0.75 dailypic,boxpark
4OrMoreCarsOrVansInHousehold% 0.49 freehold,residential 0.69 rugby,cave
Average 0.54 0.53