Structural Patterns of the Occupy Movement on Facebook

1.2 MB·English

by  Michela Del Vicario

Structural Patterns of the Occupy Movement on Facebook
MichelaDelVicario QianZhang AlessandroBessi FabianaZollo
IMTLucca,Italy NortheasternUniversity,Boston,USA IUSSPavia,Italy IMTLucca,Italy
AntonioScala GuidoCaldarelli WalterQuattrociocchi
ISCCNR,Rome,Italy IMTLucca,Italy IMTLucca,Italy
[email protected]
Abstract However, users tend to be more active on Facebook. In-
5
deed,themostofonlineactivityduringtheArabSpringhas
1
In this work we study a peculiar example of social organi- been done on Facebook (the total accesses to Twitter were
0
zationonFacebook:theOccupyMovement–i.e.,aninterna- just the 1% of the entire population) [15]. The collective
2
tionalprotestmovementagainstsocialandeconomicinequal-
framing – the processes that, out of the essential features
n ityorganizedonlineatacitylevel.Weconsider179USFace-
ofthemovement’spurposeandstruggle,establishitsnarra-
a bookpublicpagesduringthetimeperiodbetweenSeptember
tives, language, and imagery [6, 16] – better deal with the
J 2011 and February 2013. The dataset includes 618K active
8 usersand753Kpoststhatreceivedabout5.2Mlikesand1.1M Facebookinteractionparadigm.OnFacebookuserscanpost
2 comments.Bylabelinguseraccordingtotheirinteractionpat- information(videos,pictures,etc.)withoutparticularlimits
ternsonpages–e.g.,auserisconsideredtobepolarizedif andinformationcanbeliked(positivefeedback),shared,or
shehasatleastthe95%ofherlikesonaspecificpage–we commented by other users. In [17, 18] the diffusion of the
]
I find that activities are not locally coordinated by geograph- Occupy movement on Facebook has been explored. Find-
S
ically close pages, but are driven by pages linked to major ingsindicatethatmajorusesforFacebookwithinthemove-
s. UScitiesthatactashubswithinthevariousgroups.Sucha mentincludetherecruitmentofpeopleandresourcestolo-
c patternisverifiedevenbyextractingthebackbonestructure
cal occupations, information sharing and story telling, and
[ –i.e.,filteringstatisticallyrelevantweightheterogeneities–
across-groupexchanges.
forboththepages-resharesandthepages-commonusersnet-
1 works. Takingintoaccountallthepostsfrom179publicpagesof
v theOccupyMovementinsidetheUS(USOccupypagesfor
3
short)duringtheperiodSeptember2011-February2013,we
0
Introduction analyzeusers’activityonpagesandposts.Wefindhighlev-
2
elsofcorrelationforthenumberofusers,posts,likes,com-
7 Social media have proved to foster aggregation of people ments and reshares1 (which are all power law distributed)
0
aroundsharedinterestssuchaspoliticalideas,narrativesand
. on each page, and different activity patterns for posts with
1 worldviews [1, 2, 3, 4]. An interesting scenario to be ex-
different post types or shared by users from different cat-
0 plored is the case of online political movements that coor-
egories. Then, we divide users in two categories, habitual
5 dinate and interact through social media [5]. To which ex-
and occasional, depending on their total number of likes.
1 tent microblogging platforms such as Twitter or Facebook
Furthermore,habitualusershavingatleast95%oftheirlik-
v: playedacrucialroleinprotestdynamicshasbeenamatter ing activity on one particular page are defined as polarized
i ofdebatein [6,7,8].OccupyWallStreet,aninternational in that page. Finally, we extract the multi-scale backbone
X
movementagainstsocialandeconomicinequality,isapecu-
structuresfortwonetworks,thepages-resharesnetworkand
r liarexampleoftheuseofInformationandCommunication
a the pages-common users network. We want to analyze ge-
Technologies (ICTs). There, the diffusion of ideas, the re-
ographical patterns in the information diffusion and polar-
cruitmentofpeople,aswellasthepromotionoftheprotest,
ized users’ external activity – i.e., the activity on all pages
are ascribed to online social media [9, 10]. Particular em-
but the one where the user is polarized. Our findings show
phasisisgiventothesimplificationgainedinthecommuni-
thatthemostoftheactivity,intermsoflikesoneachpage,
cationandcoordinationparadigmofprotest’sactivities.The
is made by occasional and not polarized users, and more-
reason why Twitter and Facebook appeared to be particu-
overonly21%ofpolarizeduserscommentedatleastonce.
larlysuitedinsupportingthediffusionofpoliticalideasand
However, polarized users tend to increase their probability
socio-economicobjectivesofthemovementresidesintheir
ofcommentingmultipletimes,withrespecttogeneralusers.
structure. Many works analyzed the topology and dynam-
Ouranalysispointsoutthat,althoughthemajorityofposts
icsontheOccupyMovement’snetwork[11,12,13,14]on
is shared by common users, they attract little attention i.e.,
Twitter.
Copyright(cid:13)c 2015,AssociationfortheAdvancementofArtificial 1The two terms shares and reshares are interchangeable
Intelligence(www.aaai.org).Allrightsreserved. throughoutthemanuscript.
about6%oftotallikes.Onthecontrary,postssharedbypage interviews, the news of the first demonstrations came from
administrators, which represent only 27% of the total, get Facebook,face-to-faceinteractionandtelephone.Moreover
alltheremaininglikes.Theseevidencesemphasizethecen- about 50% of the interviewed declared to have access to
tral role of the pages in promoting the debate and driving Facebook, while it is known that less than 1% had access
its direction, and at the same time reshape the idea of on- toTwitter.
line debate participation. We find that online activities are
not locally coordinated by geographically close pages, in- DataDescription
deedpageslinkedtomajorUScities–e.g.,NewYork,Los
Wecollectedallpostsfrom179FacebookpublicUSpages
Angeles,Chicago,Boston,SanFrancisco–drivethediffu-
abouttheOccupyMovementduringthetimespanSeptem-
sionofcontentsonlineandserveascoordinationpointsfor
ber2011-February2013.Datahavebeencollectedusingthe
allotherpages,thatperformaminoractivityinthesystem.
Facebook Graph API [32] on public accessible pages, for
each of which we have a geographical reference located in
RelatedWork
the US. A total of 618K users is active, in terms of liking
Sociologists and political scientists explored the environ- activity,onasetof753K posts2.Thetotalnumberoflikes
ment of Internet based social movements focusing on a set and comments on the downloaded posts are about 5.2M
of common themes [6, 7, 8, 19, 20, 21, 22, 23, 16, 24]. In and 1.1M, respectively. The dataset represents a complete
the general frame of communicational and organizational screenshot of the Occupy Movement in the period imme-
issues, several relevant topics emerged, such as the afore- diately following the outbreak of the protest on September
mentionedcollectiveframing[6,16]andtheresourcesmo- 17th, 2011 in the Zuccotti Park of New York. The dataset
bilization, which refers to all those processes exploited by covers all the posts until the end of February 2013, at the
social movements in order to arrange financial, material, time when all the major protests were no more active. Af-
and human resources required to sustain their activities in tertheZuccottioccupation,infact,anOctoberfullofsimi-
an efficient way [23]. The Internet is commonly perceived laroccupationaleventsfollowed,leadingtoaninternational
as a flexible and relatively fluid media, in terms of reor- protestmovementthatextendeditselfuntiltheendof2012,
ganization. However, at the same time its flexibility may when the movement was principally an online collective
reduce the coherence of the movement’s ideological defi- protest.
nition. In [24] three communication eras have been iden-
tified, each one dominated by a different communication PreliminariesandDefinitions
medium – i.e., print media and word of mouth, television
Inthissectionweintroducethebasicnotionsanddefinitions
and the Internet. It is argued that each of those communi-
that will be used throughout the paper. We refer to the set
cationtechnologiesimpliesimportantdifferencesinthenet-
ofUSOccupypagesandtheirrespectiveactiveusersasthe
workstructuresunderwhichpotentialprotestorsmaybein-
OccupySystemorNetwork,whereauserissaidtobeactive
fluenced to act and, hence, in the resulting protest waves.
ifshelikedatleastonepostbelongingtotheOccupypages.
In the particular case of Occupy Wall Street, many works
investigated the role of Twitter in organizing and promot-
ing the protest [11, 12, 13, 14]. The relationship between Statistical Tools. We use several figures showing PDFs,
thegeospatialdimensionsofsocialmovementscommunica- CDFs, and CCDFs of some metrics related to activity and
tion networks and the organizational pressures facing such consumption patterns. We remind that the probability den-
movements,wasextensivelyinvestigatedin[11].Highlev- sity function (PDF) of a real–valued random variable is a
elsofgeospatialconcentrationintheattentionallocationis- function fX that describes the probability of the random
sueandadifferentiationofcontentsfromthenationaltothe variablefallingwithinagivenrangeofvalues,sothat
international level were found. The evolution of the com-
(cid:90) b
munication activity and of the topics under discussion was Pr[a≤X ≤b]= f (x)dx.
X
analyzedin[12,13].AstudyoftheevolutionoftheOccupy
a
Movement on Facebook was presented in [17]. Twitter, in
Thecumulativedistributionfunction(CDF)ofareal–valued
additiontoOccupy,hasalsoplayedaprominentroleinsev-
randomvariableX isdefinedas
eralothersocialmovementssuchastheEgyptianrevolution-
(cid:90) x
aryprotestsof2011[25,26,27,28]andtheArabSpringin
F (x)=Pr(X ≤x)= f (u)du.
general[29,30].Themostinvestigatedaspectsconcernin- X X
−∞
formationflowsandrelationshipsbetweennewsmediaand
Similarly,thecomplementarycumulativedistributionfunc-
information sources [25, 29], analysis of tweets’ contents
tion(CCDF)isdefinedasoneminustheCDF,sothat
[26], impact of media disruption on the dispersion of the
protest[28],presenceofsocialinfluenceandcomplexcon- (cid:90) ∞
tagionintherecruitmentpatterns[27].Theroleofsocialties CX(x)=1−FX(x)=Pr(X >x)= fX(u)du.
inmovementrecruitmentwereextensivelyanalysedalsoof- x
fline[31].In[15]evidencesregardingtheprominentroleof 2 For each post we have the following data: post ID, sharing
FacebookwithrespecttoTwitterarereported.Asetof1K userID,sharingtime,posttype–e.g.,photo,status,videoorlink–
interviews of people who took part in the protest was ana- numberofreceivedlikesandcomments,sharingpageIDandobject
lyzed,withtheinterestingfindingthat,forabout50%ofthe ID,thatisauniquereferencetothepostincaseofresharing.
Classificationofusersactivity. Weassumethefollowing posts likes comments
definitions for the users categories in the Occupy System. 0.6 0.6 0.6
Afirstdistinctionisbetweenhabitualandoccasionalusers:
habitual users are those who liked at least 5 posts within 0.5 0.5 0.5
the Occupy System, while occasional users are those who
0.4 0.4 0.4
likedlessthan5posts.Beingcommonlyperceivedasposi-
tivefeedbacks,likesareusedasadeterminanttoidentifythe 0.3 0.3 0.3
membership in one page. Polarized users have been identi-
fied by a thresholding technique, using again the like as a 0.2 0.2 0.2
discriminant. Habitual users having 95% or more of their
0.1 0.1 0.1
liking activity on a particular page are said to be polarized
onthatpage.Noticethatapolarizeduserintheoccupysce- 0.0 0.0 0.0
status photo link video status photo link video status photo link video
nario is a user having the most of her liking activity on a
specificgeolocatedcommunity.
Figure 1: Fractions of number of posts and activity on
Bipartite networks and backbone filter. A bipartite posts(likesandcomments)dividedbyposttypes.Thelev-
graph is a triple G = (A,B,E) where A = elsofcommentingactivitiesareproportionaltothoseofthe
{a |i=1...n }andB ={b |j =1...n }aretwodis- numberofpostsforeachdifferentposttype.Photosgetmost
i A j B
jointsetsofvertices,andE ⊆A×Bisthesetofedges–i.e. partofthelikes,althoughtheyrepresentlessthanafifthof
edgesexistonlybetweenverticesofthetwodifferentsetsA the total number of posts. For other post types the fraction
andB.ThebipartitegraphG isdescribedbythematrixM oflikesisonehalfofthenumberofposts.
definedas
(cid:26)
1 ifanedgeexistsbetweena andb
M = i j is able to filter out the backbone of dominant connections
ij 0 otherwise
in weighted networks with strong disorder, preserving the
The co-occurrence matrices CA = MMT and CB = structuralpropertiesandhierarchiesatallscales.TheBack-
MTM count, respectively, the number of common neigh- bone structure has been extracted for the two aforemen-
bors between two vertices of A or B. CA is the weighted tioned co-occurrence real networks. The discrimination of
adjacency matrix of the co-occurrence graph CA with ver- the right trade-off between the level of network reduction
tices on A. Each non-zero element of CA corresponds to andtheamountofrelevantinformationpreservedinthenew
anedgeamongverticesa anda withweightPA.Theco- representationinvolveadditionalissues.Inmanycases,the
i j ij
probabilitydistributionP(x)thatanygivenlinkiscarrying
occurrence graph CB is built in the same way from the co-
aweightxisbroadlydistributed,spanningseveralordersof
occurrencematrixCB.
magnitude.Suchaproblemisaddressedbyusingtheafore-
Let A be the set of the 179 Occupy US pages, B the
1 mentionedmethodpresentedin[33].
set of all posts equivalence classes’ representatives (rep-
resentatives posts for short)3, and B the set of all polar-
2
ResultsandDiscussion
izedusersactiveonA;thepages-postsbipartitenetworkis
thendefinedasthetripleG1 = (A,B1,E1),whereanedge ActivityonPages
e1 ∈E existsifrepresentativepostb1issharedonpagea1,
ij 1 j i Ouranalysistargetsinformationconsumptionpatternsofthe
whilethepages-polarizedusersbipartitenetworkisdefined
asthetripleG = (A,B ,E ),whereanedgee2 ∈ E ex- USOccupyMovement.Thefocusisontherelationshipbe-
2 2 2 ij 2 tweengeographicdistanceandthespreadingofinformation
istsifpolarizeduserb2isactiveonpagea2.Forouranalysis
j i across pages. In addition we characterize the role of polar-
weusedtwonetworksderivedasco-occurrencenetworksof
ized users promoting and bridging inter-page connections.
the pages-postsand the pages-polarizedusers bipartite net-
Asafirststepwelookattheusers’activityonthedifferent
works.Consideringtheco-occurrencematricesCA andCA
1 2 cityrelatedpages.Figure1reportsthefractionsofnumber
wegettwoco-occurrencenetworksonthevertexsetA:the
ofposts,likes,andcommentsforthefourkindofposts:sta-
pages-reshares(CA)andthepages-commonusers(CA)net-
1 2 tus,photo,link,andvideo.Thecommentingactivityiscor-
works. In CA an edge between two pages exists if at least
1 relatedtothenumberofposts,whilemorethanahalfofthe
onerepresentativepostissharedonbothpages,whileinCA
1 likesisconcentratedonphotos.Statuses,thataccountforal-
there is a link between two pages if they share at least one
most two fifths of the total number of posts, get less than
userwhoispolarizedineitherpage.
a fifth of the total number of likes. Such a disproportion in
In the last section we show results about the Backbone
thelikingactivitymightbeduetotheuserslimitedattention
Extractionpresentedin [33].Suchamethodapplyathresh-
[34, 35, 36] that preferentially tend to select easy to han-
olding filter based on the local identification of the statisti-
dleposttypes.Figure2showstheempiricalcomplementary
callyrelevantweightheterogeneities.Thiskindofapproach
cumulative distribution function (CCDF) of the number of
3Wecanconsidertheequivalencerelationofhavingthesame users,posts,likes,comments,andresharesforallpages.All
objectID;twopostsareequivalent,andhencebelongtothesame thefivedistributionsareheavy-tailed.Tobettervisualizethe
equivalenceclass,iftheyhavethesameobjectID. geographicaldistributionoftheOccupyMovement,activity
CCDF 111000--210 l*ll* uplcl*ikosoleemls**srt*llms*sl*l****e*ll*l*l*llnl**l*l*l*l***tll**s*l*l********l*l*l**l**l****l*l*l*****l*l*l******l**ll*l***ll*l***l*l*****ll*l*ll**l*l*l*ll*l**l***ll**l**l*ll*llll**l**llllll****ll*l*lll*l***l**ll**l*ll**ll**ll*ll**l**l*ll*lll*ll**l*l*ll*lll***l**l*l*l*l**l***lll**ll**l*l**l*llllll*l*llll*lll*l*lll*llllllllll*lllllll*llllllllll CCDF 111111000000-----543210 l lllliklellsllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll ll
reshares * l 10-6 comments
100 101 102 103 104 105 106 100 101 102 103 104 105
# users, posts, like s and comments # likes and comments
Figure 2: Empirical CCDF for number of users, posts, Figure 5: Empirical CCDF for likes and comments on
likes,comments,andsharesonpages.Takingalltheusers posts.TheCCDFofthenumberoflikes(red)andcomments
(purple),posts(lightblue),likes(red),comments(skyblue), (skyblue)onthetotalityofposts,showspowerlawdistribu-
andshares(pink)oneachpageweplottedtheCCDFofthe tions for the two measures, with a significant difference in
five quantities and we got a power law distribution for all thescale.
themeasures,withsimilarshapesandreasonablescaledif-
ferences.
ActivityonPosts
The CCDF of the number of likes and comments for each
patterns are shown on the US map (Figure 3). Notice that
postshowspowerlawdistributions(Figure5).
thereexistsahighcorrelationbetweenallpairsofmeasures
Regardless of the page, information can be shared by its
–i.e.,users,posts,likes,comments,andshares.
administrators,andhenceappearsassharedbythepageit-
In Figure 4 similar results to those depicted in Figure 3
self,orbycommonusers.Postssharedbypageadministra-
holdforresharingpatterns.4 Pearsoncorrelationcoefficient
tors–i.e.adminposts–are27%ofthetotal(202K)against
showshighlevelofcorrelationbetweenallconsideredmea-
the73%(551K)fromcommonusers–i.e.,notadminposts
sures – i.e., number of users, posts, likes, comments and
– and, on a page scale, there is a weak correlation (0.37)
shares – on the different pages, as shown in Table 1. Sum-
betweenthenumberofpostsmadebyadminandnotadmin
marizing, the activity of the users on the Occupy pages is
users(Figure6).
comparableacrosspages;numberofusers,posts,likes,com-
The number of likes to a post from the two categories is
mentsandsharesforeachpagearehighlycorrelated.
power law distributed with a quantitative difference: while
the majority of posts is in the not admin category, they get
Users Posts Likes Comments Shares
less than 6% of total likes, as shown in Figure 7. Number
Users 1 0.974 0.997 0.991 0.739
of posts by each category and number of likes reached are
Posts 0.974 1 0.963 0.987 0.840
not proportional. This disproportion is due to the fact that,
Likes 0.997 0.963 1 0.987 0.714
onFacebookpublicpages,postsbycommonusersbecome
Comments 0.991 0.987 0.987 1 0.769
soon hidden, while those from pages admin get higher vis-
Shares 0.739 0.840 0.714 0.769 1
ibility.Activityonpostsispowerlawdistributed.Aninter-
estingquantitativedifferencebetweenpostssharedbypage
Table1:Correlationsbetweenthenumberofusers,posts, administratorsandcommonusersemerges.
likes,comments,andshares.Pearsoncorrelationfornum-
ber of users, posts, likes, comments, and shares is high for UsersActivity
alldifferentcombinations.Therearehighcorrelationvalues
Ourdatasetincludes618Kactiveusers.Auserisconsidered
amongthefiveconsideredmeasures.
tobeactiveifshelikedatleastonepost.Weidentifiedtwo
mainuserscategories:occasional–i.e.,usersthatmadeless
than 5 total likes – and habitual – i.e., users that made at
4Inordertoidentifyareshareweneedareferencetotheprevi-
least5totallikes.
ouspoststhroughanObjectID.Thisisafundamentalinformation
forthereconstructionofresharingpatterns,althoughwehadnoin- Suchapreliminarydistinction,coherentlywiththeheavy-
formationabouttheoriginalpostsandnoreferencestothedirectly tailed distributions of the users activities, counts 183K ha-
resharedposts. bitual users (about 30% of the total). As a further step we
Figure3:Geospatialdistributionsofactivities.Thenumberofusers(purple),posts(lightblue),likes(red),andcomments(sky
blue)isplottedintheformofdotsontheUSmap.Thebiggerthedotthehigherthenumberofelementsinthecorresponding
category (on the page corresponding to the geographical spot on the map). We can see how the size of the dots, across the
different measures, is comparable for all the geographical points. This is a first empirical evidence of the high correlation
amongthefourconsideredmeasures.
l
l
l l
l l l l
l l
lllllllllllllllllllllllllllllllllllllllll ll lllllll l llllll lllllllllllllllllllllllllllllllllllllllllllllllllllllllllll ll l ll llll ll lllllllllll # lollf Re5368s0,,,005h0000a000res
l l l lll l llll
llll lll
ll l
l
Figure4:Resharingpatterns.ResultssimilartotheonesinFigure3holdfortheresharingactivity.Wecomputedthenumber
ofresharesonasubsetrepresentingaboutthe14%ofallposts.
# posts111111000000123456 llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllPP Cllllloolollllsslrlllrttllesslllll lllabbllllyyltllill ollANllnllldlol ll=lmltll l lAl0lillnlld.ll3lmlll7lllinl CCDF111000--210 ll lOPNOlooctlhlctal−earlPrislzlolielollllladnlllralillzlllllelllldllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll
l l l
lll
100 l 100 101 102 103 104 105
# of Users for each Category
pages
Figure 8: CCDF of different users’ categories on all
Figure 6: Posts by admin and not admin. On the x-axis pages. The CCDF of the number of active users for each
therearetheUSOccupypagesandonthey-axisthecorre- categories, computed for every single page, shows how the
spondingnumberofpostssharedbythepageadministrators majority of the liking activity is made by occasional (red)
(blue) and by common users (green). On the total of posts, and not polarized (purple) users. Remember that a user is
27% is made by page administrators and 73% by common activeonapageifshelikedatleastonepostfromthatpage.
users. However there is low correlation (0.37) between the Howevertheactivityofpolarizedusers(blue)isstillsignifi-
twosharingpatterns. cant,whileuserspolarizedonotherpages(violet)represent
amarginalportionofthetotallikes.
100 l definepolarizedusers–i.e.,ahabitualuserispolarizedona
l
CCDF1111100000-----54321 l aanllldollmtl lalilndllllmlllllllilllnlllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll gSi1oap–pmzlvi9o,ouelvae8llFctdpeaahdrhKinrrageiuiiiflanyzzusplfceeeertalFohaeddrrigkiemlsr,gle8enec(numssost2ihosrhfm1aeechotosnn%aomlw9hpdtdtw.eseouiogcnsal–nhfaoogiatripmrtth.ischiioezeeelme.waeass,td,Cstereoos,lenfrCeitonstafiaalspDrnlacs.ae)dwteFa,cat9gttipcohiod5ivohvfane%eletcatlpdnhaypraoeiyergztpfto.heeeanhdMced,gueut6eismrcoos2earln–b%isedbkehooaier3ov.ause.wdenot2.roi,df1nM,fnf2ou1teoh8cts5rnhlceKee%iaakrntstssehtpioipszeofpofaernaaloUtganahgomerSldee-f.,
l
l
100 101 102 103 104 Regarding the activity of polarized users we observe a
# likes
power law distribution for both number of likes and com-
ments (Figure 10). Notice that only about 21% of polar-
izeduserscommentedatleastonce.ThePearsoncorrelation
Figure 7: Likes activity on posts by admin, not admin, coefficient computed on the number of polarized users, to-
and all users. The CCDF for the number of likes reached tal users, posts, likes, comments, and shares, provides high
by all users posts (red), admin posts (blue), and not admin correlations levels for all the combinations of measures, as
posts(green)showsasignificantlybiggerprobabilityofhigh showninTable2.
number of likes for the admin posts rather than not admin
posts.Consideringthatjustaboutonethirdofallthepostsis Our findings, coherently with the heavy-tailed distribu-
sharedbypageadministrators,wegotanevidentdispropor- tionsofusersactivities,showadominantroleofoccasional
tion,thatmaybeexplainedbythehighervisibilityreserved andnotpolarizedusersintheinformationconsumptionand
toadminposts. adifferentiationoftheprobabilityoflikingandcommenting
forpolarizedusersdependingontheirlevelofactivity.
l
l
l l
l l l l
l l
lllllllllllllllllllllllllllllllllllllllll ll lllllll l llllll lllllllllllllllllllllllllllllllllllllllllllllllllllllllllll ll l ll llll ll lllllllllll # lolf Po137l050a,,,r000iz000e000d
l l l lll l llll
llll lll
ll l
l
Figure 9: Geospatial distributions of the polarized users on pages. Representation of polarized users’ communities for all
pages:thesizeofthedotsisproportionaltothesizeofthecommunitiesandemphasizesthecorrelationbetweenthenumberof
polarizedusersandthenumberofusers,posts,likes,comments,andresharesforeachpage(seealsoFigure3and4).
Polarized Users Posts Likes Comments Shares
Polarized 1 0.993 0.948 0.998 0.978 0.675
Users 0.993 1 0.974 0.997 0.991 0.739
Posts 0.948 0.974 1 0.963 0.987 0.840
Likes 0.998 0.997 0.963 1 0.987 0.714
Comments 0.978 0.991 0.987 0.987 1 0.769
Shares 0.675 0.739 0.840 0.714 0.769 1
Table2:Correlationsamongnumberofpolarizedusers,
totalusers,posts,likes,comments,andreshares.Pearson
CCDF 1111100000----43210 l likeslllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll cwpBWpeaonoaiaBergctlrchaaeeerkiwcslintkazbhabotetneoihdorotnendnupeteicsoofcofeoEluoanrfuxssersnt,iiIfirozfiddnpaeneecotcdrriestoeseitrtnfoudsaa,tnsinecelnmdilAtrfkesioleemoigrasfinmoe,nsnrutcaPhitrotttsheeaihmosetm–ngtmhe.Oie[.eiroeH3gcnng.3chetrsn]suna,ulptcpeaomyehvn,ietbdcmwlweasrrooleeovosdahfehfifamaffitcvforoleeeetirnraasera.tltenipoulitpannstnlefliieoreoustnd-f-,
10-5 comments l works, as mentioned before. Figure 11 shows on the US
100 101 102 103 104 map the results for the backbone extraction applied to the
# likes and comments
pages-reshares network, for two different levels of signifi-
cance α = {0.01, 0.05}. These results provide a clear im-
ageoftheabsenceofgeographicalcorrelationinthereshar-
Figure 10: Activity patterns for polarized users. The
ing patterns. Moreover, pages corresponding to the major
CCDFofliking(red)andcommenting(skyblue)activityfor
US cities5 emerge as leaders in the information spreading.
polarized users shows again power law distributions. Com-
Figure 12 illustrates the multi-scale backbone structure for
paringitwithFigure5,wherethenumberoflikesandcom-
thepages-commonusersnetwork,forthetwolevelsofsig-
ments are computed on all users, we can notice that the
nificanceα={0.01, 0.05}.Thelinkscorrespondtotheac-
quantitative difference between red and sky blue curves is
tivity of users polarized on one page inside another page.
smaller and that, when a level of about 1000 likes (com-
Again, no geographic correlation is present, and five main
ments) is reached, the trend starts to revert leading to a
citiesshowinganexchangeofpolarizedusers’activitymay
higherprobabilityofcommentingratherthanliking(forpo-
beidentified:NewYork,LosAngeles,Boston,Portlandand
larizedusers).
SanFrancisco.
HubscorrespondingtheUSmajorcitiesdrivetheoverall
5Forbothlevelsofsignificance,thefollowingcitiesemergeas
informationspreadingleaders:NewYork,LosAngeles,Chicago,
Boston,Portland,PhoenixandDenver.
(a) α=0.01 (a) α=0.01
(b) α=0.05 (b) α=0.05
Figure11:Backbonestructureforthepages-resharesnet- Figure 12: Backbone structure for the pages-common
work,forα = {0.01, 0.05}.TheBackboneExtractionAl- users network, for α = {0.01, 0.05}. The Backbone Ex-
gorithm, applied to the pages-reshares network, preserves tractionAlgorithm,appliedtothepages-commonusersnet-
26.58%ofthetotaledgeweightforα=0.01and42.7%for work, preserves 64.95% of the total edge weight for α =
α = 0.05. We can notice the absence of geographical cor- 0.01 and 70.65% for α = 0.05. We can notice theabsence
relation in the information spreading and the emergence of ofgeographicalcorrelationintheactivityofpolarizedusers
USmajorcities–NewYork,LosAngeles,Chicago,Boston, across pages and the emergence of US major cities – New
Portland, Phoenix and Denver – as information diffusion York,LosAngeles,Boston,PortlandandSanFrancisco–as
leaders. polarizedusers’activityexchanger.
activity of the movement, the diffusion of contents online misinformation: the role of homophily and polariza-
andserveascoordinationpointsforallotherpages. tion. arXivpreprintarXiv:1411.2893,2014.
[4] Adrien Friggeri, Lada A Adamic, Dean Eckles, and
Conclusion
JustinCheng. Rumorcascades. ICWSM,2014.
In this paper we explore the case of online political move- [5] Martha McCaughey and Michael D Ayers. Cyberac-
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addressgeographicalpatternsbehindinformationdiffusion.
Takingintoaccountallthepostsfrom179USpublicFace- [7] WinstonBennett. Communicatingglobalactivism. In-
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Flammini. The geospatial characteristics of a so-
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icalpatternsintheinformationdiffusionandpolarizedusers
[12] MichaelDConover,EmilioFerrara,FilippoMenczer,
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andAlessandroFlammini. Thedigitalevolutionofoc-
locally coordinated by geographically close pages. Indeed,
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contentsonlineandserveascoordinationpointsforallother Thetopologyofadiscussion:the#occupycase. arXiv
pages,whichperformaminoractivityinthesystem. preprintarXiv:1412.4639,2014.
[14] Kjerstin Thorson, Kevin Driscoll, Brian Ekdale,
Acknowledgments
StephanieEdgerly,LianaGamberThompson,Andrew
Funding for this work was provided by EU FET project Schrock, Lana Swartz, Emily K Vraga, and Chris
MULTIPLEXnr.317532andSIMPOLnr.610704.Thefun- Wells. Youtube, twitter and the occupy movement:
dershadnoroleinstudydesign,datacollectionandanalysis, Connectingcontentandcirculationpractices.Informa-
decisiontopublish,orpreparationofthemanuscript. tion,Communication&Society,16(3):421–451,2013.
[15] Zeynep Tufekci and Christopher Wilson. Social me-
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