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Polarization of the Vaccination Debate on Facebook

DOI:10.1016/j.vaccine.2018.05.040
Authors:
Ana Lucía Schmidt at Roche
Ana Lucía Schmidt
Fabiana Zollo at Università Ca' Foscari Venezia
Fabiana Zollo
Antonio Scala at Italian National Research Council
Antonio Scala
Cornelia Betsch at Universität Erfurt
Cornelia Betsch
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Abstract and Figures

Background: Vaccine hesitancy has been recognized as a major global health threat. Having access to any type of information in social media has been suggested as a potential influence on the growth of anti-vaccination groups. Recent studies w.r.t. other topics than vaccination show that access to a wide amount of content through the Internet without intermediaries resolved into major segregation of the users in polarized groups. Users select information adhering to theirs system of beliefs and tend to ignore dissenting information. Objectives: The goal was to assess whether users' attitudes are polarized on the topic of vaccination on Facebook and how this polarization develops over time. Methods: We perform a thorough quantitative analysis by studying the interaction of 2.6 M users with 298,018 Facebook posts over a time span of seven years and 5 months. We applied community detection algorithms to automatically detect the emergence of communities accounting for the users' activity on the pages. Also, we quantified the cohesiveness of these communities over time. Results: Our findings show that the consumption of content about vaccines is dominated by the echo chamber effect and that polarization increased over the years. Well-segregated communities emerge from the users' consumption habits i.e., the majority of users consume information in favor or against vaccines, not both. Conclusion: The existence of echo chambers may explain why social-media campaigns that provide accurate information have limited reach and be effective only in sub-groups, even fomenting further opinion polarization. The introduction of dissenting information into a sub-group is disregarded and can produce a backfire effect, thus reinforcing the pre-existing opinions within the sub-group. Public health professionals should try to understand the contents of these echo chambers, for example by getting passively involved in such groups. Only then it will be possible to find effective ways of countering anti-vaccination thinking.
a) Bipartite network with 5 users and 4 pages, the links between them indicate that a user liked a page. (b) The projection of the bipartite network, the weights on the links between the pages show the number of users they have in common. (c) The community structure as detected with the algorithm FastGreedy. Nodes sharing the same color belong to the same community.
a) Bipartite network with 5 users and 4 pages, the links between them indicate that a user liked a page. (b) The projection of the bipartite network, the weights on the links between the pages show the number of users they have in common. (c) The community structure as detected with the algorithm FastGreedy. Nodes sharing the same color belong to the same community.
… 
alidation of the community partition.
alidation of the community partition.
… 
Probability Density Function (PDF) of the users' liking (left) and commenting (right) behavior in the manual communities (top) and the 2 largest communities detected with FastGreedy (bottom). The distribution of the users is bimodal for all cases, which indicates a strong polarization among the communities, that is, the majority of the users are active in only one community.
Probability Density Function (PDF) of the users' liking (left) and commenting (right) behavior in the manual communities (top) and the 2 largest communities detected with FastGreedy (bottom). The distribution of the users is bimodal for all cases, which indicates a strong polarization among the communities, that is, the majority of the users are active in only one community.
… 
Maximum number of unique pages that users with increasing levels of standardized lifetime (top) or standardized activity (bottom) interact with yearly (left), monthly (middle) and weekly (right) for each community. Users' lifetime corresponds to the standardized time difference between their latest and earliest liked post. Users' activity corresponds to the standardized number of likes given in their lifetime. Users display a tendency to like less pages when their lifetime and activity increases. The users who interact with the anti-vaccines community also consume a larger variety of pages than the pro-vaccines users. Grey shades are 95% CI of the fitted curve, indicating significant differences between the groups at any time.
Maximum number of unique pages that users with increasing levels of standardized lifetime (top) or standardized activity (bottom) interact with yearly (left), monthly (middle) and weekly (right) for each community. Users' lifetime corresponds to the standardized time difference between their latest and earliest liked post. Users' activity corresponds to the standardized number of likes given in their lifetime. Users display a tendency to like less pages when their lifetime and activity increases. The users who interact with the anti-vaccines community also consume a larger variety of pages than the pro-vaccines users. Grey shades are 95% CI of the fitted curve, indicating significant differences between the groups at any time.
… 
Number of active pages (left) and users (right) in each community. We define a page as active in a specific quarter if it made a post (bottom panel), received a like (middle panel) or comment (upper panel) in that period. We define a user as active in a community on a given quarter, if they gave a like (bottom panel) or comment (top panel) to any page of that community in that time.
+1
Number of active pages (left) and users (right) in each community. We define a page as active in a specific quarter if it made a post (bottom panel), received a like (middle panel) or comment (upper panel) in that period. We define a user as active in a community on a given quarter, if they gave a like (bottom panel) or comment (top panel) to any page of that community in that time.
… 
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Polarization of the Vaccination
Debate on Facebook
!
Ana!Lucía!Schmidt1,!Fabiana!Zollo2,!Antonio!Scala3!,Cornelia!Betsch3,!Walter!Quattrociocchi2!
1IMT!Alti!Studi!Lucca!
2Ca'!Foscari!University!of!Venice!
3ISC-CNR,!Rome!Italy!
4University!of!Erfurt!
Abstract(
Background+
Vaccine! hesitancy! has! been! recognized! as! a! major! global! health! threat.! Having! access! to! any! type! of!
information! in! social! media! has!been!suggested!as!a!potential!powerful!influence!factor! to! hesitancy.! Recent!
studies! in! other! fields! than! vaccin ation! show! that! access! to! a! wide! amount! of! content! through! the! Internet!
without! intermediaries! resolved!into! major! segregation! of! the! users! in! polarized! groups.! Users! select! the!
information!adhering!to!theirs!system!of!beliefs!and!tend!to!ignore!dissenting!information.!!
Objectives+
In!this!paper!we!assess!whether!there!is!polarization!in!Social!Media!use!in!the!field!of!vaccination.!!!
Methods+
We!perform!a!thorough!quantitative!analysis!on!Facebook!analyzing!2.6M!users!interacting!with!298.018!posts!
over! a! time! span! of! seven! years! and! 5! months.! We! used! community! detection! algorithms!to! automatically!
detect!the!emergent!communities!from! the! users’! activity!and!to!quantify!the! cohesiveness! over! time! of!the!
communities.!
Results+
Our!findings!show!that!content!consumption!about!vaccines!is!dominated!by!the!echo-chamber!effect!and!that!
polarization!increased!over!years.!Communities!emerge!from!the!users’!consumption!habits,!i.e.!the!majority!of!
users!only!consumes!information!in!favor!or!against!vaccines,!not!both.!!
Conclusion+
The!existence!of!echo-chambers!may!explain!why!social-media!campaigns!providing!accurate!information!may!
have!limited!reach,!may!be!effective!only!in!sub-groups!and!might!even!foment!further!polarization!of!opinions.!
The! introduction! of! dissenting! information! into! a! sub-group! is! disregarded! and! can! have! a! backfire! effect,!
further!reinforcing!the!existing!opinions!within!the!sub-group.!
Keywords+
Social!media,!vaccine!hesitancy,!network!analysis,!computational!social!science,!misinformation!
!
Introduction(
Undeterred!by!the!scientific!consensus! that! vaccines! are! safe! and! effective,! unsubstantiated!claims!doubting!
their!safety!still! occur! to! this!day.!Perhaps!the!most! famous!case!is!the! multiple!times!disproved! [1,2,3]!myth!
that! the! MMR! vaccine! causes! au tism.!However,! outbreaks! and! deaths!resulting! from! objections! to! vaccines!
continue!to!happen![4,5],!with!the! anti-vaccination!movement!gaining!media!attention!as!a!result.!Mandatory!
vaccination!policies!only!seem!to!foment!the!controversy![6].!
Since! 2013,! the! World! Economic! Forum! lists! massive! digital! misinformation! among!the! main! threats! to! our!
societies![7].!Recent!studies!outline!that!misinformation!spreading!is!a!consequence!of!the!shift!of!paradigm!in!
the! consumption! of! content! induced! by! the!advent! of! social!media.! Social! media! platforms!like! Facebook!or!
Twitter!have!created! a! direct!path!for!users!to! produce! and! consume! content,!reshaping!the!way!people!get!
informed![8-13].!
Like! for! other! misinformation! campaigns,! Facebook!provides! an! ideal! medium! for! the!diffusion! of! anti-
vaccination! ideas.!Users! can! access! a! wide! amount! of! information! and! narratives! and! selection! criteria! are!
biased! toward! personal! viewpoints! [14,15,16].! Online! users! select! information! adhering! to! their! system! of!
beliefs!and!tend!to!ignore!dissenting!information!and!to!join!polarized!groups!that!cooperated!to!reinforce!and!
frame! a! shared! narrative! [17,18,19].! The! interaction! with! content! dissenting! the! shared! narrative! is! mainly!
ignored!or!might!even!foment!segregation!of!users,!heated!debating!and!thus!bursting!polarization!of!opinions!
[20].!Such!a!scenario!is!not!limited!just!to!conspiracy!theories,!but!it!is!related!to!all!issues!that!are!perceived!as!
critical! by! the! users! such! as! geopolitics! and! health! [21].! This! effect! allows! for! the! emergence! of! polarized!
groups![12],!i.e.!clusters!of!users!with!opposing!views!that!rarely!interact!with!one!another.!!
In! this! paper!we! use! quantitative! analysis! to! understand! the! evolution! of! the! debate! about!vaccines! on!
Facebook,!taking!into! account!two!opposing!views:!anti-vaccines!and!pro-vaccines.!Considering!the!liking!and!
commenting! behavior! of! 2.6M!users! we! study! the! evolution! of! the! two! communities! over! time,! taking!into!
account!the! number! of! users,!the!number! of! pages,! and! the! cohesiveness! of! the! communities.!The! analyses!
confirm! the! existence! of! two! polarized! com munities.!Additionally,! we! find! evidence! that! selective! exposure!
plays!an!pivotal!role!in!the!way!users!consume! content!online.!The!two!communities!display!different!rates!at!
which! the! variety! of! news! sources!consu med! diminishes!for! increasing! levels! of! u ser! activity,! with! the! anti-
vaccines!community!being!more!engaged.!!
Data(Description(
Ethics(Statement(
The!data!collection!process!was!carried!out!using!the!Facebook!Graph!API![22],!which!is!publicly!available.!The!
pages!from! which! we! downloaded!data! are! public! Facebook! entities! and! can! be! accessed! by! anyone.!Users'!
content!contributing! to! such! pages! is!also!public! unless! users'! privacy! settings!specify!otherwise,!and!in!that!
case!their!activity!is!not!available!to!us.!
Data(Collection(
The!dataset!was!generated!through!requests!to!Facebook!for!pages!containing!the!keywords!!"##$%&,!!"##$%&'!
or!!"##$%"($)%!in!their!name!or!description.!We!then!filtered!the!raw!Facebook!data!in!order!to!include!only!the!
ones!relevant!for!the!study.!Inclusion!criteria!were!language!(English),!a!minimum!level!of!activity!(at!least!10!
posts),!date!of!the!posts!(between!1st!January!2010!to!31st!May!2017),!and!relation!of!the!page!to!vaccination.!
This!last!step!was!essential,!as!having!one!of!the!keywords!in!the!description!does!not!necessarily!mean! the!
page's!topic!is!about!vaccines.!Some!examples!of!those!false!positive!search!results!are!the!pages!*+&,-"##$%&'!
(an!UK!music!band),!and!./(+0/,12!"##$%&!(a!comedian).!
From!the!resulting!set!of! Facebook!pages!we!downloaded!all!posts!as!well!as!all!likes!and!comments!made!on!
those! posts.!Considering! the! content! of!the!posts!made! on! the! pages,! all! the! Facebook! pages! were! also!
manually!classified!by!two!raters!into!two!groups:!145!3/)4!"##$%&'!with!1,388,677!users!and!98!"%($4!"##$%&',
5$(+,1,277,170!users.!The!Cohen’s!kappa!inter-agreement!between!both!raters!is!0.966,!showing!nearly!perfect!
agreement.!
A!list!of!Facebook!pages!with!their!respective!community!label!and!a!breakdown!of!the!dataset!in!numbers!of!
posts,!likes,!likers,!comments,!commenters!and!users!can!be!found!in!the!Appendix.!
Preliminaries(and(Definitions(
The!likes!(or!comments)!given!by!users!to!the!posts!of!different!Facebook!pages!form!a!6$3"/($(&,%&(5)/7.!The!
6$3"/($(&,%&(5)/7!is! formed!by!a!set!of! users! and! a! set!of!pages!where! links! only! exist!between!a! user! and! a!
page!if!the!user!liked!(or!commented)!anything!on!that!page.!!
We!can!represent!the!bipartite!network!with!a!matrix!where!each!column!is!a!user!and!each!row!is!a!page,!and!
the!content!of!each!cell!equals!1!if!the!user!liked!at!least!one!post!of!that!page.!If!we!multiply!the!matrix!by!its!
transpose,!we!get!the!3/)8&#($)%,)9,(+&,6$3"/($(&,%&(5)/7.!This!new!matrix!will!have!a!row!and!column!for!each!
page,!and!the!content!of!each!cell!will!represent!the!number!of!common!users!between!the!2!pages!that!define!
that!cell,!that!is,!the!number!of!users!who!liked!any!post!on!both!pages.!The!same!method!can!also!be!applied!
considering!the!matrix!formed!by!the!users’!comments.!
For!illustration,!Figure!1!visualizes!a!simplified!example!of!a!bipartite!network!with!5!users!and!4!pages!and!the!
corresponding!projection.!
(
,
:$;0/&,<,=,>"?,@$3"/($(&,%&(5)/7,5$(+,A,0'&/',"%B,C,3";&'D,(+&,E$%7',6&(5&&%,(+&F,$%B$#"(&,(+"(,",0'&/,E$7&B,",3";&G,>6?,*+&,3/)8&#($)%,)9,
(+&,6$3"/($(&, %&(5)/7D,(+&,5&$;+(',)%,(+&, E$%7',6&(5&&%,(+&,3";&','+)5, (+&,%0F6&/,)9,0'&/',(+&H, +"!&,$%, #)FF)%G,>#?, *+&,#)FF0%$(H,
'(/0#(0/&,"',B&(&#(&B,5$(+,(+&,"E;)/$(+F,:"'(I/&&BHG,J)B&','+"/$%;,(+&,'"F&,#)E)/,6&E)%;,(),(+&,'"F&,#)FF0%$(HG,
Once! we! have! the! network! of! pages! linked! by! their! common! users! (Figure! 1b),! we! can! apply! different!
community!detection!algorithms!to!detect!group!of!pages!that!are!strongly!connected!(Figure!1c).!To!do!this!
we! apply! five! well! known! community! detection! algorithms:! FastGreedy1[23],! WalkTrap2[24],! MultiLevel3[25]!
and! LabelPropagation4[26].!Different! algorithms! are! used! as! they! allow! for! unsupervised! clustering,! i.e.,! no!
human!intervention,!and!they!each!have!different!approaches!to!detecting!of!communities!in!the!networks.!To!
compare!the! communities! detected! with! the! various!algorithms!we! use! standard! methods! that!compute!the!
similarity!between!different!community!partitions!by!considering!how!the!algorithms!assign!the!nodes!to!each!
community![27].!Due!to!its!speed!and!its!lack!of!parameters!in!need!of!tuning,!the!FastGreedy!algorithm!will!be!
the! main! reference! to! compare! against! the! partitions! resulting! from! the! application! of! other! community!
detection!algorithms.!
Results(and(Discussion(
Validation(of(the(Community(Partition(
In!order!to!validate!the!manual! partition! of! the! pages! into!two!communities! we!generated!the!projections!of!
the! bipartite! networks! considering! the! user! likes!and! the! user! comments.!We! then! applied!the! community!
detection!algorithms!to!extract!the!communities!of!pages!according!to!the!users'!behavior!and!compared!those!
to!the!expert-based!partitioning.!
Table! 2! shows! the! comparison! between! a! random! partition! of! the! pages,! the! manual! partition,! and! the!
FastGreedy! partition! against! those! resulting! from! the! different! algorithms.!We! can! see! that! the! manual!
classification!matches!well! against! the!unsupervised! approaches!and! that! the! FastGreedy! results! have! a! high!
agreement! with! the! other! algorithms.!This! indicates! that! the! users'! behavior! generates! well! defined!
communities! of! pages! and! that! these! communities! are! similar! to! the! anti-vaccines! and! pro-vaccines! as!
manually!tagged.!
*"6E&,<,=,-"E$B"($)%,)9,(+&,#)FF0%$(H,3"/($($)%G,
Graph&
Communities&
FastGreedy&
WalkTrap&
MultiLevel&
LabelProp.&
Likes&
Random&
0.496!
0.497!
0.495!
0.497!
Manual&
0.774!
0.721!
0.738!
0.714!
FastGreedy&
1!
0.935!
0.950!
0.901!
Comments&
Random&
0.497!
0.499!
0.495!
0.496!
Manual&
0.590!
0.610!
0.567!
0.570!
FastGreedy&
1!
0.909!
0.876!
0.824!
Note:! We! compared! a! random! partition!of! the! pages! into! communities,! the! manual! classification,!and ! the!FastGreedy!
classification!against!the!community!partitions!detected!with!the!different!community!detection!algorithms.!The!values!of!
the!comparison!range!from!0!to!1,!where!1!is!an!exact!match!and!0!is!no!match.!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
1!It!optimizes! the!modularity!score! in!a! greedy!manner!to!calculate!the!communities.!The!modularity!is! a!benefit!function!
that!measures!the!quality!of!a!particular!division!of!a!network!into!communities.!A!high!modularity!score!corresponds!to!a!
dense! connectivity! between! nodes! inside! a! cluster! and! sparse! connections! between! clusters.! This! algorithm! takes! an!
agglomerative! bottom-up! approach:! initially! each! vertex! belongs! to! a! separate! community! and,! at! each! iteration,! the!
communities!are!merged!in!a!way!that!yields!the!largest!increase!in!the!current!value!of!modularity.!!
2!It!exploits!the!fact!that!a!random!walker!tends!to!become!trapped!in!the!denser!parts!of!a!graph,!i.e.,!in!communities.!
3!It!uses! a!multi-level!optimization!procedure!for! the!modularity!score.!It!takes!a!bottom-up!approach!where!each!vertex!
initially!belongs!to!a!separate!community!and!in!each!step,!unlike!FastGreedy,!vertices!are!reassigned!to!a!new!community.!
4!It!uses!a!simple!approach!where!each!vertex!is!assigned!a!unique!label,!which!is!updated!according!to!majority!voting!in!
the!neighboring!vertices.!Dense!node!groups!quickly!reach!a!consensus!on!a!common!label.!
Thus,!the!pages!cluster!together!according!to!the!users'!activity.!In!a!next!step,!we!analyzed!the!polarization!of!
the!users.!
Polarization(
Assuming!that!a!user!u!has!performed!x!likes!on!community!C1!and!y!likes!on!community!C2,!we!calculate!the!
users’!polarization!as!ρ(u)!=!(x!−!y)/(x!+!y).!Thus,!a!user!u!for!whom!ρ(u)!=!−1!is!polarized!towards!C2,!whereas!a!
user! whose! ρ(u)! =! 1! is! polarized! towards!C1.! We! then! measure!the! pola rization! of! all! users! considering! the!
communities!they!commented!and!liked!content!on.!We!examine!two!partitions:!the!manual!classification!of!
pages,!pro-vaccine!and!anti-vaccine,!and!the!two!biggest!communities!as!detected!with!FastGreedy,!C1!and!C2.!
Figure!2!shows!the!Probability!Density!Function!(PDF)!of!ρ(u)!for!all!users!who! have! given! at! least! 10! likes! in!
their!lifetime.!The!PDF!for!the!polarization!of!all!users!is!sharply!bi-modal,!that!is,!the!majority!of!the!users!are!
either! at! -1!or! at! 1.!This! indicates! a!strong! polarization! among! the! communities,! that! is,! the! majority!of! the!
users!are!active!either!in!the!pro-vaccines!or!anti-vaccines!community,!not!both.!
!
:$;0/&,K,4, L/)6"6$E$(H,1&%'$(H,:0%#($)%,>L1:?,)9,(+&,0'&/'M,E$7$%;,>E&9(?,"%B,#)FF&%($%;,>/$;+(?,6&+"!$)/,$%,(+&,F"%0"E,#)FF0%$($&',>()3?,
"%B,(+&,K,E"/;&'(,#)FF0%$($&',B&(&#(&B,5$(+,:"'(I/&&BH,>6)(()F?G,*+&,B$'(/$60($)%,)9,(+&,0'&/',$',6$F)B"E,9)/,"EE,#"'&'D,5+$#+,$%B$#"(&',",
'(/)%;,3)E"/$N"($)%,"F)%;,(+&,#)FF0%$($&'D,(+"(,$'D,(+&,F"8)/$(H,)9,(+&,0'&/',"/&,"#($!&,$%,)%EH,)%&,#)FF0%$(HG,
Selective(Exposure(
Facebook!users!differ!in!the!time!they!spend!with!the!pages!and!in!how!frequently!they!interact!with!the!pages.!
The!E$9&($F&!of!a!user!is!defined!as!the!period!of!time!where!the!user!started!and!stopped!interacting!with!the!
included!set!of!pages.!It!can!be!approximated!by!the!time!difference!between!a!user’s!latest!and!earliest!liked!
post.!The! total!number!of!likes! per!user!is!a! good!proxy!for!the! user’s!"#($!$(H,!i.e.,!their!level!of!engagement!
with!the!Facebook!pages.!These!two!measures!provide!important!insights!on!how!users!consume!information!
in!each!echo!chamber!as!demonstrated!in!the!following!analyses.!
Figure!3! shows!the!number!of! unique!pages!users!from! the!anti-vaccines!(red)!and!pro-vaccines!communities!
(blue)!interact!with,!considering!increasing!levels!of!lifetime!and!activity!for!different!time!windows!(yearly!left,!
monthly!middle!and!weekly!right!panel).!For!a!comparative!analysis,!we!standardized!lifetime!and!activity!to!
range!between!0!and!1,!both!over!the!entire!user!set!of!each!community,!and!the!number!of!pages.!
Note!that!for!both!communities,!users!usually!interact!with!a!small!number!of!Facebook!pages.!Longer!lifetime!
and!higher! levels! of! activity!correspond! with!less! number!of!pages!being!consumed.!This!suggests!that!more!
time! on! Facebook! corresponds! to ! a! smaller! variety! of! sources! being! consumed.! This! is! consistent! with! [12]!
showing!that! content! consumption! on! Facebook! is! dominated! by! selective! exposure!and,! over! time,! users!
personalize!their!information!sources!accordingly!with!their!tastes!which!results!in!a!smaller!number!of!sources.!
Pro-vaccine! users! interact! with! M! =! 1.42! pages! (SD! =! 0.79),! anti-vaccine! users! with! 2.45! (SD! =! 2.13).! This!
difference!is!displayed!in!Figure!3:!users!in!the!anti-vaccines!community!(red!line)!consume!information!from!a!
more! diverse! set! of! pages! than! those! in! the! pro-vaccines!community,! regardless! of! the! time! window!
considered.!Grey! shades! are! 95%! CI!of! the! local! regression! of! the! data,! indicating! significant! differences!
between! the! groups! at! any! time.! So! while! there! is! a! natural!tendency! of! users! to! confine! their! activity! to! a!
limited!set! of! pages! [12],! the!two!communities!display!different! rates! of!selective! exposure.!The!anti-vaccine!
community!shows!more!commitment!to!the!consumption!of!their!posts.!
!
:$;0/&, O, 4, P"Q$F0F, %0F6&/,)9, 0%$R0&, 3";&', (+"(, 0'&/',5$(+, $%#/&"'$%;, E&!&E',)9, '("%B"/B$N&B, E$9&($F&,>()3?, )/, '("%B"/B$N&B,"#($!$(H,
>6)(()F?, $%(&/"#(, 5$(+, H&"/EH, >E&9(?D, F)%(+EH, >F$BBE&?, "%B, 5&&7EH, >/$;+(?, 9)/, &"#+, #)FF0%$(HG, S'&/'M, E$9&($F&, #)//&'3)%B', (), (+&,
'("%B"/B$N&B,($F&,B$99&/&%#&,6&(5&&%,(+&$/,E"(&'(,"%B,&"/E$&'(,E$7&B,3)'(G,S'&/'M,"#($!$(H,#)//&'3)%B',(),(+&,'("%B"/B$N&B,%0F6&/,)9,E$7&',
;$!&%,$%,(+&$/,E$9&($F&G,S'&/',B$'3E"H,",(&%B&%#H,(),E$7&,E&'',3";&',5+&%,(+&$/,E$9&($F&,"%B,"#($!$(H,$%#/&"'&'G,*+&,0'&/',5+),$%(&/"#(,5$(+,
(+&,"%($4!"##$%&',#)FF0%$(H, "E'), #)%'0F&, ",E"/;&/,!"/$&(H, )9,3";&',(+"%,(+&, 3/)4!"##$%&', 0'&/'G,I/&H,'+"B&', "/&,TAU,VW,)9, (+&,9$((&B,
#0/!&D,$%B$#"($%;,'$;%$9$#"%(,B$99&/&%#&',6&(5&&%,(+&,;/)03',"(,"%H,($F&G,
Growth(of(the(Communities(over(Time(
We! also! analyzed! the! growth! of! the! two! communities! over! time,! considering! the! variety! of! pages! and! the!
number!of! users!that!interact!with!them.!Figures!4!shows!the!evolution!of!the!communities!over!the! years! in!
quarterly!increments.!
The!left!panel!plots!the!number! of! active!pages! in! each! community.!We!define!a!page! as! active! in! a! specific!
quarter! if! it! made! a! post! (bottom! panel),! received! a! like! (middle)! or! comment! in! that! period!(upper! panel).!
Overall,!the!number!of!active!pages!in!both!communities!increases!at!similar!rates,!with!slight!variations!when!
we!consider!the!different! types!of!action!that! marks! a!page!as!active.!If!we!use!the!pages'!posting! activity! or!
the!likes!they!received!to!determine!whether!they!were!active!in!a!given!quarter,!we!can!see!that,!from!2013,!
the!pro-vaccine!community!consistently! tends! to!show!a!higher!number!of! active!pages!than!the!anti-vaccine!
community!(interaction!effect!in!a!MANOVA!with!sentiment!(pro,!anti)!and!time!(until!2012Q4!vs.!following)!as!
factors!and!posts!and!likes!as!dependent!variables!F(2,55)!=!2.708,!p!=!0.076;!eta2!=!0.09;!both!main!effects!are!
highly! significant).! On! the! other! hand,! if! we! focus!on! the! comments,! the! anti-vaccines!community! shows! a!
boost! in! activity! starting! in! 2015!(interaction! effect! in! an! ANOVA! with! sentiment! (pro,! anti)! and! time! (until!
2014Q4!vs.! following)!as!factors!and!comments!as!dependent!variable!F(1,56)! =!5.053,! p!=!0.029;! eta2!=! 0.08;!
both!main!effects!are!significant).!!
The!right! panel!plots!the!number!of! active! users! in!each!community.!We!define!users!as!active!if!they!gave!a!
like! (or! comment)! to! any! page! of! that! community! in! the! given! quarter.! The! plot! shows! that! while! both!
communities!gain!users!throughout!the!entire! period,!the!anti-vaccines!community!has,!until!the!end!of!2015!
and!beginning!of!2016,! more! active! users! than!the!pro-vaccines!community.!After!that,!this! relation!reverses!
(interaction!effect!in!a!MANOVA!with!sentiment!(pro,!anti)!and!time!(until!2015Q4!vs.!following)!as!factors!and!
comments! and! likes! as! dependent! variables! F(2,55)! =! 12.218,! p! <! 0.001;! eta2!=! 0.31;! both ! main! effects! are!
highly!significant).!
!
!
:$;0/&,C,=,J0F6&/,)9,"#($!&,3";&',>E&9(?,"%B,0'&/',>/$;+(?,$%,&"#+,#)FF0%$(HG,X&,B&9$%&,",3";&,"',"#($!&,$%,",'3&#$9$#,R0"/(&/,$9,$(,F"B&,",
3)'(,>6)(()F,3"%&E?D,/&#&$!&B,",E$7&,>F$BBE&,3"%&E?,)/,#)FF&%(,>033&/,3"%&E?,$%,(+"(,3&/$)BG,X&,B&9$%&,",0'&/,"',"#($!&,$%,", #)FF0%$(H,
)%,",;$!&%,R0"/(&/D,$9,(+&H,;"!&,",E$7&,>6)(()F,3"%&E?,)/,#)FF&%(,>()3,3"%&E?,(),"%H,3";&,)9,(+"(,#)FF0%$(H,$%,(+"(,($F&G!!
!
Another!important!factor!to!consider!is!the!cohesiveness!of!the!pro-vaccines!and!anti-vaccines!communities.!In!
order! to! analyze! whether! the! growth! of! the! communities! depends! on! the! emergence! of! isolated! pages!or!
whether!it!grows!steadily,!we!split!the!projections!of!the!bipartite!likes!and!comments!graph!by!the!community!
of!the! pages.!This!results!in!4!sub-graphs,!each!containing! the! pages!of!one!community,!pro-vaccines!or! anti-
vaccines,! and! the! common! users! that! linked! them! considering! the! likes! or! the! comments.! We! can! then!
calculate!the!fragmentation!of!each!community!by!applying!the!community!detection!algorithms!and!obtaining!
their!partition.!
Figure!5!shows!the!number!of!pages!of! the! biggest! sub-community! of! the! anti-vaccines!(left)! or! pro-vaccines!
communities! (right)! in! a! given! quarter,! that! is,! the! largest! connected! component! found! with! the! different!
community!detection!algorithms.!The!black!line!represents!the!total!number!of!pages!in!the!sub-graphs!in!that!
quarter.!It! marks!the!maximum! possible! size!for!the!largest!connected! component!to!take!in!that!moment!in!
time.!The!closer!the!size!of!the!largest!connected!component!is!to!the!total!number!of!pages!in!the!system,!the!
more!tightly!linked!that!community!is!in!that!moment!in!time.!
!
:$;0/&, A, 4, Y$N&, )9, (+&, E"/;&'(, #)%%&#(&B, #)F3)%&%(, 5$(+$%, (+&, '&(, )9, 3";&', (";;&B, "', "%($4!"##$%&', "%B, 3/)4!"##$%&', )!&/, ($F&D,
#)%'$B&/$%;,!"/$)0',#)FF0%$(H,B&(&#($)%,"E;)/$(+F'G,*+&,6E"#7, E$%&,/&3/&'&%(',(+&,()("E,%0F6&/,)9,3";&', )!&/,($F&,$%,(+&,"%($4!"##$%&',
"%B,3/)4!"##$%&', #)FF0%$($&'D,(+"(, $'D, (+&, F"Q$F0F,3)''$6E&,'$N&, 9)/, (+&,E"/;&'(,#)%%&#(&B, #)F3)%&%(,$%,(+"(, F)F&%(, $%, ($F&G,*+&,
;/"3+,'+)5',(+"(,(+&,"%($4!"##$%&',#)FF0%$(H,;/)5',#)+&'$!&EHD,5$(+,(+&,%&5,3";&',8)$%$%;,(+&,"E/&"BH,&Q$'($%;, ;/)03,)9, 3";&'D,5+$E&,
(+&,3/)4!"##$%&',#)FF0%$(H,;/)5',$%,",F)/&,9/";F&%(&BD,$%B&3&%B&%(,5"HG,,
The!plots!show! that!in! the!anti-vaccines! community! the! number! of! pages! in! the! largest! component!remains!
close!to!the!total!number!of!pages!in!the!system.!In!the!case!of!the!pro-vaccines!sub-graphs,!however,!the!size!
of!the!largest!community!does!not!increase!closely!with!the!number!of!pages!in!the!system.!This!signifies!that!
the!anti-vaccines!community!grows!in!a!more!cohesive!manner,!with!pages!tightly!linked!by!their!users'!activity,!
while!the!pro-vaccines!community!is!more!fragmented.!!
Discussion((
By!means!of!quantitative!analysis!of!Facebook!likes!and!comments!we!validated!the!existence!of!two!opposing!
narratives! regarding! the! vaccination! debate! on! Facebook.! We! show! that! the! communities! emerge! from! the!
users’!consumption!habits!and!that!users!are!highly!polarized,!that!is,!the!majority!of!users!only!consumes!and!
produces!information!in!favor!or!against!vaccines,!not!both.!
We!also!showed!that!both!narratives!are!subjected!to!selective!exposure,!and!that!the!more!active!a!user!is!on!
Facebook!the!smaller! is!the!variety!of! sources! they!tend!to!consume.!We!note,! however,! that!the!users!from!
the!anti-vaccination!community!consume!more!sources!compared!to!the!pro-vaccine! users.! This! is!consistent!
with! the! results! of! previous! studies! [14]!that! show! that! people! in! conspiracy-like! groups! show! higher!
engagement!with!the!community.!
We!also!analyzed!the!communities’!evolution!over!time.!While!the!pro-vaccine!pages!are!generally!more!active,!
the!anti-vaccine!pages!concentrate!the!majority!of!the!debate,!receiving!more!comments!from!users.!We!show!
that!the!anti-vaccine!community!had!a!more!active!user!base!until!the!end!of!2015,!where!the!activity!seems!to!
stall.!This!matches!with!the!outbreak!of!measles!at!Disneyland![4],!which!put!the!anti-vaccination!movement!in!
the!spotlight! and!gained!the!attention!of!mainstream!media![28-34].!Further!studies!are!needed!to!determine!
the!reason!for!this!stagnation.!
Finally,!we! show! that! while!both!narratives!have!gained! attention! on! Facebook!over!time,!anti-vaccine!pages!
display!a!more!cohesive!growth!(i.e.!pages!are!liked!by!the!same!people),!while!the!pro-vaccine!pages!seem!to!
grow!in!a!highly!fragmented!fashion!(i.e.!pages!are!liked!by!different!people).!
Limitations((
The! data! collection! process! was! done! the! 5th! of! June! 2017! and!represents! a! snapshot! of! the! pages,! posts,!
comments!and!likes!available!at!the!time.!Pages,!posts,!likes!and!comments!that!were!made!in!the!downloaded!
period!(1st! January! 2010!to!31st!May!2017)! and! were! removed!before!the!download!date!are!not!present!in!
the! dataset.! The! data! only! includes! the! likes! and! comments! by! users! whose! privacy! settings! allowed! pub lic!
access!to!their!activity!on!public!pages!on!the!download!date.!!
Conclusions((
Facebook! allows! echo-chambers! to! emerge,! in! which! pro-! and! anti-vaccination! attitudes! polarize! the! users.!
Social!media!campaigns!that!advocate! for!vaccination!and!provide!accurate!information!should!expect!to!only!
reach!pro-vaccination!users!as!there!is!nearly!no!interaction!between!the!groups.!Overall,!social!media!seem!to!
be!a! powerful!promoter!of!different!sentiments!about!vaccination!and!therefore!it!is! likely! that! it!contributes!
to!vaccine!hesitancy.!!
Appendix(
*"6E&,K,4,1"("'&(,1&'#/$3($)%G,
!
Anti-vaccines!
Pro-vaccines!
Pages!
98!
145!
Posts!
189,759!
108,259!
Likes!
12,696,440!
11,459,295!
Likers!
1,145,650&
1,325,511!
Comments!
1,351,839!
749,209!
Commenters!
271,598!
146,196!
Users!
1,277,170!
1,388,677!
Note:!The!posts,!likes!and! comments!are! considered!pro!or!anti!vaccines!if!they!were!made!on!a!page! classified!as!such.!
Likers! is! the! number! of! unique! users! who! have! given! at! least! one! like! to! the! community.! Commenters! is! the! unique!
number!of!users!who!have!given!at!least!one!comment!to!the!community.!Users!is!the!number!people!who!have!given!at!
least!a!like!or!a!comment!to!the!community.(
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Prior to the COVID-19 pandemic, the World Health Organization named vaccine hesitancy as one of the top 10 threats to global health. The impact of hesitancy on the uptake of human papillomavirus (HPV) vaccines was of particular concern, given the markedly lower uptake compared to other adolescent vaccines in some countries, notably the United States. With the recent approval of COVID-19 vaccines, coupled with the widespread use of social media, concerns regarding vaccine hesitancy have grown. However, the association between COVID-related vaccine hesitancy and cancer vaccines such as HPV is unclear. To examine the potential association, we performed two reviews using Ovid Medline and APA PsychInfo. Our aim was to answer two questions: (1) Is COVID-19 vaccine hesitancy, intention, or uptake associated with HPV or hepatitis B (HBV) vaccine hesitancy, intention, or uptake? and (2) Is exposure to COVID-19 vaccine misinformation on social media associated with HPV or HBV vaccine hesitancy, intention, or uptake? Our review identified few published empirical studies that addressed these questions. Our results highlight the urgent need for studies that can shift through the vast quantities of social media data to better understand the link between COVID-19 vaccine misinformation and disinformation and its impact on uptake of cancer vaccines.
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... That these echo chambers exist, particularly in social media, is now well established (e.g. Del Vicario et al., 2016;Schmidt et al., 2018). Whether echo chambers increase polarization and reduce the likelihood of optimal public choice is still uncertain (Bail et al., 2018;Jann and Schottmüller, 2018). ...
What can egocentric network measures contribute to stated preference analyses? An exploration
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  • Aug 2023
While economics has long recognized concern for others (e.g. altruism and bequest motives), explicit inclusion of social network metrics in non market valuation models is relatively recent. Social network effects on willingness to pay can propagate through the entire network and bias willingness to pay (WTP) estimates. However, social networks are complex systems of relationships between individuals, and measuring them can be difficult. We explored the potential for egocentric social network (ESN) measures to help explain variations in preference for the status quo. Using simulated random networks, we demonstrate that respondents more likely to choose an alternative to the status quo are part of more dense ESNs. A strong influence of an attitude toward the impact of economic development on the environmental goods and services is consistent with network structure and preference for environmental improvements being jointly determined.
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... A similar study conducted before the epidemic found similar results, as well as that defenders of vaccines tend to be retweeted by a greater number of users and that mainstream media occupy an important share of discussions [48]. Several recent studies on social media content suggest that the balance between defenders and critics of vaccines might have tipped in favor of the former [48,73,74]. Part of the explanation for the limited association between preference for social media and VH could therefore be a surge in pro-vaccination mobilization on social media and changes made by platforms to limit the spread of misinformation. ...
The Influence of Social Media and Institutional Trust on Vaccine Hesitancy in France: Examining Direct and Mediating Processes
Article
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  • Aug 2023
Vaccine hesitancy (VH) remains an ongoing challenge in French society. This project explored how institutional trust and preference for information via social media (PISM) drive hesitancy. Across a large, nationally represented population, our findings show that PISM and trust are strongly correlated measures, with both independently predicting VH. Subsequent mediation tests show that social media operates as primarily an indirect contributor to VH through trust. Additional tests involving VH and non-VH typologies revealed that institutional trust consistently predicts greater general support for vaccines and reduced distrust in vaccination. Conversely, PISM directly drives vaccine distrust, with its impact on non-hesitancy fully mediated by institutional trust. Overall, these findings point to the relevance for researchers and public health deciders to address the nature by which people utilize social media information resources and how that interacts with levels of trust for national institutions.
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Did COVID-19 Blur Partisan Boundaries? A Comparison of Partisan Affinity and Source Heterophily in Online Alternative News-Sharing Networks Before and During the COVID-19 Pandemic
Article
  • Aug 2023
This study explores partisan and group heterophily within cross-platform online communities that share alternative news media content in Denmark, Sweden, Germany, and Austria. The analysis is related to the emergence of anti-systemic cross-partisan counter-publics in Europe that have gained momentum with the outbreak of COVID-19 and the subsequent resistance against government restrictions. Comparing two periods (before and after the outbreak of COVID-19), we investigate whether these developments foster cross-partisan information sharing in online communities that form around right-wing, left-wing, and anti-systemic alternative news media content. Drawing on a network-analytical approach, we study networks formed around URL sharing of alternative news content across Facebook, Instagram, Twitter, Reddit, Telegram, TikTok, YouTube, and VKontakte. Data include 30 million social media posts from January 2019 to September 2021. The results show that overall source heterophily in online alternative news networks increases slightly with the COVID-19 pandemic, mainly due to the increased proliferation of anti-system news. This increase is, however, not an expression of a more profound collapse of bi-partisan, left-right cleavages and is contingent on country contexts. Except for the time of the initial outbreak, the overall sharing of COVID-19-related content tends to increase rather than decrease partisan homophily. Finally, the results show that non-bi-partisan, anti-system media have had a significant effect on alternative media information ecosystems during the COVID-19 pandemic.
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Anti‐vaccine Movement (United States)
Chapter
  • Sep 2022
The anti‐vaccine movement is arguably one of the more concerning social movements to have surfaced during the first two decades of the current century. Opposition to vaccination is particularly worrisome for its actual and potential impacts to public health, including increased frequency and severity of outbreaks, heightened virus positivity and death rates, and threats to herd immunity. While public reaction against vaccination regimens is hardly a new phenomenon, rarely if ever has such resistance congealed into the type of potent and widespread movement seen today. This resistance has been most pronounced in countries where vaccines are most readily available, particularly for COVID‐19, as in the European Union countries, Russia, and the United States, which is the primary focus of this entry. Moreover, the rise in vaccine skepticism, particularly in the US, is representative of a deepening fault line in the twenty‐first century marked by political polarization, competing social realities, and an erosion of institutional trust.
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Vaccine Misinformation on Social Media: Historical Contexts, Lessons Learned, and Paths Forward
Chapter
  • Aug 2023
In 2019 the World Health Organization named vaccine hesitancy a top-10 threat to global health (World Health Organization, 2019). To understand the historical roots of this phenomenon and its contemporary implications, this chapter will begin with the history of vaccine development and policy for multiple infectious diseases and an overview of the growth of the anti-vaccine movement. Next, we will place findings from research on vaccine misinformation on social media into a broader historical framework. Finally, we will discuss how applying a historical perspective can help counter the impact and spread of vaccine misinformation, thus improving vaccine education, promotion, and policy.
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Parents' Decision-making Experience in Choosing the MMR Vaccine in Banten, Indonesia
Article
  • Aug 2023
Research has extensively studied parental vaccination decision-making drivers and barriers. The most powerful predictors of vaccination actions include the understanding of the risks posed by the disease; and the side effects of vaccination; vaccine beliefs and attitudes; and their effectiveness and safety concerns. Thus, this study aimed to explore the parents decision-making experience in choosing MMR vaccine in Banten, Indonesia. In qualitative study, a purposeful sampling process was used to identify parents with a variety of expected MMR decisions: (1) accept MMR on time, (2) accept MMR late, (3) receive one or more individuals, (4) obtain no MMR or individuals. A qualitative quality analysis was used to interpret the transcribed text. A total of 25 participants from 5 different FGDs were included in this study. This qualitative interview resulted in 4 themes, namely: healthy life, own health perceptions, disease history, perceived severity, and susceptibility of vaccine-preventable illnesses. Research on the MMR vaccination should move a step forward and include studies looking at similarities and differences in the factors predicting parents’ intention to follow MMR vaccination recommendations by comparing parents of very young children, being the primary target group of MMR vaccination campaigns and interventions, with parents of adolescent children. Keywords: decision process, MMR vaccine, qualitative study
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"Wake up and think of the children!": The ambivalent relationship between motherhood, femininity and anti-vaccination
Chapter
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  • Jun 2023
Anti-vaccination sentiments have grown strong in public discourse in recent decades and especially during the Covid-19 pandemic, as online environment has proved to be the fertile setting for spreading conspiracy theories and false news. Anti-vaccine groups are using social networks to spread dubious health information, creating their own content without any evidence to confuse users who access their pages (Ortiz-Sánchez, Velando-Soriano et.al, 2020). Recent surveys found men were more likely to take the Covid-19 vaccine, compared to women (National Geographic survey, Gallup poll, Pew Survey, etc.), whilst existing studies show that the "vast majority" of people commenting, sharing, and liking anti-vaccination information on Facebook are women. Therefore, it is essential to comprehend, how notions about femininity and motherhood relate to decisions about vaccination.
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Opinion dynamics on interacting networks: Media competition and social influence
Article
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  • May 2014
The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.
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Homophily and polarization in the age of misinformation
Article
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  • Oct 2016
The World Economic Forum listed massive digital misinformation as one of the main threats for our society. The spreading of unsubstantiated rumors may have serious consequences on public opinion such as in the case of rumors about Ebola causing disruption to health-care workers. In this work we target Facebook to characterize information consumption patterns of 1.2 M Italian users with respect to verified (science news) and unverified (conspiracy news) contents. Through a thorough quantitative analysis we provide important insights about the anatomy of the system across which misinformation might spread. In particular, we show that users’ engagement on verified (or unverified) content correlates with the number of friends having similar consumption patterns (homophily). Finally, we measure how this social system responded to the injection of 4,709 false information. We find that the frequent (and selective) exposure to specific kind of content (polarization) is a good proxy for the detection of homophile clusters where certain kind of rumors are more likely to spread.
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Debunking in a world of tribes
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Social media aggregate people around common interests eliciting collective framing of narratives and worldviews. However, in such a disintermediated environment misinformation is pervasive and attempts to debunk are often undertaken to contrast this trend. In this work, we examine the effectiveness of debunking on Facebook through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users usually consuming proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook US interact with specific debunking posts. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. However, both groups interact similarly with the information within their echo chamber. Then, we measure how users from both echo chambers interacted with 50,220 debunking posts accounting for both users consumption patterns and the sentiment expressed in their comments. Sentiment analysis reveals a dominant negativity in the comments to debunking posts. Furthermore, such posts remain mainly confined to the scientific echo chamber. Only few conspiracy users engage with corrections and their liking and commenting rates on conspiracy posts increases after the interaction.
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Barriers of Influenza Vaccination Intention and Behavior – A Systematic Review of Influenza Vaccine Hesitancy, 2005 – 2016
Article
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Background: Influenza vaccine hesitancy is a significant threat to global efforts to reduce the burden of seasonal and pandemic influenza. Potential barriers of influenza vaccination need to be identified to inform interventions to raise awareness, influenza vaccine acceptance and uptake. Objective: This review aims to (1) identify relevant studies and extract individual barriers of seasonal and pandemic influenza vaccination for risk groups and the general public; and (2) map knowledge gaps in understanding influenza vaccine hesitancy to derive directions for further research and inform interventions in this area. Methods: Thirteen databases covering the areas of Medicine, Bioscience, Psychology, Sociology and Public Health were searched for peer-reviewed articles published between the years 2005 and 2016. Following the PRISMA approach, 470 articles were selected and analyzed for significant barriers to influenza vaccine uptake or intention. The barriers for different risk groups and flu types were clustered according to a conceptual framework based on the Theory of Planned Behavior and discussed using the 4C model of reasons for non-vaccination. Results: Most studies were conducted in the American and European region. Health care personnel (HCP) and the general public were the most studied populations, while parental decisions for children at high risk were under-represented. This study also identifies understudied concepts. A lack of confidence, inconvenience, calculation and complacency were identified to different extents as barriers to influenza vaccine uptake in risk groups. Conclusion: Many different psychological, contextual, sociodemographic and physical barriers that are specific to certain risk groups were identified. While most sociodemographic and physical variables may be significantly related to influenza vaccine hesitancy, they cannot be used to explain its emergence or intensity. Psychological determinants were meaningfully related to uptake and should therefore be measured in a valid and comparable way. A compendium of measurements for future use is suggested as supporting information.
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Echo Chambers: Emotional Contagion and Group Polarization on Facebook
Article
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  • Jun 2016
Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups -- i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models -- i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities' emotional behavior is affected by the users' involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.
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The spreading of misinformation online
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Significance The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size.
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Using Behavioral Insights to Increase Vaccination Policy Effectiveness
Article
Full-text available
  • Oct 2015
Even though there are policies in place, and safe and effective vaccines available, almost every country struggles with vaccine hesitancy, i.e., a delay in acceptance or refusal of vaccination. Consequently, it is important to understand the determinants of individual vaccination decisions in order to establish effective strategies to support the success of country-specific public health policies. Vaccine refusal can result from complacency, inconvenience, a lack of confidence, and a rational calculation of pros and cons. Interventions should therefore be carefully targeted to focus on the reason for non-vaccination. We suggest that there are several interventions that may be effective for complacent, convenient, and calculating individuals while interventions that might be effective for those who lack confidence are scarce. Thus, efforts should be concentrated on motivating the complacent, removing barriers for those for whom vaccination is inconvenient, and adding incentives and additional utility for the calculating. These strategies might be more promising, economic, and effective than convincing those who lack confidence in vaccination.
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Detrimental effects of introducing partial compulsory vaccination: Experimental evidence
Article
  • Jun 2016
During outbreaks of vaccine-preventable diseases, compulsory vaccination is often discussed as a last resort to counter vaccine refusal. Besides ethical arguments, however, empirical evidence on the consequences of making selected vaccinations compulsory is lacking. Such evidence is needed to make informed public health decisions. Objective. To assess the effect of partial compulsory vaccination on the uptake of other voluntary vaccines. Method. In an incentivized behavioral vaccination game, N = 297 participants were randomized to the compulsory vaccination intervention or voluntary vaccination control group, which determined the decision architecture of a first decision. The critical second decision was voluntary for all participants. Results. Compulsory vaccination increased the level of anger among individuals with a rather negative vaccination attitude, whereas voluntary vaccination did not. This led to a decrease in vaccination uptake by 39% in the second voluntary vaccination. Conclusion. Making only selected vaccinations compulsory can have detrimental effects on the vaccination program by decreasing the uptake of voluntary vaccinations. As this effect occurred especially for vaccine hesitant participants, the prevalence of vaccine hesitancy within a society will influence the damage of partial compulsory vaccination.
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