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APPROACHES OF DATA ANALYSIS: Networks generated through Social Media
NOVAUniversity,LisbonPhDcandidateatUTAus;nIPortugal
@jannajoceli˚thesocialplaCorms.wordpress.com
SMART Data Sprint 23-27 January 2017
Janna Joceli C. de Omena
Omena, 2017. Approaches of Data Analysis
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Black Mirror (2016), Nosedive
We cannot speak of data analysis without considering the logic, features, grammars or the “ways of being” of social media.
Social Media Platforms
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
channels of connectivity and sociability that must be taken as techno-cultural constructs
objects of study +
methodological process
Social phenomena
+ means of media
critique (Rogers, 2015)
“structure of feelings”
(Papacharissi, 2015)
alternative form of journalism (Poell & Borra, 2012;
Cardoso & Fátima, 2013; Malini et. al., 2014;)
Concepts
Programmability Popularity Connectivity Datification (Dijck & Poell, 2013)
Logic
Posts URLs Tweets Comments Replies Hashtags Location Memes Links Channels
Grammars’ of Action (Agre,1994)
Lack of neutrality
Omena, 2017. Approaches of Data Analysis
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
Social Media Studies with Digital Methods
Machine-readable interfaces (Berlind, 2015)
«Give third-parties access to data and
functionalities that belong to the platform»
Omena, 2017. Approaches of Data Analysis
What digital objects are available for data extraction?
What media content can be part of my analysis?
How far back in time can data be retrieved?
What are the standard output files?
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Social Media Studies with Digital Methods
Omena, 2017. Approaches of Data Analysis
PagesGroups
PageNetworkSharedLinksListofEvents
UsersKeywordsHashtagsLoca;on
Data extraction*
Media content and digital objects
VideosChannels Hashtags
Loca;onsFollowNetwork Hashtags
Posts(textualcontent:
Cap;on,comments,replies)(visualcontent:videos,photos,memes)
PageLikeNetworkGroupsNetwork
EventsURLs
Tweets(textualcontent:
Tweettext,men;ons,replies)(visualcontent:
videos,photos,gifs)
GeotagsURLs
VideoInfo(basicinfoanstats,comments,
commentsauthors,interac;onsbetween
usersinthecommentsec;ons)VideoListandNetwork
ChannelInfoandNetwork
*Tools:Netvizz,Twitonomy,DMI-TCAT,YouTubeDataTools,VisualTagnetExplorer,TumblrTool
MediaandusersInfo(textualcontent:cap;on,tags,usersbio)
(visualcontent:photoandvideo)
(basicstats)Co-TagNetwork
Posts(textualcontent:
summary,cap;on,tags,usersbio)(visualcontent:photoandvideo)
(basicstats)Co-TagNetwork
Output files
CSV.,TAB.GDF.,XML.,interac;vechart
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
Social Media APIs (limited data access)
Pages or Open Groups Data = months or years
Events = list of upcoming events (not past events)
Twitter Search API = hours or few days (e.g. it returns to Twitonomy a sample of up to 3,100 tweets)
Hashtag or locale based extraction = months or years (e.g. results will depend on the popularity of a hashtag and
the adoption of the tag itself by users)
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Network Analysis on Social Media
Omena, 2017. Approaches of Data Analysis
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
Mypersonalfriendshipconnec;onsonFacebookinJanuary2014.Extrac;onSo`ware:Netvizz.Visualiza;onso`ware:Gephi.
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Network Analysis on Social Media
• Explore associations
• Identify unexpected connections
• Key or marginal actors
• Mapping:
Alliances and oppositions (Bounegru et.al, 2016)
Program and anti-program (Rogers, 2017, forthcoming)
Supporters and non-supporters (Omena, 2017, forthcoming)
• Clusters and weak/hidden ties
• Authority
• Activity (nodes properties) and weight of
connections (edges properties)
Omena, 2017. Approaches of Data Analysis
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Choose a node attribute:
Omena, 2017. Approaches of Data Analysis
• Degree = total n. of connections out-degree = activity
in-degree = popularity
• People talking about = Debate (Facebook parameter: https://developers.facebook.com/docs/graph-api/reference/v2.1/page)
• Modularity = Clusters (community detection algorithm) R. Lambiotte, J.-C. Delvenne, M. Barahona Laplacian (2009). Dynamics and Multiscale
Modular Structure in Networks.
• Betweenness or Bridgeness Centrality =
Influence/Discriminate between local centers
and global bridges (key players) (Ulrik Brandes (2001).A Faster Algorithm for Betweenness Centrality, in Journal of Mathematical Sociology 25(2):163-177); (Pablo Jesen et. al (2015). Detecting global
bridges in networks. Journal of Complex Networks. Doi:10.1093/comnet/cnv022)
• PageRank = Authority/Importance (pagerank algorithm) Sergey Brin and Lawrence Page (1998).The Anatomy of a Large- Scale Hypertextual Web
Search Engine, in Proceedings of the seventh International Conference on the World Wide
Web (WWW1998):107-117
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Hashtag Exploration #lovewins
Bas;aanBaccarne,AngelesBriones,StefanBaack,EmilyMaemura,JannaJoceli,PeiqingZhou,HumbertoFerreira.DigitalMethodsSummerSchool2015,Doeslovewin?Themechanicsofmeme;cs,heps://wiki.digitalmethods.net/Dmi/SummerSchool2015DoesLoveWin.
Mapping:
program and anti-program (Rogers, 2017, forthcoming)
supporters and non-supporters (Omena, 2017, forthcoming)
Omena, 2017. Approaches of Data Analysis
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
Hashtag Exploration
Bas;aanBaccarne,AngelesBriones,StefanBaack,EmilyMaemura,JannaJoceli,PeiqingZhou,HumbertoFerreira.DigitalMethodsSummerSchool2015,Doeslovewin?Themechanicsofmeme;cs,heps://wiki.digitalmethods.net/Dmi/SummerSchool2015DoesLoveWin.
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
Hashtag Exploration
Bas;aanBaccarne,AngelesBriones,StefanBaack,EmilyMaemura,JannaJoceli,PeiqingZhou,HumbertoFerreira.DigitalMethodsSummerSchool2015,Doeslovewin?Themechanicsofmeme;cs,heps://wiki.digitalmethods.net/Dmi/SummerSchool2015DoesLoveWin.
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Hashtag Exploration #lovewins
Page Like Network
Exploring:
associations and connections
Page activity Debate within the network
Main organizers of pro-impeachment
protests in Brazil, 2015
Mapping:
program and anti-program (Rogers, 2017, forthcoming)
supporters and non-supporters (Omena, 2017, forthcoming)
Omena, 2017. Approaches of Data Analysis
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Page Like Network on Facebook
MovimentoBrasilLivreandVemPraRuaBrasilpagelikenetwork(depth1),March2015.Nodesize:degree.Colours:clusters.Dataextrac;onbyNetvizzandvizualiza;onbyGephi.
MovimentoBrasilLivreandVemPraRuaBrasilpagelikenetwork(depth2),March2015.Nodesize:degree.Colours:clusters.Dataextrac;onbyNetvizzandvizualiza;onbyGephi.
(Omena and Rosa, 2015)
Omena, 2017. Approaches of Data Analysis
Vem pra Rua Brasil
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
The Tricks of Single Attributes
Omena, 2017. Approaches of Data Analysis
MovimentoBrasilLivreandVemPraRuaBrasilpagelikenetwork(depth2),March2015.Nodesize:in-degree.Colours:clusters.Dataextrac;onbyNetvizzandvizualiza;onbyGephi.
Node size: In-Degree Colours: Modularity
Node size: Out-Degree Colours: Modularity
MovimentoBrasilLivreandVemPraRuaBrasilpagelikenetwork(depth2),March2015.Nodesize:out-degree.Colours:clusters.Dataextrac;onbyNetvizzandvizualiza;onbyGephi.
Page Activity
Page Popularity
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
The Tricks of Single Attributes
Omena, 2017. Approaches of Data Analysis
1. Activity (out-degree) does not call for popularity
(in-degree).
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
The Tricks of Single Attributes
Omena, 2017. Approaches of Data Analysis
MovimentoBrasilLivreandVemPraRuaBrasilpagelikenetwork(depth2),March2015.Nodesize:degree.Colours:clusters.Dataextrac;onbyNetvizzandvizualiza;onbyGephi.
(Omena and Rosa, 2015)
Who generated more debate? (people talking about)
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
The Tricks of Single Attributes
Omena, 2017. Approaches of Data Analysis
1. Activity (out-degree) does not call for popularity
(in-degree).
2. Populate Facebook(e.g. MBL created 68 Facebook pages in 2015) or to have a high number of pages around
the same topic does not mean to generate or create debate.
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Hashtag Exploration #lovewins
Exploring:
associations and connections
Page activity Debate within the network
Main organizers of pro-impeachment
protests in Brazil, 2015
Mapping:
program and anti-program (Rogers, 2017, forthcoming)
supporters and non-supporters (Omena, 2017, forthcoming)
Omena, 2017. Approaches of Data Analysis
Jornalistic Storytelling
Exploring:
Associations around single
actors (ego-network) (Bounegru, et. al, 2016)
“Connected China” (Thomson Reuters, February 2013)
http://china.fathom.info/
Page Like Network
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
http://china.fathom.info/
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Omena, 2017. Approaches of Data Analysis
http://china.fathom.info/
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
An analytical perspective:
Omena, 2017. Approaches of Data Analysis
i) Dominant voice ii) Concern iii) Commitment iv) Positioning v) Alignment
Critical Analytics and Engagement Metrics (Rogers, 2016)
SMART Data Sprint 23-27 January 2017 ˚ Universidade Nova de Lisboa ˚ iNOVA Media Lab
Data Critique
Omena, 2017. Approaches of Data Analysis
i) Situate social media data in time and space
ii) Social media APIs are never neutral
iii) Social media data does not act out of context
iv) Data is never ‘raw’
(AdaptedfromDaltonandThatcher,2016)
Data are not simple evidence of phenomena, they are phenomena in
and of themselves (Wilson, 2014). It (data) has always been
“baked” through both its construction and its resulting
interpretation (Gitelman, 2013). (apud Dalton and Thatcher, 2016, p.4)
NOVAUniversity,LisbonPhDcandidateatUTAus;nIPortugal
@jannajoceli˚thesocialplaCorms.wordpress.com
Janna Joceli C. de Omena
Thanks for your time and attention =)