Approaches of Data Analysis: Networks generated through Social Media

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

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)

Posts URLs Tweets Comments Replies Hashtags Location Memes Links Channels

Grammars’ of Action (Agre,1994)

Lack of neutrality

Social Media Studies with Digital Methods

Machine-readable interfaces (Berlind, 2015)

«Give third-parties access to data and

functionalities that belong to the platform»

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?

Social Media Studies with Digital Methods

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

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)

Network Analysis on Social Media

Mypersonalfriendshipconnec;onsonFacebookinJanuary2014.Extrac;onSo`ware:Netvizz.Visualiza;onso`ware:Gephi.

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)

Choose a node attribute:

•  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

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)

Hashtag Exploration

Page Like Network

Exploring:

associations and connections

Page activity Debate within the network

Main organizers of pro-impeachment

protests in Brazil, 2015

Mapping:

Page Like Network on Facebook

MovimentoBrasilLivreandVemPraRuaBrasilpagelikenetwork(depth1),March2015.Nodesize:degree.Colours:clusters.Dataextrac;onbyNetvizzandvizualiza;onbyGephi.

(Omena and Rosa, 2015)

Vem pra Rua Brasil

The Tricks of Single Attributes

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

1.   Activity (out-degree) does not call for popularity

(in-degree).

(Omena and Rosa, 2015)

Who generated more debate? (people talking about)

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.

Exploring:

associations and connections

Page activity Debate within the network

Main organizers of pro-impeachment

protests in Brazil, 2015

Mapping:

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

An analytical perspective:

i) Dominant voice ii) Concern iii) Commitment iv) Positioning v) Alignment

Critical Analytics and Engagement Metrics (Rogers, 2016)

Data Critique

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 =)

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