Approaches of Data Analysis: Networks generated through Social Media

APPROACHES OF DATA ANALYSIS:
Networks generated through Social Media
NOVA University, Lisbon
PhD candidate at UT Aus;n I Portugal
@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)
Logic
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

Pages
Groups
Page Network
Shared Links
List of Events

Users
Key words
Hashtags
Loca;on
Data extraction*
Media content and digital objects
Videos
Channels Hashtags
Loca;ons
Follow Network Hashtags
Posts
(textual content:
Cap;on, comments, replies)
(visual content: videos, photos, memes)
Page Like Network
Groups Network
Events
URLs

Tweets
(textual content:
Tweet text, men;ons, replies)
(visual content:
videos, photos, gifs)
Geotags
URLs

Video Info
(basic info an stats, comments,
comments authors, interac;ons between
users in the comment sec;ons)
Video List and Network
Channel Info and Network

*Tools: Netvizz, Twitonomy, DMI-TCAT, YouTube Data Tools, Visual Tagnet Explorer, Tumblr Tool
Media and users Info
(textual content: cap;on, tags, users bio)
(visual content: photo and video)
(basic stats)
Co-Tag Network

Posts
(textual content:
summary, cap;on, tags, users bio)
(visual content: photo and video)
(basic stats)
Co-Tag Network

Output files
CSV., TAB. GDF., XML., interac;ve chart

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

My personal friendship connec;ons on Facebook in January 2014. Extrac;on So`ware: Netvizz. Visualiza;on so`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;aan Baccarne, Angeles Briones, Stefan Baack, Emily Maemura, Janna Joceli, Peiqing Zhou, Humberto Ferreira. Digital Methods Summer School 2015,
Does love win? The mechanics of meme;cs, heps://wiki.digitalmethods.net/Dmi/SummerSchool2015DoesLoveWin.
Mapping:
program and anti-program
supporters and non-supporters

Hashtag Exploration
Bas;aan Baccarne, Angeles Briones, Stefan Baack, Emily Maemura, Janna Joceli, Peiqing Zhou, Humberto Ferreira. Digital Methods Summer School 2015,
Does love win? The mechanics of meme;cs, heps://wiki.digitalmethods.net/Dmi/SummerSchool2015DoesLoveWin.

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
Movimento Brasil Livre and Vem Pra Rua Brasil page like network (depth 1), March 2015.
Node size: degree. Colours: clusters. Data extrac;on by Netvizz and vizualiza;on by Gephi.
(Omena and Rosa, 2015)
Vem pra Rua Brasil

The Tricks of Single Attributes
Node size: in-degree. Colours: clusters. Data extrac;on by Netvizz and vizualiza;on by Gephi.
Node size: In-Degree
Colours: Modularity
Node size: Out-Degree
Colours: Modularity
Node size: out-degree. Colours: clusters. Data extrac;on by Netvizz and vizualiza;on by Gephi.
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

http://china.fathom.info/

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’
(Adapted from Dalton and Thatcher, 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)

NOVA University, Lisbon
PhD candidate at UT Aus;n I Portugal
@jannajoceli ˚ thesocialplaCorms.wordpress.com

Janna Joceli C. de Omena

Thanks for your time and attention =)

Approaches of Data Analysis: Networks generated through Social Media

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