The Grammars of Social Media: Thinking platform data under the modes of technicity

THE GRAMMARS OF SOCIAL MEDIA:
Universidade NOVA de Lisboa
NOVA FCSH ˚ iNOVA Media Lab
@jannajoceli ˚ thesocialpla?orms.wordpress.com

Janna Joceli C. de Omena

Digital Media Winter InsGtute 2018 ˚ SMART Data Sprint: Interpreters of Pla?orm of Data
˚ 29 January - 2 February ˚ Universidade NOVA de Lisboa˚ NOVA FCSH
Thinking platform data under the modes of technicity

THE GRAMMARS OF SOCIAL MEDIA following SHARING watching TAGGING linking COMMENTING
replying DELETING hashtagging POSTING reposting TWEETING retweeting LIKING following
SHARING watching TAGGING linking COMMENTING replying DELETING hashtagging POSTING
reposting TWEETING retweeting LIKING following SHARING watching TAGGING linking
COMMENTING replying DELETING hashtagging POSTING reposting TWEETING retweeting
LIKING following SHARING watching TAGGING linking COMMENTING replying DELETING
hashtagging POSTING reposting TWEETING retweeting LIKING following SHARING watching
TAGGING linking COMMENTING replying DELETING hashtagging POSTING reposting TWEETING
retweeting LIKING following SHARING watching TAGGING linking COMMENTING replying
DELETING hashtagging POSTING reposting TWEETING retweeting LIKING following SHARING
watching TAGGING linking COMMENTING replying DELETING hashtagging POSTING reposting
TWEETING retweeting LIKING following SHARING watching TAGGING linking COMMENTING
All trackable and collectable forms of activity
imposed by social media platforms

TECHNICITY following SHARING watching TAGGING linking COMMENTING replying DELETING
hashtagging POSTING reposting TWEETING retweeting LIKING following SHARING
A mode of existence* of web platforms,
and a form of knowledge
*The use of “mode of existence” is inspired by the work of Gilbert Simondon, On the Mode of Existence of Technical Objects (1980),

1. Grammars
2. Technicity
4. Interpretation
3. Knowledge
5. Case
à HASHTAG ENGAGEMENT RESEARCH

Grammars of action are schemas of representation (and also reorganisers)
of human activity that originate from computational system’s capture model:
“discourse-made-machinery” or “a linguistic metaphor for human activity”
[Phil Agre 1994]
1. Grammars
Users + Developers + Advertisers + Brands + Researchers +
Activists + Government Authorities + Non-profit organizations
Application Programming Interface
Front-End Interface Back-End Interface
[What we can do in the front-end interface is stored,
organised, and partially made available in the back-end]
Social Media Grammars:
Omena, J.J. 2018 The Grammars of Social Media
Posts ˚˚ URLs˚˚ Tweets ˚˚ Comments ˚˚ Replies ˚˚ Hashtags ˚˚Location ˚˚ Memes ˚˚ Links ˚˚ Channels ˚˚ Reactions ˚˚ Shares ˚˚ Likes

Front-end Grammatisation
Public or Private Account ˚˚ Stories [photos, videos,
live, boomerang] ˚˚ Mute Stories ˚˚ Direct Messages ˚˚
Upload one or more photos and one minute video ˚˚ Like
˚˚ Comment ˚˚ Save Post ˚˚ Like a comment ˚˚ Reply a
comment ˚˚ Post Notifications ˚˚ Tag people ˚˚ Block
Users ˚˚ Search [people, tags, places]
Back-end Grammatisation
GET/users/user-id ˚˚ GET/users/self/media/recent ˚˚
GET/users/user-id/media/recent ˚˚ GET/users/self/
media/liked ˚˚ GET/users/self/follows ˚˚ GET/users/
self/followed-by ˚˚ GET/users/user-id/relationship ˚˚
GET/media/media-id ˚˚ GET/media/shortcode/shortcode ˚˚
GET/media/search ˚˚ GET/media-id/comments ˚˚ GET/˚˚
Digital Media Winter InsGtute 2018 ˚ SMART Data Sprint: Interpreters of Pla?orm of Data ˚ 29 January ˚ Universidade NOVA de Lisboa˚ NOVA FCSH
[Instagram Pla?orm API]

Platform Data ---à “the outcome of capture systems which allow to collapse action
and its capture into pre-structured in form, and flexible in meaning”
[Gerlitz 2016]

Grammars: mechanisms, production and meaning
Posts ˚˚ URLs˚˚ Tweets ˚˚ Comments ˚˚ Replies ˚˚ Hashtags ˚˚Location ˚˚ Memes ˚˚ Links ˚˚ Channels ˚˚ Reactions ˚˚ Shares ˚˚ Likes
2. Actors [humans or automated beings]:
---à have different reasons to produce social media grammars
[‘the interpretative flexibility’ van Dijck 2012]
---à assign different meanings to social media grammars
[a call for accounting the thickness of the data and its
variety of meanings Dalton and Thatcher 2014]
3. Social media grammars should not be treated equally across platforms
[Rogers 2017]
1.  Likes, shares, tweets, comments work under particular mechanisms
(and an inherent technicity) that belong to social media platforms
---à The politics of platforms (Gillespie 2010] and cultures of use
---à Datafication, Commodification, Selection [van Dijck 2016]
---à Algorithmic configurations [Rieder 2014]

Bots comments --à InstagramMy friends comments --à Facebook
Sponsored post --à Facebook My Mix suggested by YouTubeTrends --à Twitter

MODE OF EXISTENCE LIKING WAY OF BEING watching THE OPERACIONAL FUNCTIONING replying
Hashtagging A FORM OF KNOWLEDGE following SHARING FUNCTIONAL NOTIONS OF KNOWLEDGE
A degree of technicity is required
to grasp social media grammars

2.Technicity Omena, J.J. 2018 The Grammars of Social Media
“If one wants to understand a being
completely, one must study it by
considering it in its entelechy,
and not it in its inactivity
or its static states”
[Simondon 2009, 19]

3) Applied research
with digital
methods
2) Method
Social Media Technicity
Mode of existence
Way of being
Schema of concretization
Gilbert Simondon (1980, 2009)
Algorithmic Techniques
APIs
Research Strategies
1) Technical
Knowledge
--—> THICK DESCRIPTION:
APIS + ALGORITHMS
WHAT TO LOOK FOR IN
SOCIAL MEDIA APIS?
--—>
◊ politics ◊ technical
features ◊ values and
assumptions that define them
Three connected kinds of problems:
--—>
--—>
--—>
ALGORITHIMIC MECHANISMS--—>
◊ shifting concerns from “what is being said” to “how
it is being processed and rendered” (Langlois and Elmer 2013)
◊lack of consciousness of what it means to “mechanise
methodology” Rieder and Rohle (2012)
REFINE THE COMPREHENSION OF
SOCIAL MEDIA GRAMMARS’ OF ACTION
ALGORITHIMIC API RESULTS
EXPLORE WAYS OF DOING RESEARCH
--—>
--—>
--—>

A CALL FOR FUNCTIONAL NOTIONS OF KNOWLEGDE TAGGING linking COMMENTING replying
1
2
3
4 5

3. Knowledge Omena, J.J. 2018 The Grammars of Social Media
1. What are the entry points used to collect social media grammars?
2. What entry points cannot be captured anymore?
3. What grammars (digital objects) can be part of my study?
4. How far back in time can data be retrieved?
5. What are the standard output files?

User ID
Page id
Public Group id
Closed Group ids
Key word
URL
Users
Key words
Hashtag
LocaGon
Entry points for Data ExtracGon*
Video ID
Channel ID
User Name
Hashtag
LocaGon

Hashtag
Tweets meta-info
Networks
Geotags
URLs

Video Info
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
Co-Tag Network
Follow and
follower Networks

Posts
Co-Tag Network

Output Files: CSV., TAB. GDF., XML., interacGve chart

--—>
Social media grammars that can be part of my research
Personal Networks
[friendship connecGons
and like networks]
Pages and Public Groups data
Page Like Network
Page Gmeline images
Pages and Groups Network
Closed Group Data
Friendship connecGon in Groups
List of events
List of shared URLs

Unavailable entry point for data collection on Instagram:
[Instagram User Name to retrieve the Follow or Follower Network]
Instagram Pla?orm, 2016. The Follower Network of Dilma Rousseﬀ and Michel Temer.
ExtracGon tool: Instagram Network, developer: Erik Borra. VisualizaGon sohware: Gephi (Omena, 31 May 2016)

Facebook Pla?orm, 2013. My Friendship Like Network in December 2013. ExtracGon tool: Netvizz.
VisualizaGon sohware: Gephi.(Omena, 2015)
Unavailable entry points for data collection on Facebook
[Users ID to retrieve Personal Networks – Friendship connections and Like Network]

Unavailable entry points for data collection on Facebook
[Groups ID to retrieve Close Group Data, and Friendship Like Network]
Facebook Pla?orm, 2014. Five Facebook Groups - the fans of The Big Bang Theory. Undirected graph of the friendship connecGon
according to gender and locale. 13.762 acGve users. ExtracGon tool: Netvizz, developer: Bernhard Rieder.
VisualizaGon sohware: Gephi. (Omena, 2015)

[The search for websummit tags
was made on 28 sep 2017]

961 posts retrieved in November 2014
2.256 posts retrieved in November 2015
7.216 posts retrieved in November 2016
ParGcular event: #websummit
Data extracGon: 28 September 2017
How far back in time can data be retrieved? Omena, J.J. 2018 The Grammars of Social Media
media items are ordered according to when they were tagged ˚˚
Instagram is a good source for historical data when it comes to the study
of non-popular tags or event-based tags.

Popular tag: #ForaTemer
Data extracGon: 8 August 2017
#foratemer
10898 media items that were posted
between 2014.08.19 and 2017.08.08
»» total per year:
2014: 13 posts
2015: 1 post
2016: 17 posts
2017: 10.865 posts
jul_836
aug_9.986
[only 8 days]
How far back in time can data be retrieved?
media items are ordered according to when they were tagged ˚˚

the decisions we make and the metrics we opt for in order to first
select and then give meaning to social media grammars
the need to go beyond high-engagement parameters
4.Interpretation Omena, J.J. 2018 The Grammars of Social Media

Engagement

The dual logic of
Engagement
1. the sums of acGons
media items receive

2. the recurrent use of
na-vely digital objects (1) or
grammars of ac-on (2) from
many actors about a topic
+
(1) Rogers, 2013; (2) Agre, 1994

the most engaged list
[or popular or famous]

the ordinary list
[or marginal or less popular]
1. the sums of acGons
media items receive

2. the ordinary use of na-vely digital
objects (1) or grammars of ac-on (2)
from many actors about a topic
+
The dual logic of Engagement
(1) Rogers, 2013; (2) Agre, 1994
?
dominant voice

ordinary voice


1. Grammars
2. Technicity
4. Interpretation
3. Knowledge
5. Case study: Hashtag Engagement Research

*Project conducted in the context of Digital Methods Summer School at the University of
Amsterdam in 2017, lead together with Elaine Rabello, Andre Mintz, and others.
The four perspecGves to visualise hashtag engagement is an alternaGve approach to research based
on high-engagement parameters. It proposes the split of dominant and ordinary voices for reaching a
broader narraGve of the not as popular publicaGons; providing both diﬀerent ouqakes of the the
same phenomenon and ﬁndings the span across the perspecGves.
[Omena, J.J., Mintz, & A Rabello, E. (forthcoming). Visualising hashtag engagement]

Media Item Perspective Omena, J.J. 2018 The Grammars of Social Media
Four perspectives to visualise Hashtag Engagement˚˚
Metrics of audience engagement per media item combined with user activity ˚˚
1.  It is a form of delimiting the dominant and
ordinary voices
2.  It allows to detect the composition and
structure of the dominant/ordinary voices
3.  It benefits both the visual and textual content
analysis of these groups of actors

Workers’ Protests: General Strike
[15 March 2017]
midianinja
uneoficial
jeanwyllys_real
br4sileirissimos
samanthaschmutz
dilmabolada
jornalistaslivres
cutbrasil
Tuliparuiz
ofalanteblog
foratemer
sergio_moro_heroi_do_brasil
umaoutraopiniao
artesdepressao
revistaforum
portalg1
foracorruptosoficial
trlacerda
radioguerrilha
filhasdefrida
Conservative Protests: Anti-corruption
[26 March 2017]
direitadaopressao
chegadecorruptos
sergio_moro_heroi_do_brasil
rachelsherazade
vaza.di_u_ma_vez
foracorruptosoficial
sergiomoroironico
vejanoinsta
ronaldocaiado
quadrinsta
radiojovempan
jornal_opopular
vitoriotrevisanneto
fora.corruptos
brtrends
opovoonline
promonarquia
portalne10
acordabrasil__
foi_golpe
The dominant voice along the day of protests, March 2017, Brazil.
Media Item perspective Omena, J.J. 2018 The Grammars of Social Media

User perspective Omena, J.J. 2018 The Grammars of Social Media
Metrics of caption or hashtag adoption per user ˚˚
1.  It is a way to visualise the textual content
(captions) by clustering tags, terms or positioning
2. It allows to relate hashtag content with caption
or visual content
3. It benefits the identification of actors who try
to create a buzz around the hashtag
4. It helps to detect botted accounts

Methodological experiment conducted in the context of Digital Methods Summer School at the University of Amsterdam in 2017.
Analysis by Janna Joceli, Elaine Rabello, Andre Mintz, Suay Ozkula, Gabriela Sued, Ece Elbeyi, and Alessandra Cicali.
(see hqps://wiki.digitalmethods.net/Dmi/InstagramLivenessVisualisingengagement).

26 March 2017 – Instagram’s most active users by hashtag mention
A chronological perspective from 2016 to March 2017
Total of mentions: 33.856 ˚˚ Data extraction: 27 March 2017
Tools: Visual Tagnet Explorer and RankFlow

Visual perspective Omena, J.J. 2018 The Grammars of Social Media
Visual characteristics of images associated with hashtags˚˚
It promotes automated analysis of visual content or the
option of plotting images to detect visual patterns
FOOD
TEXT
BANNERS
FACES
SELFIES
CROWDS
URBAN LANDSCAPE
SKY
Image-Label Network of the anG-coup protests in Brazil, 18 March 2016.

Methodological experiment conducted in the context of Digital Methods Summer School at the University of Amsterdam in 2017.
Analysis by Janna Joceli, Elaine Rabello, Andre Mintz, Suay Ozkula, Gabriela Sued, Ece Elbeyi, and Alessandra Cicali.
(see hqps://wiki.digitalmethods.net/Dmi/InstagramLivenessVisualisingengagement).

15 March 2017, Brazil – Exploring the visual patterns of the Workers Protests
[Available images: 5.688 ˚˚ Tools: DownThemAll and ImageSorter]

Everybody in the streets against The Terminator:
Michel Temer
NAMASTEMER: The ‘Fora Temer’ that
lives in me, salutes the ‘Fora
Temer’ that lives in you.
I Want to retire!
[plotting images to detect memes]

Grammars of hashtag perspective Omena, J.J. 2018 The Grammars of Social Media
Forms of hashtag use: positioning, double sense hashtags,
hijacked hashtags, hate hashtags

The stories pla?orm grammars can tell will depend on what you have collected and how,
what you have selected and the opGons you have made to filter and visualise social media
grammars. Before analysing or restarGng this same process, you describe what you see, you
describe your impressions - accounGng the whole process. The descripGon is as important
as the findings. Finally, all intellectual effort you have put on that dataset will define what or
how you interpret a parGcular issue according to one or more grammars.

TWEETING following SHARING watching TAGGING linking COMMENTING replying DELETING
TWEETING retweeting LIKING following SHARING watching OBRIGADA J linking COMMENTING
hashtagging POSTING reposting retweeting LIKING following SHARING

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