DigiMeth festival, Centre of Interdisciplinary Methodologies at the University of Warwick.
December 9, 2022.
https://warwick.ac.uk/fac/cross_fac/cim/events/digi-meth/
Workshop facilitators: Janna Joceli Omena, Beatrice Gobbo
Abstract:
This workshop offers methodological guidance for narrating networks through visual network analysis (VNA) (Venturini et al. 2021) and a technicity perspective to the practice of digital methods (Omena 2021). It is divided into two parts. First, we will introduce what questions we should ask to make sense of network building and the key principles of VNA. Second, students will work on digital and printed recommendation networks aiming at narrating what they see.
Main takeaways
Students will be able to explore and identify the main components of a digital network
Students will reflect on the distinction between what is network exploration (description tasks) and network narration (insights, findings)
Students will develop the ability to tell a story about the topic under investigation and what constitutes the network.
Requirements:
Please bring your own computer and get familiar with
Retina (https://ouestware.gitlab.io/retina/beta/)
An example of a network đ link.
Related projects
Venturini, T., Jacomy, M., & Jensen, P. (2021). What do we see when we look at networks: Visual network analysis, relational ambiguity, and force-directed layouts. Big Data & Society, 8(1). https://doi.org/10.1177/20539517211018488
Omena, J.J.(2021). Digital Methods and Technicity-of-the-Mediums. From Regimes of Functioning to Digital Research. [Doctoral Dissertation, Nova University Lisbon]. RepositĂłrio da Universidade Nova de Lisboa. http://hdl.handle.net/10362/127961
Venturini, Tommaso & Bounegru, Liliana & Jacomy, Mathieu & Gray, Jonathan. (2017). 11. How to Tell Stories with Networks Exploring the Narrative Affordances of Graphs with the Iliad: Studying Culture through Data. 10.1515/9789048531011-014.
TOGETHER: TOpology GEneration THrough HEuRisticsSubin Mathew
Â
Network Virtualization is a growing technological process that combines the hardware and software elements in the physical networks and brings it together on a software level. The aim of this project is to develop the process of deploying virtual networks easily. The project involves a software developed by us called âTOGETHER: TOpology GEneration THrough HEuRistics"written in Perl in its simplest form without dependencies so that it could be deployed on any environment. TOGETHER is an isomorphic graph modelling solution used to allow users to make use of topology generators and software like AutoNetkit to make topologies that work on virtual systems.TOGETHER is designed to work in Juniper Networks Virtual Private Cloud architecture and has possibilities for supporting much more. TOGETHER also manages how multiple topologies are interconnected and aims to help researchers work with network virtualization.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on Graph Neural Networks for Recommendations.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/4aXk3LNTJRc
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to make use of several generators for particular classes, which limits its application to datasets that have a just a few classes available. Instead, I suggest a new Group Decrease Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting natural images with semantic marks and can produce high quality outcomes that are feasible for containing a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in task. Ashish Jobson | Dr. Kamlraj R "Fashion AI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41256.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/41256/fashion-ai/ashish-jobson
TOGETHER: TOpology GEneration THrough HEuRisticsSubin Mathew
Â
Network Virtualization is a growing technological process that combines the hardware and software elements in the physical networks and brings it together on a software level. The aim of this project is to develop the process of deploying virtual networks easily. The project involves a software developed by us called âTOGETHER: TOpology GEneration THrough HEuRistics"written in Perl in its simplest form without dependencies so that it could be deployed on any environment. TOGETHER is an isomorphic graph modelling solution used to allow users to make use of topology generators and software like AutoNetkit to make topologies that work on virtual systems.TOGETHER is designed to work in Juniper Networks Virtual Private Cloud architecture and has possibilities for supporting much more. TOGETHER also manages how multiple topologies are interconnected and aims to help researchers work with network virtualization.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on Graph Neural Networks for Recommendations.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/4aXk3LNTJRc
We concentrate on the task of Fashion AI, which entails creating images that are multimodal in terms of semantics. Previous research has attempted to make use of several generators for particular classes, which limits its application to datasets that have a just a few classes available. Instead, I suggest a new Group Decrease Network GroupDNet , which takes advantage in the generator of group convolutions and gradually reduces the percentages of the groups decoders convolutions. As a result, GroupDNet has a lot of influence over converting natural images with semantic marks and can produce high quality outcomes that are feasible for containing a lot of groups. Experiments on a variety of difficult datasets show that GroupDNet outperforms other algorithms in task. Ashish Jobson | Dr. Kamlraj R "Fashion AI" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41256.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/41256/fashion-ai/ashish-jobson
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Brief History of Visual Representation LearningSangwoo Mo
Â
- [2012-2015] Evolution of deep learning architectures
- [2016-2019] Learning paradigms for diverse tasks
- [2020-current] Scaling laws and foundation models
Networks, Hashtags, Memes: A Quali-Quantitative Approach for Exploring Social...Janna Joceli Omena
Â
Workshop at CAIS (Center of Advanced Internet Studies), in Bochum. 24 July 2019.
Part 1: Studying Hashtag Engagement through
Digital Networks (and Methods!)
Janna Joceli Omena
Part 2: Situating Internet Memes as Mediators &
Techno-Social Multiplicities
Elena Pilipets
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
Stacked Generalization of Random Forest and Decision Tree Techniques for Libr...IJEACS
Â
The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
Scaling the mirrorworld with knowledge graphsAlan Morrison
Â
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
Information to Wisdom: Commonsense Knowledge Extraction and Compilation - Part 2Dr. Aparna Varde
Â
This is the 2nd part of the tutorial on commonsense knowledge (CSK) in ACM WSDM 2021 by Simon Razniewski, Niket Tandon and Aparna Varde. It focuses on multimodal knowledge and deep learning based techniques.
Abstract: Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pittâs birth date, or Angelina Jolieâs movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
PhD defense : Multi-points of view semantic enrichment of folksonomiesFreddy Limpens
Â
This thesis, set at the crossroads of Social Web and Semantic Web, is an attempt to bridge Social tagging-based systems with structured representations such as thesauri or ontologies (in the informatics sense). Folksonomies resulting from the use of social tagging systems suffer from a lack of precision that hinders their potentials to retrieve or exchange information. This thesis proposes supporting the use of folksonomies with formal languages and ontologies from the Semantic Web. Automatic processing of tags allows bootstraping the process by using a combination of a custom method analyzing tags' labels and adapted methods analyzing the structure of folksonomies. The contributions of users are described thanks to our model SRTag, which allows supporting diverging points of view, and captured thanks to our user friendly interface allowing the users to structure tags while searching the folksonomy. Conflicts between individual points of view are detected, solved, and then exploited to help a referent user maintain a global and coherent structuring of the folksonomy, which is in return used to garanty the coherence while enriching individual contributions with the others' contributions. The result of our method allows enhancing the navigation within tag-based knowledge systems, but can also serve as a basis for building thesauri fed by a truly bottom up process.
Image Segmentation: Approaches and ChallengesApache MXNet
Â
This slides go over the problem of deep semantic segmentation. It covers the different approaches taken, from hourglass autoencoder to pyramid networks.
Slides by Thomas Delteil
Analysing image collections with the computer vision network approachJanna Joceli Omena
Â
Images in Social Media Research:
Digital Tools and Methodological Challenges
Online-Workshop, 10th February 2023.
https://tu-dresden.de/gsw/phil/ikm/kuge/forschung/aktuelle-projekte/bildproteste/news/online-workshop-images-in-social-media-research-digital-tools-and-methodological-challenges
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Brief History of Visual Representation LearningSangwoo Mo
Â
- [2012-2015] Evolution of deep learning architectures
- [2016-2019] Learning paradigms for diverse tasks
- [2020-current] Scaling laws and foundation models
Networks, Hashtags, Memes: A Quali-Quantitative Approach for Exploring Social...Janna Joceli Omena
Â
Workshop at CAIS (Center of Advanced Internet Studies), in Bochum. 24 July 2019.
Part 1: Studying Hashtag Engagement through
Digital Networks (and Methods!)
Janna Joceli Omena
Part 2: Situating Internet Memes as Mediators &
Techno-Social Multiplicities
Elena Pilipets
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
Stacked Generalization of Random Forest and Decision Tree Techniques for Libr...IJEACS
Â
The huge amount of library data stored in our modern research and statistic centers of organizations is springing up on daily bases. These databases grow exponentially in size with respect to time, it becomes exceptionally difficult to easily understand the behavior and interpret data with the relationships that exist between attributes. This exponential growth of data poses new organizational challenges like the conventional record management system infrastructure could no longer cope to give precise and detailed information about the behavior data over time. There is confusion and novel concern in selecting tools that can support and handle big data visualization that deals with multi-dimension. Viewing all related data at once in a database is a problem that has attracted the interest of data professionals with machine learning skills. This is a lingering issue in the data industry because the existing techniques cannot be used to remove or filter noise from relevant data and pad up missing values in order to get the required information. The aim is to develop a stacked generalization model that combines the functionality of random forest and decision tree to visualization library database visualization. In this paper, the random forest and decision tree techniques were employed to effectively visualize large amounts of school library data. The proposed system was implemented with a few lines of Python code to create visualizations that can help users at a glance understand and interpret the behavior of data and its relationships. The model was trained and tested to learn and extract hidden patterns of data with a cross-validation test. It combined the functionalities of both models to form a stacked generalization model that performed better than the individual techniques. The stacked model produced 95% followed by the RF which produced a 95% accuracy rate and 0.223600 RMSE error value in comparison with the DT which recorded an 80.00% success rate and 0.15990 RMSE value.
Scaling the mirrorworld with knowledge graphsAlan Morrison
Â
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
Information to Wisdom: Commonsense Knowledge Extraction and Compilation - Part 2Dr. Aparna Varde
Â
This is the 2nd part of the tutorial on commonsense knowledge (CSK) in ACM WSDM 2021 by Simon Razniewski, Niket Tandon and Aparna Varde. It focuses on multimodal knowledge and deep learning based techniques.
Abstract: Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pittâs birth date, or Angelina Jolieâs movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
PhD defense : Multi-points of view semantic enrichment of folksonomiesFreddy Limpens
Â
This thesis, set at the crossroads of Social Web and Semantic Web, is an attempt to bridge Social tagging-based systems with structured representations such as thesauri or ontologies (in the informatics sense). Folksonomies resulting from the use of social tagging systems suffer from a lack of precision that hinders their potentials to retrieve or exchange information. This thesis proposes supporting the use of folksonomies with formal languages and ontologies from the Semantic Web. Automatic processing of tags allows bootstraping the process by using a combination of a custom method analyzing tags' labels and adapted methods analyzing the structure of folksonomies. The contributions of users are described thanks to our model SRTag, which allows supporting diverging points of view, and captured thanks to our user friendly interface allowing the users to structure tags while searching the folksonomy. Conflicts between individual points of view are detected, solved, and then exploited to help a referent user maintain a global and coherent structuring of the folksonomy, which is in return used to garanty the coherence while enriching individual contributions with the others' contributions. The result of our method allows enhancing the navigation within tag-based knowledge systems, but can also serve as a basis for building thesauri fed by a truly bottom up process.
Image Segmentation: Approaches and ChallengesApache MXNet
Â
This slides go over the problem of deep semantic segmentation. It covers the different approaches taken, from hourglass autoencoder to pyramid networks.
Slides by Thomas Delteil
Analysing image collections with the computer vision network approachJanna Joceli Omena
Â
Images in Social Media Research:
Digital Tools and Methodological Challenges
Online-Workshop, 10th February 2023.
https://tu-dresden.de/gsw/phil/ikm/kuge/forschung/aktuelle-projekte/bildproteste/news/online-workshop-images-in-social-media-research-digital-tools-and-methodological-challenges
Making methods with vision APIs, online data & network building (lessons lear...Janna Joceli Omena
Â
Research project on building and interpreting computer vision networks with the purpose to develop visual digital methods for social and media research. Project diary: https://thesocialplatforms.wordpress.com/2020/09/10/computer-vision-networks/
SeminĂĄrio Internacional Ecologia de MĂdia no Contexto da Plataformização
23 a 25 de Setembro, 2021 I Evento virtual I Universidade Federal de Ouro Preto, Minas Gerais, Brasil.
Video: https://www.youtube.com/watch?v=ZXunLB5kfwU&t=22s
Info + videos + grupos organizadores do seminĂĄrio:
https://www.conjor.com.br/seminarioplataforma
https://r-est.fafich.ufmg.br/seminario-ecologia-de-midias-e-plataformizacao/
Project developed during the Digital Methods Summer School 2021.
https://wiki.digitalmethods.net/Dmi/SummerSchool2021BolsobotsNetworks
DOI: 10.13140/RG.2.2.18020.30084
Digital Media Winter Institute 2019
Smart Data Sprint: Beyond visible engagement, Jan. 28 - Feb.1, Universidade Nova de Lisboa, Lisbon, Portugal.
[Short talk]
The Grammars of Social Media: Thinking platform data under the modes of techn...Janna Joceli Omena
Â
Digital Media Winter Institute 2018
Smart Data Sprint: Interpreters of Platform Data, Jan. 29 - Feb.2, Universidade Nova de Lisboa, Lisbon, Portugal.
[Short talk]
...................................
Correction in slide 17: #DilmaRoussef and #MichelTemer co-related tag Network. See the Follow Network here: https://www.slideshare.net/jannajoceli/why-look-at-social-media-apis-81702316
Digital Culture Meets Data: Critical Approaches
Ë ECREA Digital Culture and Communication Section Conference Ë
Ë 6 - 7 November 2017 Ë University of Brighton Ë Mithras House Ë
This was the official presentation of SMART; a research group of iNOVA Media Lab. Presentation was held at Universidade Nova de Lisboa (FCSH), on 6 October 2016.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
Â
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
Â
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion âCompetition and Regulation in Professions and Occupationsâ held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the authorâs consent.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Â
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
3. In this workshop, you will learn about visual network
analysis, knowing how to apply a technicity perspective
to this research practice. We will not follow a
traditional teaching schedule, but one based on
practice, experience and tacit knowledge.
4. What precedes the network visualisation?
What takes place with and in the network?
As a guide to applyvisual networkanalysis yet accountingfor a
technicityperspectiveto this research practice
As a means of narratingdigital networks
Learn by Doing & Question Driven
Teaching Approach
5. Learning Outcomes
By the end of this workshop, we expect you will
â be able to explore and identify the main components of digital networks
â reflect on the distinction between what is network exploration (description
tasks) and network narration (insights, findings)
â develop the ability to tell a story about the topic under investigation and
what constitutes the network.
7. Digital Networks
In concept
â [froma theoretical standpoint,STS] a conceptualmetaphor
(spaceof connections)
â [from a methodological standpoint,SNA] an analytic or computation
technique
(e.g., the mathematicsof graphs)
â [from network materiality perspective] Inscriptions"producing relational
data complementary to that of human
relations" (relational dataset)
â a socio-technical system
[Venturini,Munkand Jacomy, 2019]
10. Digital Networks
In concept
â a conceptual metaphor
(space of connections)
â an analytic or computation technique
(e.g., the mathematics of graphs)
â Inscriptions or digital records "producing
relational data complementary to that of human
relations" (relational dataset)
â a socio-technical system
[Venturini, Munk and Jacomy, 2019]
In practice
Network of image
circulation: image
URLs where fully
matching /chug/ logos
are found. 4chan,
July 2022.
PROTOCOLS NETWORK VISUALISATIONS
MAKING
[Omena & Amaral, 2019; Omena, Gobbo et al. 2021]
11. YouTube Channel Network
On the right
YouTube channel network representation of
the giant connected component formed by
the 100k+ elite channels (145,117 channels,
2,572,163 edges), sizeindicating subscriber
count.
(Rieder, Coromina, Matamoros-FernĂĄndez,
2020)
Python script* â YouTube Data API
*"The script startedfrom a single seed and followed
connections until no new channels were discovered".
MAKING
12. 4Chan Image Circulation Network
4CAT â Archive 4plebs
Board â /pol
subject contains: "chug"
On the right
Network of image circulation: image
URLs where fully matching /chug/
logos are found. 4chan, July 2022.
MAKING
14. = a set of:
nodes (vertices)
edges
Nodes can be people, organization,
institutions or digital objects (images,
link domains, hashtags, emojis, post, video,
etc.)
Monopartite:
One type of node
Bipartite:
Two types of nodes
Image retrieved from:
https://adrianmejia.com/blog/2018/05/
14/data-structures-for-beginners-
graphs-time-complexity-tutorial/
Graph Representation Types of Connections Degree of Connections
TYPE AND COMPOSITION
15. How do technical mediation, extraction software
associated with platform's mechanisms add meaning to
the data acquired?
Monopartite network
One type of node: YouTube channel
Connections: Subscriptions
Bipartite network
Two types of nodes: images and
webpages
Connections: whether images are found
in webpages
Channels subscribe to or
feature other channels.
Connections may stand for affinity,
endorsement, or interest.
Although threads in 4chan have a short
life span, archives like 4plebs make visual
data accessible.
When invoking Google Vision AI to
detect sites of image circulation, the API
would return results according to Google
Image Search.
TYPE AND COMPOSITION
Network
YouTube Channel Network
4chan Image circulation network
Nodes and Connections Meaning
16. TYPE AND COMPOSITION
YouTube Channel Network
4chan Image circulation network
Network Conceptual Metaphor
A societal response to a still unknow
virus and the specific actors (i.e., news
media outlets, Youtubers)
Sub-cultural reactions to the Russia-
Ukraine war: the pro-Russian visualities
belonging exclusively to 4chan
vernacular and flowing out to other
platforms.
18. Layered diagram
Image Source here
Circular layouts
Image sourcehere
Force-directed layouts:ForceAtlas2
Image source: personal files
SHAPE
There are graph layoutalgorithms ...and force-directedgraph layouts.
19. SHAPE
Beyond existing connections,
what shapes digital networks?
Purpose
â Serve the purpose of arranging graph structures
â Aesthetically â minimize edge crossing.
(Fruchterman and Reingold,1991; Kobourov2013; Jacomy et al., 2014)
Functioning
â Work under the logic of repulsive and attractive forces. (Fruchterman &
Reingold,1991; Jacomy et al., 2014)
â Calculatethe layout of a graph using only information contained within
the structure of the graph itself, rather than relyingon domain-specific
knowledge.(Kobourov,2013)
Space
â The space of networks is relative rather absolute, the
space is a consequence and not a condition of element
positioning. (Venturini et al., 2019)
[force-directedlayouts]
20. In Visual Network Analysis
Reading networks require more intuitive
spatial metaphors, and less computational
and statistical metrics.
Different network zones should inform different
perspectives
from how connections are made
and what does it mean
SHAPE
21. Co-hashtag Network of#jornalismoindependente
(independent journalism) Instagram, 2019.
In this network:
âą Node proximity means co-occurrence of
hashtagsin a given dataset
âą Nodes positionedin different network zones
should inform what are the topics or issues
associated with the hashtag(s) used as entry
point for data collection
22. Co-hashtag Network of#jornalismoindependente
(independent journalism) Instagram, 2019.
[centre]
The hashtag used as entry point to
collect data and
Associated hashtags
Hashtags frequently co-occurring
with #jornalismindependente
[periphery]
Hashtagclusters addressingspecific
agendas,still co-occurringwith
#jornalismindependente
[mid-zone]
Bridginghashtags`
#economy
23. â The overall shape of the network
â The particularities of different zones
â The specific situation of a given zone or a node path
â The connections between clusters
Descriptionsand a good understanding
of what we look at lead us valuable
insights or more questions
We look at
SHAPE
29. âAnnotation adds information, labeling, and/or
commentary into any model and can be added to
any feature of a data set present in a display: a
node, edge, point, text, image. Annotations can be
recorded in a data structure as attributes noting
connections, relations, or other analytic and
interpretative featuresâ
Drucker J., Non-representational approaches to modeling interpretationin a graphical
environment, (2018), p.256
42. âA story is defined as all of the
events in a narrative, those
presented directly to an audience
and those which might be inferredâ
(Bach et al., 2018, p. 108)
45. Activity 2
25 mins
â Define roles (annotators,
browsers, googling and
youtoubing).
â Description and
annotations
(question driven process)
15 mins
â Decide the story to tell
â Narrate the story (annotate it
and write it! 2 minutes
showcasing)
46. What is the type of video titles in the centre?
Which kind of videos/channels populate the zones of the network?
Are there bridging nodes connecting different zones?
Why are clusters located in the peripheral zone? Is it the language, location or topic of
the videos?
What do video/channel categoriestell?
What do YouTube engagementmetrics (i.e., view count now and before) inform?
What are the insights provided by community detection (modularity)?
Activity 2
47. In Conclusion
â VNNs are built on top of VNA
â VNA e VNN require time, this is just a taste!
â Annotations can âbe recorded in a data structure as
attributesâ (Drucker, 2018)
â Stories can be told using visulisations and Narrative Design
Patterns (Bach et al. 2018)