Presentation for a workshop given at the Centre for Interdisciplinary Methodologies at Warwick University on May 9 2013. Focuses on conceptual and historical questions. Comments, references, and explanations are in the notes.
Engines of Order. Social Media and the Rise of Algorithmic Knowing.Bernhard Rieder
Talk given at the Social Media and the Transformation of Public Space Conference on June 19 at the University of Amsterdam. References and comments are in the notes section.
Engines of Order. Social Media and the Rise of Algorithmic Knowing.Bernhard Rieder
Talk given at the Social Media and the Transformation of Public Space Conference on June 19 at the University of Amsterdam. References and comments are in the notes section.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
What is NodeXL (Network Overview, Discovery and Exploration for Excel)?
Graph aesthetics in NodeXL
Visual pleasure
Cognitive pleasure
Bridging to NodeXL for research and analysis
Introduction to Computational Social Science - Lecture 1Lauri Eloranta
First lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015. (http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Complex Social Systems - Lecture 5 in Introduction to Computational Social Sc...Lauri Eloranta
Fifth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
Paper Writing in Applied Mathematics (slightly updated slides)Mason Porter
Here are my slides (which I have updated very slightly) in writing papers in applied mathematics.
There will be an accompanying oral presentation and discussion on Friday 20 April. I am recording the video for that and plan to post it along with these (or a further updated version of these) slides.
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Towards Contested Collective Intelligence
Simon Buckingham Shum, Director Connected Intelligence Centre, University of Technology Sydney
This talk is to open up a dialogue with the important work of the SWARM project. I’ll introduce the key ideas that have shaped my work on interactive software tools to make thinking visible, shareable and contestable, some of the design prototypes, and some of the lessons we’ve learnt en route.
Everything is connected: people, information, events and places. A practical way of making sense of the tangle of connections is to analyze them as networks. The objective of this workshop is to introduce the essential concepts of Social Network Analysis (SNA). It also seeks to show how SNA may help organizations unlock and mobilize these informal networks in order to achieve sustainable strategic goals. After discussing the essential concepts in theory of SNA, the computational tools for modeling and analysis of social networks will also be introduced in this presentation.
Formations & Deformations of Social Network GraphsShalin Hai-Jew
Social network graphs are node-link (vertex-edge; entity-relationship) diagrams that show relationships between people and groups. Open-source tools like NodeXL Basic (available on Microsoft’s CodePlex) enable the capture of network data from select social media platforms through third-party add-ons and social media APIs. From social groups, relational clusters are extracted with clustering algorithms which identify intensities of connections. Visually, structural relational data is conveyed with layout algorithms in two-dimensional space. Using these various layout options and built-in visual design features, it is possible to aesthetically “deform” the network graph data for visual effects. This presentation introduces novel datasets and novel data visualizations.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
What is NodeXL (Network Overview, Discovery and Exploration for Excel)?
Graph aesthetics in NodeXL
Visual pleasure
Cognitive pleasure
Bridging to NodeXL for research and analysis
Introduction to Computational Social Science - Lecture 1Lauri Eloranta
First lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015. (http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Complex Social Systems - Lecture 5 in Introduction to Computational Social Sc...Lauri Eloranta
Fifth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
Paper Writing in Applied Mathematics (slightly updated slides)Mason Porter
Here are my slides (which I have updated very slightly) in writing papers in applied mathematics.
There will be an accompanying oral presentation and discussion on Friday 20 April. I am recording the video for that and plan to post it along with these (or a further updated version of these) slides.
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Towards Contested Collective Intelligence
Simon Buckingham Shum, Director Connected Intelligence Centre, University of Technology Sydney
This talk is to open up a dialogue with the important work of the SWARM project. I’ll introduce the key ideas that have shaped my work on interactive software tools to make thinking visible, shareable and contestable, some of the design prototypes, and some of the lessons we’ve learnt en route.
Everything is connected: people, information, events and places. A practical way of making sense of the tangle of connections is to analyze them as networks. The objective of this workshop is to introduce the essential concepts of Social Network Analysis (SNA). It also seeks to show how SNA may help organizations unlock and mobilize these informal networks in order to achieve sustainable strategic goals. After discussing the essential concepts in theory of SNA, the computational tools for modeling and analysis of social networks will also be introduced in this presentation.
Formations & Deformations of Social Network GraphsShalin Hai-Jew
Social network graphs are node-link (vertex-edge; entity-relationship) diagrams that show relationships between people and groups. Open-source tools like NodeXL Basic (available on Microsoft’s CodePlex) enable the capture of network data from select social media platforms through third-party add-ons and social media APIs. From social groups, relational clusters are extracted with clustering algorithms which identify intensities of connections. Visually, structural relational data is conveyed with layout algorithms in two-dimensional space. Using these various layout options and built-in visual design features, it is possible to aesthetically “deform” the network graph data for visual effects. This presentation introduces novel datasets and novel data visualizations.
Biourbanism focuses on the urban organism, considering it as a hypercomplex system, according to its internal and external dynamics and their mutual interactions.
The urban body is composed of several interconnected layers of dynamic structure, all influencing each other in a non-linear manner. This interaction results in emergent properties, which are not predictable except through a dynamical analysis of the connected whole. This approach therefore links Biourbanism to the Life Sciences, and to Integrated Systems Sciences like Statistical Mechanics, Thermodynamics, Operations Research, and Ecology in an essential manner. The similarity of approaches lies not only in the common methodology, but also in the content of the results (hence the prefix “Bio”), because the city represents the living environment of the human species. Biourbanism recognizes “optimal forms” defined at different scales (from the purely physiological up to the ecological levels) which, through morphogenetic processes, guarantee an optimum of systemic efficiency and for the quality of life of the inhabitants. A design that does not follow these laws produces anti-natural, hostile environments, which do not fit into an individual’s evolution, and thus fail to enhance life in any way.
Biourbanism acts in the real world by applying a participative and helping methodology. It verifies results inter-subjectively (as people express their physical and emotional wellbeing through feedback) as well as objectively (via experimental measures of physiological, social, and economic reactions).
The aim of Biourbanism is to make a scientific contribution towards: (i) the development and implementation of the premises of Deep Ecology (Bateson) on social-environmental grounds; (ii) the identification and actualization of environmental enhancement according to the natural needs of human beings and the ecosystem in which they live; (iii) managing the transition of the fossil fuel economy towards a new organizational model of civilization; and (iv) deepening the organic interaction between cultural and physical factors in urban reality (as, for example, the geometry of social action, fluxes and networks study, etc.).
This is an intro talk about data visualization, focused on showing few basic concepts on data visualization.
Presented during 1st Machine Learning Meetup - Porto Alegre - 1st June 2016
Presenter - Roberto Silveira
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
91 Free Twitter Tools and Apps to Fit Any NeedBuffer
We’ve collected a great bunch of free tools for Twitter - all the tools we’ve found helpful and many more that we’re excited to try. If there’s a free Twitter tool out there, you’re likely to find a mention here in our list.
Gephi Toolkit Developer Tutorial.
The Gephi Toolkit project package essential modules (Graph, Layout, Filters, IO...) in a standard Java library, which any Java project can use for getting things done. The toolkit is just a single JAR that anyone could reuse.
This tutorial introduce the project, show possibilities and code examples to get started.
This is a presentation I gave in a workshop on "Language, concepts, history" organized by historian Joanna Innes. It took place on Friday 4/22/16 in Somerville College, Oxford.
I was one of the only people present who was not from the humanities, so it was a rather different-than-usual audience and set of participants for me.
I drew some of these slides from other presentations to rather different audiences. I emphasized rather different parts of some of those slides, so I am not sure if the slides on their own give an accurate reflection of the difference between this presentation and some of my other ones.
I thought the presentation went rather well.
Lev Manovich.
How and why study big cultural data.
Presentation at Data Mining and Visualization for the Humanities symposium, NYU, March 19, 2012.
softwarestudies.com
The science of networks is becoming an increasingly important and intriguing area of study that reveals many a patterns and relationships often hidden. This presentation is about the use of SNA to study the network of the Digital Library Community
Presentation given at the HEA Social Sciences learning and teaching summit 'Exploring the implications of ‘the era of big data’ for learning and teaching'.
A blog post outlining the issues discussed at the summit is available via: http://bit.ly/1lCBUIB
Comparison of methods – an unloved duty? Examples from an ongoing bibliometri...Andrea Scharnhorst
Andrea Scharnhorst, Rob Koopman, Shenghui Wang (2016) Comparison of methods – an unloved duty? Examples from an ongoing bibliometric study. Presentation given at eHumanities group, research meeting, Feb 11, 2016
Presentation by Tunde Varga-Atkins at the Methods@Manchester Methods Fair on Creativity in Social Science Research, recorded session available on YouTube (https://www.youtube.com/watch?v=E9tF0C-75A8)
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Interactive visualization and exploration of network data with gephi
1. Interactive visualization and exploration
of network data with gephi
Bernhard Rieder
Universiteit van Amsterdam
Mediastudies Department
and some conceptual context
2. Context
Terms like "big data", "computational social science", "digital humanities",
"digital methods", etc. are receiving a lot of attention.
They point to a set of practices of knowledge production: data analysis,
visualization, modeling, etc.
Instead of a totalizing search for a "logic" of data analysis, we could
inquire into the vocabulary of concepts and analytical gestures that
constitute the practice of data analysis.
A twofold approach to methods:
☉ Engagement, development, application => digital methods
☉ Conceptual, historical, and political analysis and critique => software studies
3. This workshop
How do we talk about data? How do we analyze them? What is our frame
of thought? How do we go further in terms of imagination, expressivity?
☉ Introduction
☉ A bit of math
☉ Two kinds of mathematics
☉ Concepts and techniques from graph theory
☉ Working with gephi
Engage the theory of knowledge (epistemology) mobilized in data analysis,
but through the actual techniques and not generalizing concepts.
4. Basic ideas
Why?
Why do network analysis and visualization? Which arguments are put
forward?
☉ New media: technical and conceptual structures modeled as networks
☉ Calculative capacities: powerful techniques and tools
☉ Visualization: the network diagram, "visual analytics"
☉ Logistics: data and software are available
☉ Methodology: dissatisfaction with statistics (SNA)
☉ Society: diversification, problems with demographics / statistics / theory
6. At the same time, they
produce detailed data
traces that are highly
centralized and searchable.
Much of these data can be
analyzed as graphs.
7. What styles of reasoning?
Hacking (1991) building the concept of "style of reasoning" on A. C.
Crombie’s (1994) "styles of scientific thinking":
☉ postulation and deduction
☉ experiment and empirical research
☉ reasoning by analogy
☉ ordering by comparison and taxonomy
☉ statistical analysis of regularities and probabilities
☉ genetic development
What kind of reasoning are we mobilizing in data analysis?
Is it one type of reasoning or many?
Are we "positivists" when we do data analysis? Reductionists?
8. Quality / quantity
"One of my favorite fantasies is a dialogue between Mills and Lazarsfeld in which the former
reads to the latter the first sentence of The Sociological Imagination: 'Nowadays men often
feel that their private lives are a series of traps.' Lazarsfeld immediately replies: 'How many
men, which men, how long have they felt this way, which aspects of their private lives
bother them, do their public lives bother them, when do they feel free rather than trapped,
what kinds of traps do they experience, etc., etc., etc.' If Mills succumbed, the two of them
would have to apply to the National Institute of Mental Health for a million-dollar grant to
check out and elaborate that first sentence. They would need a staff of hundreds, and when
finished they would have written Americans View Their Mental Health rather than The
Sociological Imagination, provided that they finished at all, and provided that either of them
cared enough at the end to bother writing anything." (Maurice Stein, cit. in Gitlin 1978)
Theory vs. empiricism, macro vs. micro, qualitative vs. quantitative, inductive vs.
deductive, associative vs. formalistic, etc.
The promise of data analysis tools, applied to exhaustive (and cheap) data, is to
bridge the gap, to allow zooming, "quali-quanti" (Latour 2010).
9. Two kinds of mathematics
Can there be data analysis without math? No.
Does this imply epistemological commitments? Yes.
But there are choices, e.g. between:
☉ Confirmatory data analysis => deductive
☉ Exploratory data analysis (Tukey 1962) => inductive
There is a fast growing variety of formal analytical gestures relying on
mathematical modeling and computation.
10. Two kinds of mathematics
Statistics
Observed: objects and properties
Inferred: social forces
Data representation: the table
Visual representation: quantity charts
Grouping: "class" (similar properties)
Graph-theory
Observed: objects and relations
Inferred: structure
Data representation: the matrix
Visual representation: network diagrams
Grouping: "clique" (dense relations)
12. Graph theory
Develops over the 20th century, in particular the second half.
Integrates branches of mathematics (topology, geometry, statistics, etc.).
Graph theory is "the mathematics of structure" (Harary 1965), "a
mathematical model for any system involving a binary relation" (Harary
1969); it makes relational structure calculable.
"Perhaps even more than to the contact between mankind and nature, graph theory owes to
the contact of human beings between each other." (König 1936)
13. Basic ideas
Moreno 1934
Graph theory developed in
exchange with sociometry,
small-group research and
(later) social exchange
theory.
Starting point:
"the sociometric test"
(experimental definition of
"relation")
17. Basic ideas
The late 1990s
The network "singularity":
☉ The network imaginary, a "new science of networks" (Watts 2005)
☉ Computational capacities (memory, speed, interfaces, etc.)
☉ New platforms and datasets
☉ Packaged tools
Different traditions conflate to form network analysis:
☉ Social network analysis and sociometrics
☉ Scientometrics / science and technology studies
☉ Mathematics / physics / computer science
☉ Information and data visualization
☉ Digital sociology / new media studies
19. Formalization
"As we have seen, the basic terms of digraph theory are point and line. Thus, if an
appropriate coordination is made so that each entity of an empirical system is identified
with a point and each relationship is identified with a line, then for all true statements
about structural properties of the obtained digraph there are corresponding true statements
about structural properties of the empirical system." (Harary et al. 1965)
There is always an epistemological commitment!
=> What can "carry" the reductionism and formalization?
=> What types of analytical gestures?
20. Facebook Page "ElShaheeed", June 2010 – June 2011, (Poell / Rieder, forthcoming)
7K posts, 700K users, 3.6M comments, 10M likes (tool: netvizz), work in progress!
25. Basic ideas
What Kind of Phenomena/Data?
Interactive networks (Watts 2004): link encodes tangible interaction
☉ social network
☉ citation networks
☉ hypertext networks
Symbolic networks (Watts 2004): link is conceptual
☉ co-presence (Tracker Tracker, IMDB, etc.)
☉ co-word
☉ any kind of "structure" that can be as point and line
=> do all kinds of analysis (SNA, transportation, text mining, etc.)
=> analyze structure in various ways
26. Basic ideas
What is a graph?
An abstract representation of nodes connected by links.
Two ways of dealing with graphs:
☉ mathematical analysis (graph statistics, structural measures, etc.)
☉ visualization (network diagram, matrix, arc diagram, etc.)
29. Network statistics
betweenness centrality
degree
Relational elements of graphs can
be represented as tables (nodes
have properties) and analyzed
through statistics.
Network statistics bridge the gap
between individual units and the
structural forms they are
embedded in.
This is currently an extremely
prolific field of research.
31. Basic ideas
What is a graph?
Vertices and edges!
Nodes and lines!
Two main types:
Directed (e.g. Twitter)
Undirected (e.g. Facebook)
Properties of nodes:
degree, centrality, etc.
Properties of edges:
weight, direction, etc.
Properties of the graph:
averages, diameter, communities, etc.
43. FB group "Islam is dangerous"
Friendship network, color: betweenness centrality
2.339 members
Average degree of 39.69
81.7% have at least one friend in the group
55.4% five or more
37.2% have 20 or more
founder and admin has 609 friends
44. FB group "Islam is dangerous"
Friendship network, color: Interface language
en_us, de, en_uk, it dominate
54. Thank You
rieder@uva.nl
https://www.digitalmethods.net
http://thepoliticsofsystems.net
"Far better an approximate answer to the right question,
which is often vague, than an exact answer to the wrong
question, which can always be made precise. Data
analysis must progress by approximate answers, at best,
since its knowledge of what the problem really is will at
best be approximate." (Tukey 1962)
Editor's Notes
Anatomy of a tweet. https://twitter.com/ICIJorg/status/321585235491962880https://api.twitter.com/1/statuses/show/321585235491962880.json
Very large scale systems on the one side, but highly concentrated data repositories on the other.The promise of data analysis is, of course, to use that data to make sense of all the complexity.
Reduction to the point and line model requires purging of context, etc. but provides considerable computational capacities. No free lunch, as it were.
Gitlin, The Dominant Paradigm, 1978Many people argue that we no longer need that grant, we already have the data.
Tukey, The Future of Data Analysis, 1962
Allows for all kinds of folding, combinations, etc. – Math is not homogeneous, but sprawling!Different forms of reasoning, different modes of aggregation.These are already analytical frameworks, different ways of formalizing.Statistics: atomism, structure is implicit ("hidden forces", "social forces" cf. Durhkeim) => groups are abstractions, constituted by socioeconomic similaritySocial Network Analysis: atomism, structure is explicit ("dyadic forces") => groups are concrete, constituted by social exchange
Now we can calculate (in particular via matrix algebra).
Handbooks on graph theory are full of exhaustive discussions of basic graph types. Loads of vocabulary and analytical approaches.
http://www.facebook.com/ElShaheeed (Created by WaelGhonim, considered to be a central place for the sparking of the Egyptian Revolution)http://apps.facebook.com/netvizz/ (tool used for extraction)
Classic, simple statistics are often very useful.
It's easy and interesting to produce correlations.
A very different concept of "relation" – no longer derived and probabilistic (correlation) but explicit.Formalization: here: posts and users, but we could do many other things, e.g. users linked by co-reaction, posts linked by co-reaction, we could look at relationships between words, etc.How do we interpret this: understand the platform, understand the context of the phenomenon, understand the algorithm, etc.
How do we interpret something like this?
Visualization is, again, one type of analysis.Which properties of the network are "made salient" by an algorithm?http://thepoliticsofsystems.net/2010/10/one-network-and-four-algorithms/Models behind: spring simulation, simulated annealing (http://wiki.cns.iu.edu/pages/viewpage.action?pageId=1704113)
Non force-based layouts can be extremely useful. Gephi can produce those as well
Extend word lists (what am I missing?), account for refraction. Rieder & Gerlitz 2013: http://journal.media-culture.org.au/index.php/mcjournal/article/viewArticle/620Rieder 2012: http://firstmonday.org/ojs/index.php/fm/article/view/4199/3359
Project variables into the graph User diversity = no of unique users of a hashtag divided by hashtag frequency
Larger roles of hashtags, not all are issue markers!
There is no need to analyze and visualize a graph as a network.Characterize hashtags in relation to a whole. (their role beyond a particular topic sample), better understand our "fishing pole" (the sample technique) and the weight it carries.Tbt: throwback thursday
From DMI workshop on anti-Islamism and right-wing extremism.We can also look at interaction patters: activity structure, held together by leaders?
From DMI workshop on anti-Islamism and right-wing extremism.Netvizz also allows for looking into interaction patterns in groups..
Unique user id allows for large scale analysis.The connectors are often the admins.See: https://wiki.digitalmethods.net/CounterJihadism/ProjectGroup7
We can also look at interaction patters: activity structure, held together by leaders?
Combination of methods is most interesting.The move to posting a larger number of photos is highly successful for this page.What is happening in April 2012?For more details see: https://wiki.digitalmethods.net/CounterJihadism/ProjectGroup3Network diagrams are not so good with time.
Simply looking at "images" in quantitative terms is not enough, here the qualitative part begins and netvizz makes it easier to take that step:Extracting photo URLsExtracting commentsStudying most "successful" tropes in depth, etc.For more details see: https://wiki.digitalmethods.net/CounterJihadism/ProjectGroup3
Network analysis has produced a large number of calculated metrics that take into account the structure of the network."All in all, this process resulted in the specification of nine centrality measures based on three conceptual foundations. Three are based on the degrees of points and are indexes of communication activity. Three are based on the betweenness of points and are indexes of potential for control of communication. And three are based on closeness and are indexes either of independence or efficiency." (Freeman 1979)What concepts are these metricsbased on?
Network metrics are highly dependent on individual variables. Here: the same network with PageRank with four different values for the dampening parameter alpha. (red=highest PR value, yellow=second highest, turquoise=third highest)See Rieder 2012: http://computationalculture.net/article/what_is_in_pagerank