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.
Interactive visualization and exploration of network data with gephiBernhard Rieder
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.
What Data Can Do: A Typology of Mechanisms
Angèle Christin .
International Journal of Communication > Vol 14 (2020) , de Angèle Christin del Departamento de Comunicación de Stanford University, USA titulado "What Data Can Do: A Typology of Mechanisms". Entre otras cosas es autora del libro "Metrics at Work.
OSi Geographic Information Research & Development Initiatives Launch
Ordnance Survey Ireland GI R&D Initiatives
Tuesday, 22 March 2016, 13:00 to 20:30 (GMT) , Maynooth University
Crowdsourcing Approaches for Smart City Open Data ManagementEdward Curry
A wide-scale bottom-up approach to the creation and management of open data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. This talk explores how to involving a wide community of users in collaborative management of open data activities within a Smart City. The talk discusses how crowdsourcing techniques can be applied within a Smart City context using crowdsourcing and human computation platforms such as Amazon Mechanical Turk, Mobile Works, and Crowd Flower.
Interactive visualization and exploration of network data with gephiBernhard Rieder
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.
What Data Can Do: A Typology of Mechanisms
Angèle Christin .
International Journal of Communication > Vol 14 (2020) , de Angèle Christin del Departamento de Comunicación de Stanford University, USA titulado "What Data Can Do: A Typology of Mechanisms". Entre otras cosas es autora del libro "Metrics at Work.
OSi Geographic Information Research & Development Initiatives Launch
Ordnance Survey Ireland GI R&D Initiatives
Tuesday, 22 March 2016, 13:00 to 20:30 (GMT) , Maynooth University
Crowdsourcing Approaches for Smart City Open Data ManagementEdward Curry
A wide-scale bottom-up approach to the creation and management of open data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. This talk explores how to involving a wide community of users in collaborative management of open data activities within a Smart City. The talk discusses how crowdsourcing techniques can be applied within a Smart City context using crowdsourcing and human computation platforms such as Amazon Mechanical Turk, Mobile Works, and Crowd Flower.
Tutorial, Learning Analytics Summer Institute, Ann Arbor, June 2017
As algorithms pervade societal life, they’re moving from an arcane topic reserved for computer scientists and mathematicians, to the object of far wider academic and mainstream media attention (try a web news search on algorithms, and then add ethics). As agencies delegate machines with increasing powers to make judgements about complex human qualities such as ’employability’, ‘credit worthiness’, or ‘likelihood of committing a crime’, we are confronted by the challenge of “governing algorithms”, lest they turn into Weapons of Math Destruction. But in what senses are they opaque, and to whom? And what is meant by “accountable”?
The education sector is clearly not immune from these questions, and it falls to the Learning Analytics community to convene a vigorous debate, and devise good responses. In this tutorial, I’ll set the scene, and then propose a set of lenses that we can bring to bear on a learning analytics infrastructure, to identify some of the meanings that “accountability” might have. It turns out that algorithmic transparency and accountability may be the wrong focus — or rather, just one piece of the jigsaw. Intriguingly, even if you can look inside the algorithmic ‘black box’, which is imagined to lie in the system’s code, there may be little of use there. I propose that a human-centred informatics approach offers a more holistic framing, where the aggregate quality we are after might be termed Analytic System Integrity. I’ll work through a couple of examples as a form of ‘audit’, to show where one can identify weaknesses and opportunities, and consider the implications for how we conceive and design learning analytics that are responsive to the questions that society will rightly be asking.
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
Researching Social Media – Big Data and Social Media AnalysisFarida Vis
Researching Social Media – Big Data and Social Media Analysis, presentation for the Social Media for Researchers: A Sheffield Universities Social Media Symposium, 23 September 2014
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
RUNNING HEADER: Analytics Ecosystem 1
Analytics Ecosystem 4
Analytics Ecosystem
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastasia Rashtchian’s B288 Business Analytics Course.
This paper looks at the nine clusters of the ecosystem. Clustering refers to a system of grouping functions that are similar so as to set them out from others. It begins by highlighting them before proceeding to defining them. It then identifies clusters that represent technology developers and technology users. Peer reviewed materials are used in this endeavor.
They include executive sponsor cluster which contains information that concerns administrators for directing the system. Another one is end-user tools and dashboards cluster that is made of functions that facilitate ability of persons to ultimately engage the system. Data owners cluster is made up of programs that are related to persons who have data in the system. Business users’ cluster is made up of functions that are related to clients of the system. Business applications and systems cluster is made up programs related to features of a given system. Developers cluster is made of programs that are related to the development of programs in the system. Analyst cluster is made up of materials that are related to analysis of data in the system. SME cluster that is made up switches that run SME applications in the system. Lastly, operational data stores that are made up of programs that are concerned with storage of data in a system (Pitelis, 2012).
While developers cluster is made up of technology developers in the system, business users’ cluster is made up of technology users in the system. In conclusion, clustering serves to bring roles together as well as separating roles that are not related in a system (Cameron, Gelbach & Miller, 2012).
They can be represented as follows:-
References
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2012). Robust inference with multiway clustering. Journal of Business & Economic Statistics.
Pitelis, C. (2012). Clusters, entrepreneurial ecosystem co-creation, and appropriability: a conceptual framework. Industrial and Corporate Change, dts008.
Infrastructure
Executive Sponsor Cluster
End-user tools and dashboards cluster
operational data stores
Data Owners Cluster
Business users' cluster
Business systems and applications cluster
Developers Cluster
Analysts Cluster
SME cluster
4
Running head: Sentiment analysis
Sentiment Analysis
Lisa Garay
Rasmussen College
Authors Note
This paper is being submitted for Anastashia Rashtcian’s B288 Business Analytics course.
Sentiment analysis has played a significant role in the concurrent marketing field, specifically in product marketing. According to Somasundaran, Swapna, (2010), the process’ operational module is structured on a data mining sequence, whereby the end users of given particulars the feedback pertaining a used.
This presentation goes over Data Mining the City, a course taught at Columbia University GSAPP. This lecture also covers, complexity, cybernetics and agent based modeling.
FirstReview these assigned readings; they will serve as your .docxclydes2
First:
Review these assigned readings; they will serve as your scientific sources of accurate information:
http://www.closerlookatstemcells.org/Top_10_Stem_Cell_Treatment_Facts.html
http://www.closerlookatstemcells.org/How_Science_Becomes_Medicine.html
http://www.newvision.co.ug/news/649266-fighting-ageing-using-stem-cell-therapy.html
http://www.nature.com/news/stem-cells-in-texas-cowboy-culture-1.12404
http://www.cbc.ca/radio/whitecoat/blog/stem-cell-hype-and-risk-1.3654515
http://stm.sciencemag.org/content/7/278/278ps4.full
Next:
Use a standard Google search for this phrase: “stem cell therapy.” Do not go to Google Scholar. Select one of the websites, blogs, or other locations that offer stem cell therapies.
Save the link for your selected site.
Read the materials provided on your selected site and find out who the authors and sponsors of the site are by going to their “home” or “about us” pages.
Finally, submit your responses to the following in an essay of 500-750 words (2-3 pages of text—use a separate page for a title and for your references):
You are going to prepare a critique of the site you located and compare it to the scientific information available on this therapy.
Give the full title of the website, web blog, or other site that you selected, along with the link.
Describe the therapy that is being offered and what conditions it is designed to treat.
Who are the authors and sponsors of the site you selected?
Compare the claims about the therapy offered to what is said in the assigned readings about this type of therapy. You may have to use our library, as well, to determine what scientists and researchers have to say about the use of stem cells to treat this condition.
Would you say that the therapy you found is a well-established, proven technique for humans, or more of an experimental, unproven approach?
What about the type of language discussed in the Goldman article? Is the therapy you found using sensationalist claims and terminology that are not supported by the scientific research?
Would you recommend that a patient with this condition go ahead and participate in this treatment? Why or why not?
Literature review on how Information Technology has impacted governing bodies’ ability to align public policy with stakeholder needs
Nowadays, the governing bodies both in public and private sectors are dealing with complex systems on a day to day operations. These systems are made up of different components which present varying interactions and interrelationships with and/or among each other; therefore, making their management to be difficult or challenging. Indeed, Ruiz, Zabaleta & Elorza (2016), highlighted that public policymakers have to deal with complex systems which involve heterogeneous agents that act in non-linear behaviors making their management difficult. Neziraj & Shaqiri (2018) also stated that the policymakers are faced with problems which are complex and non-uniform due to a lot of uncertainties and risk situ.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
The Real-time Linked Dataspace (RLD) is an enabling platform for data management for intelligent systems within smart environments that combines the pay-as-you-go paradigm of dataspaces, linked data, and knowledge graphs with entity-centric real-time query capabilities.
The RLD contains all the relevant information within a data ecosystem including things, sensors, and data sources and has the responsibility for managing the relationships among these participants.
It manages sources without presuming a pre-existing semantic integration among them using specialised dataspace support services for loose administrative proximity and semantic integration for event and stream systems. Support services leverage approximate and best-effort techniques and operate under a 5 star model for “pay-as-you-go” incremental data management.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
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.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Engines of Order. Social Media and the Rise of Algorithmic Knowing.
1. Engines of Order
Social Media and the Rise of Algorithmic Knowing
Bernhard Rieder
Universiteit van Amsterdam
Mediastudies Department
2. Starting point
"Algorithms play an increasingly important role in selecting what information is considered
most relevant to us, a crucial feature of our participation in public life." (Gillespie 2015)
From search engines to social media and beyond, the impression is that
socially and culturally relevant tasks are delegated to and performed by
algorithms.
Because algorithms draw together many different things, there are many
ways of beginning to address them.
New forms of "knowing" that have quite different means of producing
knowledge and of making it performative. Can we think of it as a "style of
reasoning" (Hacking 1992)?
3. My approach to the question
As researcher and software developer with the Digital Methods Initiative, I
build and apply tools that contribute to "knowing" what is happening on
social media, most recently:
☉ Netvizz (Facebook data extraction), Rieder 2013
https://apps.facebook.com/netvizz/
☉ DMI-TCAT (DMI Twitter Capture and Analysis Toolkit), Borra & Rieder 2014
https://github.com/digitalmethodsinitiative/dmi-tcat/
This project is more closely aligned with a book project that investigates
the conceptual content and history of algorithmic information processing.
A critical approach is necessary both for my own role in algorithmic
knowledge production and for understanding how social media make use
of algorithms on various levels.
Algorithms used by computational researchers and platforms are similar.
4. algorithminput output
system
in use
system
in use
- interface elements
- contents
- users and uses
- interface elements
- contents
- users and uses
- capture
- formalization
- semantics
- display
- interactivity
- performativity
- techniques
- parameters
- internal states
latent
order
revealed
order
users tweeting, clicking,
navigating, reading, etc.
some math 10 trending phrases
Algorithmic configurations
loads of
data results
possible effects
5. Very large numbers and variety in users,
contents, purposes, arrangements, etc.
"[Commensuration] standardizes
relations between disparate things and
reduces the relevance of context."
(Espeland & Stevens 1998)
6. Platforms like Twitter
provide opportunities for
creating connections
between defined types of
entities (users, messages,
hashtags, resources, etc.).
They formalize and channel
expression, exchange, and
coordination.
"You cannot reply to a
hashtag."
"Simply put, a system can only
track what it can capture, and it
can only capture information that
can be expressed within a
grammar of action that has been
imposed upon the activity." (Agre
7. Using social media and the Web
is like living in a survey.
Or rather, in an experiment,
since so many parameters are
controlled.
Grammars need to become
more pervasive or more explicit
("deeper") so that more
semantic data can be captured.
8. Data pools in social media are
centralized and searchable.
Data is used by social media
platforms at various instances
for various goals.
Data is made accessible at
varying degree to various actors
for various reasons.
9. Taxonomy of the Encyclopédie
(Diderot and d'Alembert ca. 1783)
11. Knowing the many
Similar experience of "too many" in different fields:
☉ Maxwell (1859): even if atoms are fully deterministic, we could never model the
behavior of a gas by observing individual atoms; => statistical mechanics
☉ Foucault (2004): epidemics, economic dynamics, etc. cast doubt on the family as a
model for understanding and governing society; => "population" and social sciences
☉ Bush (1945): "There is a growing mountain of research." => information retrieval
Between 1850 and 1940 many techniques to think and analyze "the many"
are introduced, looking at the structure and dynamics of interacting
ensembles.
The "erosion of determinism" (Hacking 1981) means that modes of
description are increasingly probabilistic and oriented towards "acting in
an uncertain world" (Callon, Lascoumes, Barthe 2001) that can be "tamed"
(Hacking 1990) through statistical techniques.
12. Social media deal with various kinds of "the many" (users,
messages, products, ideas, etc.) and strife to provide
answers to questions like who to talk to, what to read,
where to go, what to buy, etc. in the form of decisions.
They make use of various techniques to algorithmically
reduce complexity to allow continuous activity.
13. From classification to calculation
Classifications as information infrastructures (cf. Bowker and Star 1999) that
orient practice through normalization, standardization, selective
discarding, reformulation, positioning, navigational structuring, etc.
are still relevant.
But various forms of process and calculation are making things much,
much more complicated.
We are currently seeing a race toward understanding the semantics of
expression, behavior, and cultural artifacts.
14.
15. There are different ways of
producing "semantic" data.
Users are not only filling up the fields, they are
increasingly participating in shaping formalizations.
From classifications to classification procedures.
16. "One of the simplest ways to
derive information about a user
is to look at the way he uses the
system." (Rich 1983)
Let's not forget that some of
the valuable data are simply a
byproduct of people using the
system.
17. What are "personal data"?
"Facebook Likes can be used to automatically
and accurately predict a range of highly
sensitive personal attributes including:
sexual orientation, ethnicity, religious and
political views, personality traits,
intelligence, happiness, use of addictive
substances, parental separation, age, and
gender." (Kosinskia, Stillwell, Graepel 2013)
The data used in this study does not
even include friends' likes.
Prediction is determination of
likelihood based on knowledge of
previous events.
18. Data is analyzed and made
performative immediately inside
of the system.
New categories can be derived
from other data and are instantly
made actionable.
19.
20. Recapitulation
By providing functionality through always more fine-grained grammars of
action (and other data capturing techniques), social media platforms
accumulate loads of structured and unstructured data.
The semantization of data in relation to operational contexts (through
formalization, derivation, etc.) begins early on.
Classification is deeply caught up with calculation and process.
21. algorithminput output
system
in use
system
in use
- interface elements
- contents
- users and uses
- interface elements
- contents
- users and uses
- capture
- formalization
- semantics
- display
- interactivity
- performativity
- techniques
- parameters
- internal states
latent
order
revealed
order
Algorithmic configurations
Algorithmic configurations imply "distributed calculative agencies" (Callon
and Munesia 2005) that run through the system and its users.
The data arriving at the algorithm has both latent meaning and order: it is
related to actual practices and not random noise.
30. Techniques
There are many different algorithmic techniques that have complex
histories. Each technique reveals the data from a specific angle, but they
are highly plastic and can be easily combined.
They may be reductionist (e.g. graph theory: everything is a point or line),
but also very generative (unlimited number of "views").
Many techniques focus on the relationship between populations and
individuals. In social media units can be qualified in terms of other units.
All of these techniques are "revealing" (in the sense of Heidegger) the data:
they show certain aspects of the latent order in certain ways; they make truth
that is caught up in a position towards the world, a finality.
37. Parameters
Any somewhat complex technique reacts (strongly) to variation in
parameters and data. This means that without knowledge of parameters
and data, it is hard to understand/critique an algorithm.
A single parameter can encode a commitment to a specific theory of power
(PageRank at low α is "one person, one vote", at high α "patronage of the
powerful").
Parameters are now often set through continuous testing. They are one of
the places where empirical practices and operational goals can be brought
to converge; - automatically.
38. We move from "what should the formula be according to our ideas about
relevance?" to "what has our testing engine identified as the optimal
parameters given our operational goal of more user interaction?".
Whenever you read "n000 factors", machine learning techniques are at work.
39. Machine learning techniques (e.g. Bayesian filters,
maximum entropy classifiers, etc.) can learn to
"interpret" any input signal in relation to
categories, based on feedback ("supervision").
In these techniques, the state of the machine (i.e.
the statistical model) becomes the algorithm.
These self-optimizing, empirical machines are
becoming increasingly common.
40. The "risk technology" is trained by associating "thousands of
pieces of data" with a probability of defaulting or not defaulting.
Every signal receives meaning as predictor for defaulting.
41. States
In digital media, we often need to do preciously little to "make things
calculable", since everything already has been made so.
Algorithms are increasingly empirical knowledge machines, that tie the
"real world" to operational modes of optimization and validation.
The epistemological commitment, then, is no longer to a theory or model,
but to a method for generating models.
The difference is thus not just between the "editorial" and the
"algorithmic" (Gillespie 2012), but also between "editorial algorithms" and
"generated algorithms".
42. "To date, the complexity of mobile and the disparate, closed
platforms that dominate it have caused most people to ignore the
possibility and benefits of A/B testing. […] To us at Taplytics this is
crazy. If you are developing on the web everything is calculated
and optimized and viewed in terms of hypotheses, significance
levels and confidence intervals. On mobile, however, for the past
6 years we have been living in the era of the 'artform' of mobile
apps, where things are viewed in terms of gut feel and shooting
from the hip." (Druxerman 2014)
43. Since the digital operational environment is fully
integrated, data collection, analysis, decision-
making, and execution are all folded into one.
These are engines of order.
44. Conclusions
Moving from classification to calculation implies a move from "thing
concepts" (Dingbegriffe) to "relational concepts" (Relationsbegriffe), of
from substance notions of knowledge to functional ones (cf. Cassirer
1910).
A good analogue to algorithmic configurations on social media platforms
are markets and in particular multi-sided markets (Rochet and Tirole 2004).
Just like markets, algorithmic configurations are "places of truth" (Foucault
2004) not in that they show "the truth" but that truth is produced as a
byproduct of their optimal functioning e.g. the right price, the right
trending topics, the right number and type of stories shown, etc.
The right algorithm is the one that produces an optimal equilibrium
between user satisfaction and value extraction through advertising.
45. Conclusions
"The current mythology of big data is that with more data comes greater accuracy and truth. This
epistemological position is so seductive that many industries, from advertising to automobile
manufacturing, are repositioning themselves for massive data gathering." (Crawford 2014)
This position is problematic and potentially dangerous if it frames
proponents as either naïve ("they don’t know what they are saying") or
cynical ("they don't believe what they are saying").
The danger is not that "big data" acolytes are wrong, but that they are
right. We should consider this as a real possibility.
46. Conclusions
If they are right, we face a series of really big problems:
☉ If better data + algorithms means better truth, we can expect further
concentration and concentric diversification of large Internet companies through
tipping markets;
☉ Operational concepts of knowledge and truth would become even more pervasive;
☉ Privacy issues pale compared to the threat of knowledge monopolization and the
reconfiguration of publicness according to operational goals that are geared
toward profit maximization;
☉ Political institutions and critical forces are direly unprepared for dealing with
algorithmic engines of order, both technically and normatively;
"I will argue that democratic talk is not essentially spontaneous but essentially role-
governed, essentially civil, and unlike the kinds of conversation often held in highest
esteem for their freedom and their wit, it is essentially oriented to problem-solving."
(Schudson 1997)
Question of classification is not new, obviously and conflicts around classification have a long history.
Parameters: a little bit shorter
Image from Techcrunch: http://techcrunch.com/2014/04/03/the-filtered-feed-problem/
The lists can be seen as vectors as well and then treated with the full arsenal of geometry (e.g. to calculate a similarity coefficient between two such vectors)