A Summary of Computational Social Science - Lecture 8 in Introduction to Comp...Lauri Eloranta
Final 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
Basics of Computation and Modeling - Lecture 2 in Introduction to Computation...Lauri Eloranta
Second 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
Simulation in Social Sciences - Lecture 6 in Introduction to Computational S...Lauri Eloranta
Sixth 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 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
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
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.
A Summary of Computational Social Science - Lecture 8 in Introduction to Comp...Lauri Eloranta
Final 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
Basics of Computation and Modeling - Lecture 2 in Introduction to Computation...Lauri Eloranta
Second 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
Simulation in Social Sciences - Lecture 6 in Introduction to Computational S...Lauri Eloranta
Sixth 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 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
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
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.
MIT Program on Information Science Talk -- Julia Flanders on Jobs, Roles, Ski...Micah Altman
Julia Flanders, who is the Director of the Digital Scholarship Group in the Northeastern University Library, and a Professor of Practice in Northeastern's English Department gave a talk on Jobs, Roles, Skills, Tools: Working in the Digital Academy as part of the Program on Information Science Brown Bag Series.
In the talk, illustrated by the slides below, Julia discusses the evolving landscape of digital humanities (and digital scholarship more broadly) and considers the relationship between technology, tool development, and professional roles.
For more see: http://informatics.mit.edu/event/brown-bag-jobs-roles-skills-tools-working-digital-academy-julia-flanders
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/
SOLARSPELL: THE SOLAR POWERED EDUCATIONAL LEARNING LIBRARY - EXPERIENTIAL LEA...Micah Altman
Access to high-quality, relevant information is absolutely foundational for a quality education. Yet, so many schools across the developing world lack fundamental resources, like textbooks, libraries, electricity and Internet connectivity. The SolarSPELL (Solar Powered Educational Learning Library) is designed specifically to address these infrastructural challenges, by bringing relevant, digital educational content to offline, off-grid locations. SolarSPELL is a portable, ruggedized, solar-powered digital library that broadcasts a webpage with open-access educational content over an offline WiFi hotspot, content that is curated for a particular audience in a specified locality—in this case, for schoolchildren and teachers in remote locations. It is a hands-on, iteratively developed project that has involved undergraduate students in all facets and at every stage of development. This talk will examine the design, development, and deployment of a for-the-field technology that looks simple but has a quite complex background.
Laura Hosman is Assistant Professor at Arizona State University, holding a joint appointment in the School for the Future of Innovation in Society and in The Polytechnic School. Her work is action-oriented and focuses on the role for information and communications technology (ICT) in developing countries. Presently, she focuses on ICT-in-education projects, and brings her passion for experiential learning to the classroom by leading real-world-focused, project-based courses that have seen student-built technology deployed in schools in Haiti, Vanuatu, Micronesia, Samoa, and Tonga.
Information Science Brown Bag talks, hosted by the Program on Information Science, consists of regular discussions and brainstorming sessions on all aspects of information science and uses of information science and technology to assess and solve institutional, social and research problems. These are informal talks. Discussions are often inspired by real-world problems being faced by the lead discussant.
Brief tutorial on Influence and Homophily in social networks. Key concepts. How to distinguish influence from correlation. Information diffusion processes. Influence Maximization Problem
and viral marketing.
Talk at Bournemouth University 16th September.
The main part of this talk is on the post-hoc analysis of REF data for computing, the apparent bias by sub-area, institution and gender, and the implications of this for policy in UK computing.
In addition I briefly review a number of other areas of my research where data is central.
http://alandix.com/ref2014/2015/09/16/ref-talk-at-bournemouth/
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.
A talk presented at IBM's "Academy of Technology" exploring, in brief, what the research community has to learn from Watson (and the techniques derived therefrom) and some new research ideas that can be explored therefrom. All known proprietary information from either IBM or RPI has been removed from the original talk.
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
This is my attempt at an introduction to data ethics for mathematicians. Mathematicians increasingly need to deal with these kinds of issues, but we don't have the tradition of ethics training from other disciplines.
I welcome comments on how to improve these slides. Did I miss any salient points? Do you want to offer a different perspective on any of these? Do you want to offer any counterpoints? (Please e-mail me directly with comments and suggestions.)
Eventually, I hope to develop these slides further into an article for a venue aimed at mathematical scientists, and of course I would love to have knowledgeable coauthors who can offer a different perspective from mine.
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.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
Field of Study and Research Methods for an Effect of Cognitive and Informatio...Yury Solonitsyn
Authors show one of the possible approaches to the experimental research of the cognitive and information load on PC’s users – the mental load or amount of efforts, spent because of the necessity to recognise screen form’s elements and to process the data presented.
Andrei Balkanskii, Artem Smolin, Yury Solonitsyn
ITMO University, Saint Petersburg, Russia
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
MIT Program on Information Science Talk -- Julia Flanders on Jobs, Roles, Ski...Micah Altman
Julia Flanders, who is the Director of the Digital Scholarship Group in the Northeastern University Library, and a Professor of Practice in Northeastern's English Department gave a talk on Jobs, Roles, Skills, Tools: Working in the Digital Academy as part of the Program on Information Science Brown Bag Series.
In the talk, illustrated by the slides below, Julia discusses the evolving landscape of digital humanities (and digital scholarship more broadly) and considers the relationship between technology, tool development, and professional roles.
For more see: http://informatics.mit.edu/event/brown-bag-jobs-roles-skills-tools-working-digital-academy-julia-flanders
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/
SOLARSPELL: THE SOLAR POWERED EDUCATIONAL LEARNING LIBRARY - EXPERIENTIAL LEA...Micah Altman
Access to high-quality, relevant information is absolutely foundational for a quality education. Yet, so many schools across the developing world lack fundamental resources, like textbooks, libraries, electricity and Internet connectivity. The SolarSPELL (Solar Powered Educational Learning Library) is designed specifically to address these infrastructural challenges, by bringing relevant, digital educational content to offline, off-grid locations. SolarSPELL is a portable, ruggedized, solar-powered digital library that broadcasts a webpage with open-access educational content over an offline WiFi hotspot, content that is curated for a particular audience in a specified locality—in this case, for schoolchildren and teachers in remote locations. It is a hands-on, iteratively developed project that has involved undergraduate students in all facets and at every stage of development. This talk will examine the design, development, and deployment of a for-the-field technology that looks simple but has a quite complex background.
Laura Hosman is Assistant Professor at Arizona State University, holding a joint appointment in the School for the Future of Innovation in Society and in The Polytechnic School. Her work is action-oriented and focuses on the role for information and communications technology (ICT) in developing countries. Presently, she focuses on ICT-in-education projects, and brings her passion for experiential learning to the classroom by leading real-world-focused, project-based courses that have seen student-built technology deployed in schools in Haiti, Vanuatu, Micronesia, Samoa, and Tonga.
Information Science Brown Bag talks, hosted by the Program on Information Science, consists of regular discussions and brainstorming sessions on all aspects of information science and uses of information science and technology to assess and solve institutional, social and research problems. These are informal talks. Discussions are often inspired by real-world problems being faced by the lead discussant.
Brief tutorial on Influence and Homophily in social networks. Key concepts. How to distinguish influence from correlation. Information diffusion processes. Influence Maximization Problem
and viral marketing.
Talk at Bournemouth University 16th September.
The main part of this talk is on the post-hoc analysis of REF data for computing, the apparent bias by sub-area, institution and gender, and the implications of this for policy in UK computing.
In addition I briefly review a number of other areas of my research where data is central.
http://alandix.com/ref2014/2015/09/16/ref-talk-at-bournemouth/
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.
A talk presented at IBM's "Academy of Technology" exploring, in brief, what the research community has to learn from Watson (and the techniques derived therefrom) and some new research ideas that can be explored therefrom. All known proprietary information from either IBM or RPI has been removed from the original talk.
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
This is my attempt at an introduction to data ethics for mathematicians. Mathematicians increasingly need to deal with these kinds of issues, but we don't have the tradition of ethics training from other disciplines.
I welcome comments on how to improve these slides. Did I miss any salient points? Do you want to offer a different perspective on any of these? Do you want to offer any counterpoints? (Please e-mail me directly with comments and suggestions.)
Eventually, I hope to develop these slides further into an article for a venue aimed at mathematical scientists, and of course I would love to have knowledgeable coauthors who can offer a different perspective from mine.
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.
Recommending Items in Social Tagging Systems Using Tag and Time InformationChristoph Trattner
In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.
Field of Study and Research Methods for an Effect of Cognitive and Informatio...Yury Solonitsyn
Authors show one of the possible approaches to the experimental research of the cognitive and information load on PC’s users – the mental load or amount of efforts, spent because of the necessity to recognise screen form’s elements and to process the data presented.
Andrei Balkanskii, Artem Smolin, Yury Solonitsyn
ITMO University, Saint Petersburg, Russia
Social Computing in the area of Big Data at the Know-Center Austria's leading...Christoph Trattner
Nowadays, social networks and media, such as Facebook, Twitter & Co, affect our communication and our exchange of knowledge more than ever. But which additional benefits can offer social media apart from easy interaction with friends and how can they be used to create additional value for companies and institutions? These are the questions that the area Social Computing at Know-Center addresses in detail.
In this talk we will give a brief overview of industry and non-industry related research projects which we have been involved in recently with my group, Social Computing at the Know-Center, in the context of Big Data and social media. In particular, the talk will highlight specific research project outcomes and work-in-progress that make use of social media data to help people to explore the vastly growing overloaded information space more efficiently.
Digital Transformation in Social Sciences - What is Computational Social Science?
A keynote presentation given at the Digital Humanities Morning held at the University of Helsinki, 12.05.2015
By: Lauri Eloranta
twitter: @laurieloranta
Ethical and Legal Issues in Computational Social Science - Lecture 7 in Intro...Lauri Eloranta
Seventh 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
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third 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
User experience (UX) is the basis for all Web activity, and thus underpins everything we do in Web design and development. Successful projects bake UX in from the ground up, from discovery through planning, iteration, testing and deployment. No matter how beautiful our code may be, of what use is it if it’s irrelevant to our users?
Tips from a Design Thinking Perspective, Part 1
See - Understand it:
This is the first of three journeys to travel through a design process. With See - Understand it, we will explore possibilities in your inspiration, research and idea creation process. I am working on a video series which will support this content, so stay tuned!
Optimising Mobile Seminar, Melbourne & Perth-June'13Precedent
Precedent latest "Putting Mobile First" seminar run in Melbourne on the 4 & 5th June and Perth on the 7th June.
John Campbell and Rufus Spiller presented
A brief introduction of Product Designing process followed at www.actiwate.in . Being the UI/UX in-charge i have listed down all the important processes to be followed from the start to the prototyping of the product.
Web UI Design Patterns and best-practices guide from http://www.uxpin.com -- the best online wireframing, UX & product management suite available anywhere.
Content Marketing: Leveraging print, web and mobile to build brand affinity James Windrow
Details the anatomy of a successful content marketing strategy and provides a case study example of a Fortune 100 financial institution.
This was presented during the Online Marketing Summit in May 2010.
3. 1. Select the platform
2. Know the guidelines
3. Do dynamic design
4. Utilize the right kind of media
9 Steps of Mobile 5. Think about the business logic
Magazine Design 6. Data is everything! (design with
analytics)
7. Enable sharing, think about user
generated content
8. Prototype!
9. Think about the overall journalistic
process
4. 1. What is the target platform?
1. 1. Is it right for your customers? 2. Right for your readers? 3. Right for your
business objectives? 4.Right for your current technical solutions? 5. Right
for your investment levels? 6. Right for your magazine concept?
25. Be creative in ad design, the
possibilities are endless.
(gesture based ads, augmented reality ads, location aware, user
profiling based, user action based…)
26. Who pays for the magazine and
how? How does the design
encourage purchases?
27. What are the distribution
channels (linked directly to the
platform)?
28. 6. Data is everything!
(Design with analytics)
30. NO DATA MEANS NO BUSINESS CASE!
» Data is used to link the usage to the
journalistic process
» Data is used to sell ads (to advertisers)
» Data is used to configure content
» Data is used to configure ads
» Use at least Google Analytics as a bare
minimum
31. »Iterative design based
on actual user data
»A/B testing
»Design linked to
actual business goals
40. PARTY LIKE IT’S 1450!
The journalistic process is basically unchanged.
41. Black box of content generation
A Journalist The content is
The content is
Creates distributed in
laid out with
Content (static)
ads
(black box) channels
Where are the feedback loops? Why do we still
generate content based on the old rules of static
daily media?
42. The distribution and consumption
have changed, but the journalistic
tools are still the same.