The document summarizes research on analyzing social media data from platforms like Twitter, YouTube, Google+, Blogger and Picasa. It provides statistics on usage and growth rates across these platforms. It then describes several research projects analyzing factors that impact information sharing and recommendation across social streams. One study examined how bilingual users spread information across different languages on Twitter by analyzing a dataset of 62 million tweets in over 100 languages. The accuracy of automated language detection was evaluated against human judges and found to be over 90% for most popular languages.
Location and Language in Social Media (Stanford Mobi Social Invited Talk)Ed Chi
http://forum.stanford.edu/events/2012mobi.php
Title: Location and Language in Social Media
Ed H. Chi
Staff Research Scientist, Google Research
(work done at [Xerox] PARC)
Abstract:
Despite the widespread adoption of social media internationally,
little research has investigated the differences among users of
different languages. Moreover, we know relatively little about how
people reveal their location information. In this talk, I will
outline our recent characterization studies on how users of differing
geographical locations and languages use social media.
First, on geographical location: We found that 34% of users did not
provide real location information in Twitter, frequently incorporating
fake locations or sarcastic comments that can fool traditional
geographic information tools. We performed a simple machine learning
experiment to determine whether we can identify a user’s location by
only looking at what that user tweets.
Second, on language, Examining users of the top 10 languages, we
discovered cross-language differences in adoption of features such as
URLs, hashtags, mentions, replies, and retweets.
We discuss our work’s implications for research on large-scale social
systems and design of cross-cultural communication tools.
Homepage:
edchi.net
Speaker Bio:
Ed H. Chi is a Staff Research Scientist at Google. Until recently, he
was the Area Manager and a Principal Scientist at Palo Alto Research
Center's Augmented Social Cognition Group. He led the group in
understanding how Web2.0 and Social Computing systems help groups of
people to remember, think and reason. Ed completed his three degrees
(B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and
has been doing research on user interface software systems since 1993.
He has been featured and quoted in the press, including the Economist,
Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 90 research articles, his most well-known
past project is the study of Information Scent --- understanding how
users navigate and understand the Web and information environments. He
also led a group of researchers at PARC to understand the underlying
mechanisms in online social systems such as Wikipedia and social
tagging sites. He has also worked on information visualization,
computational molecular biology, ubicomp, and recommendation/search
engines, and has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and
snowboarder.
Enhancing the Social Web through Augmented Social Cognition ResearchEd Chi
Keynote talk given at the International Conference on Asia-Pacific Digital Libraries (ICADL) 2008. December, 2008 in Bali, Indonesia ICADL 2008 link here
We are experiencing the new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by Wikipedia and del.icio.us, Web 2.0 environments are turning people into social information foragers and sharers. Users interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."
PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, sociology, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Web 2.0 systems like social tagging, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.
In this talk, we summarize recent PARC work and early findings on: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.
HCI Korea 2012 Keynote Talk on Model-Driven Research in Social ComputingEd Chi
Model-Driven Research in Social Computing
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.
This Brainmates presentation seeks to answer the question "What is product management?"
This presentation investigates this important strategic role and illustrates its responsibilities and functional applications.
A useful reference for people working in product management or who are interested in a career in this field.
** About Brainmates:
Brainmates is an Australian based business that has is championing the important role that Product Managers perform in delivering a product's that are loved by their customers and deliver a return on investment to the businesses that provide them.
Brainmates trains coaches and supported Product Management Professionals in all kinds of industries and business sizes. Contact the team on +61 1800 272 466 to see if we can help your products and business.
** Connect with Brainmates online:
Visit the Brainmates WEBSITE: http://bit.ly/1lQ51mE
Like Brainmates on FACEBOOK: http://bit.ly/2c0RVaO
Follow Brainmates on TWITTER: http://bit.ly/2bNhKft
Brainmates - Product Management Training and Expertise
Product Manager 101: What Does A Product Manager Actually Do?Chris Cummings
This is an expanded and updated version of the original Product Manager 101. The purpose is to explain the role of the product manager and product management to new and prospective PMs as well as those who will interact with PMs.
Location and Language in Social Media (Stanford Mobi Social Invited Talk)Ed Chi
http://forum.stanford.edu/events/2012mobi.php
Title: Location and Language in Social Media
Ed H. Chi
Staff Research Scientist, Google Research
(work done at [Xerox] PARC)
Abstract:
Despite the widespread adoption of social media internationally,
little research has investigated the differences among users of
different languages. Moreover, we know relatively little about how
people reveal their location information. In this talk, I will
outline our recent characterization studies on how users of differing
geographical locations and languages use social media.
First, on geographical location: We found that 34% of users did not
provide real location information in Twitter, frequently incorporating
fake locations or sarcastic comments that can fool traditional
geographic information tools. We performed a simple machine learning
experiment to determine whether we can identify a user’s location by
only looking at what that user tweets.
Second, on language, Examining users of the top 10 languages, we
discovered cross-language differences in adoption of features such as
URLs, hashtags, mentions, replies, and retweets.
We discuss our work’s implications for research on large-scale social
systems and design of cross-cultural communication tools.
Homepage:
edchi.net
Speaker Bio:
Ed H. Chi is a Staff Research Scientist at Google. Until recently, he
was the Area Manager and a Principal Scientist at Palo Alto Research
Center's Augmented Social Cognition Group. He led the group in
understanding how Web2.0 and Social Computing systems help groups of
people to remember, think and reason. Ed completed his three degrees
(B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and
has been doing research on user interface software systems since 1993.
He has been featured and quoted in the press, including the Economist,
Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 90 research articles, his most well-known
past project is the study of Information Scent --- understanding how
users navigate and understand the Web and information environments. He
also led a group of researchers at PARC to understand the underlying
mechanisms in online social systems such as Wikipedia and social
tagging sites. He has also worked on information visualization,
computational molecular biology, ubicomp, and recommendation/search
engines, and has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and
snowboarder.
Enhancing the Social Web through Augmented Social Cognition ResearchEd Chi
Keynote talk given at the International Conference on Asia-Pacific Digital Libraries (ICADL) 2008. December, 2008 in Bali, Indonesia ICADL 2008 link here
We are experiencing the new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by Wikipedia and del.icio.us, Web 2.0 environments are turning people into social information foragers and sharers. Users interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."
PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, sociology, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Web 2.0 systems like social tagging, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.
In this talk, we summarize recent PARC work and early findings on: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.
HCI Korea 2012 Keynote Talk on Model-Driven Research in Social ComputingEd Chi
Model-Driven Research in Social Computing
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building.
This Brainmates presentation seeks to answer the question "What is product management?"
This presentation investigates this important strategic role and illustrates its responsibilities and functional applications.
A useful reference for people working in product management or who are interested in a career in this field.
** About Brainmates:
Brainmates is an Australian based business that has is championing the important role that Product Managers perform in delivering a product's that are loved by their customers and deliver a return on investment to the businesses that provide them.
Brainmates trains coaches and supported Product Management Professionals in all kinds of industries and business sizes. Contact the team on +61 1800 272 466 to see if we can help your products and business.
** Connect with Brainmates online:
Visit the Brainmates WEBSITE: http://bit.ly/1lQ51mE
Like Brainmates on FACEBOOK: http://bit.ly/2c0RVaO
Follow Brainmates on TWITTER: http://bit.ly/2bNhKft
Brainmates - Product Management Training and Expertise
Product Manager 101: What Does A Product Manager Actually Do?Chris Cummings
This is an expanded and updated version of the original Product Manager 101. The purpose is to explain the role of the product manager and product management to new and prospective PMs as well as those who will interact with PMs.
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
Model-Driven Research in Social Computing
Abstract:
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building
Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
CSCL 2011 Keynote on Social Computing and eLearningEd Chi
Ed H. Chi
Google Research (Work done at Xerox PARC)
CSCL2011 Keynote Abstract:
Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
Indeed, increasingly, new social online resources such as social bookmarking sites and Wikis are becoming central in eLearning. By studying them, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for social computing and social learning.
-----
Large Scale Social Analytics on Wikipedia, Delicious, and Twitter (presented ...Ed Chi
Ed H. Chi, Palo Alto Research Center
Large-Scale Social Analytics in Wikipedia, Delicious, and Twitter
Abstract
We will illustrate an analytical research approach in social computing. Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. The drive to build models and theories for social computing research should further our understanding of how network science, behavioral economics, and evolutionary theories could explain how social systems work. Here we will summarize the published research we conducted on large-scale social analytics in Wikipedia, Delicious, and Twitter, and point out how social analytics can help us understand the intricacies of large social systems.
About the Speaker
Ed H. Chi is area manager and principal scientist at Palo Alto Research Center's Augmented Social Cognition Group. He leads the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, such as the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 70 research articles, he has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
2010 June 13
Keynote talk given at the
Workshop for Modeling Social Media
ACM Hypertext 2010 Conference
Presenter: Ed H. Chi
Talk Title:
Model-driven Research for Augmenting Social Cognition
Short Abstract:
Model-driven research seeks to predict and to explain the phenomena in systems. The drive to do this for social computing research should further our understanding of how these systems evolve and develop. I will illustrate how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I will also show how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia's contribution patterns.
Using Information Scent to Model Users in Web1.0 and Web2.0Ed Chi
This talk summarizes the work I have been doing on modeling user behavior on Web1.0 and Web2.0 systems in the last 13 years
Talk given at a workshop on Cognitive Modeling in Utrecht, Netherlands on March 20, 2010.
2010-03-10 PARC Augmented Social Cognition Research OverviewEd Chi
This is an overview of the 3-year research works done at the Augmented Social Cognition research group at PARC.
See blog at:
http://asc-parc.blogspot.com
2010-02-22 Wikipedia MTurk Research talk given in Taiwan's Academica SinicaEd Chi
This is the talk I gave at the Academica Sinica Inst. for Information Science in Taiwan. It focuses on our Wikipedia and Amazon Mechanical Turk research.
Slowing Growth of Wikipedia and Models of its Dynamic (Presented at Wikimedia...Ed Chi
Presentation given at Wikimedia Foundation and WikiSym 2009 conference on the surprising data that show a change in how Wikipedia is growing, and how the editor population might be changing inside the system. Also shows the resistance to new content, and population shifts.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2017 10-10 (netflix ml platform meetup) learning item and user representation...Ed Chi
Learning item and user representations with sparse data in recommender systems
Ed H. Chi
Google Inc.
Recommenders match users in a particular context with the best personalized items that they will engage with. The problem is that users have shifting item and topic preferences, and give sparse feedback over time (or no-feedback at all). Contexts shift from interaction-to-interaction at various time scales (seconds to minutes to days). Learning about users and items is hard because of noisy and sparse labels, and the user/item set changes rapidly and is large and long-tailed. Given the enormity of the problem, it is a wonder that we learn anything at all about our items and users.
In this talk, I will outline some research at Google to tackle the sparsity problem. First, I will summarize some work on focused learning, which suggests that learning about subsets of the data requires tuning the parameters for estimating the missing unobserved entries. Second, we utilize joint feature factorization to impute possible user affinity to freshly-uploaded items, and employ hashing-based techniques to perform extremely fast similarity scoring on a large item catalog, while controlling variance. This approach is currently serving a ~1TB model on production traffic using distributed TensorFlow Serving, demonstrating that our techniques work in practice. I will conclude with some remarks on possible future directions.
Model-Driven Research in Social Computing
Abstract:
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building
Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
CSCL 2011 Keynote on Social Computing and eLearningEd Chi
Ed H. Chi
Google Research (Work done at Xerox PARC)
CSCL2011 Keynote Abstract:
Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
Indeed, increasingly, new social online resources such as social bookmarking sites and Wikis are becoming central in eLearning. By studying them, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for social computing and social learning.
-----
Large Scale Social Analytics on Wikipedia, Delicious, and Twitter (presented ...Ed Chi
Ed H. Chi, Palo Alto Research Center
Large-Scale Social Analytics in Wikipedia, Delicious, and Twitter
Abstract
We will illustrate an analytical research approach in social computing. Our research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. The drive to build models and theories for social computing research should further our understanding of how network science, behavioral economics, and evolutionary theories could explain how social systems work. Here we will summarize the published research we conducted on large-scale social analytics in Wikipedia, Delicious, and Twitter, and point out how social analytics can help us understand the intricacies of large social systems.
About the Speaker
Ed H. Chi is area manager and principal scientist at Palo Alto Research Center's Augmented Social Cognition Group. He leads the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, such as the Economist, Time Magazine, LA Times, and the Associated Press. With 20 patents and over 70 research articles, he has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
2010 June 13
Keynote talk given at the
Workshop for Modeling Social Media
ACM Hypertext 2010 Conference
Presenter: Ed H. Chi
Talk Title:
Model-driven Research for Augmenting Social Cognition
Short Abstract:
Model-driven research seeks to predict and to explain the phenomena in systems. The drive to do this for social computing research should further our understanding of how these systems evolve and develop. I will illustrate how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I will also show how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia's contribution patterns.
Using Information Scent to Model Users in Web1.0 and Web2.0Ed Chi
This talk summarizes the work I have been doing on modeling user behavior on Web1.0 and Web2.0 systems in the last 13 years
Talk given at a workshop on Cognitive Modeling in Utrecht, Netherlands on March 20, 2010.
2010-03-10 PARC Augmented Social Cognition Research OverviewEd Chi
This is an overview of the 3-year research works done at the Augmented Social Cognition research group at PARC.
See blog at:
http://asc-parc.blogspot.com
2010-02-22 Wikipedia MTurk Research talk given in Taiwan's Academica SinicaEd Chi
This is the talk I gave at the Academica Sinica Inst. for Information Science in Taiwan. It focuses on our Wikipedia and Amazon Mechanical Turk research.
Slowing Growth of Wikipedia and Models of its Dynamic (Presented at Wikimedia...Ed Chi
Presentation given at Wikimedia Foundation and WikiSym 2009 conference on the surprising data that show a change in how Wikipedia is growing, and how the editor population might be changing inside the system. Also shows the resistance to new content, and population shifts.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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1. CIKM 2011 | Invited Talk
Model-Driven Research in Social Computing
Ed H. Chi
Google
Research
Work done while
at Palo Alto
Research Center
(PARC)
2011-10-27 CIKM 2011 Invited Talk 1
2. Some
Google
Social
Stats
n 250,000
words
are
written
each
minute
on
Blogger
-‐
that’s
360
million
words
a
day
n Every
16
seconds
people
view
enough
photos
from
Picasa
Web
Albums
to
cover
an
entire
football
field
n Every
8
minutes,
more
photos
are
viewed
on
Picasa
Web
Albums
than
exist
in
the
entire
Time-‐LIFE
photo
collection
2011-10-27 CIKM 2011 Invited Talk 2
3. YouTube
Stats
n 150
years
of
YouTube
video
are
watched
everyday
on
Facebook
(up
2.5x
y/y)
n every
minute
400+
tweets
contain
YouTube
links
(up
3x
y/y)
[Q1
20111]
n 100M+
people
take
a
social
action
with
YouTube
(likes,
shares,
comments,
etc)
every
week
(10/15/10)
2011-10-27 CIKM 2011 Invited Talk 3
4. Google+
Stats
n 40
million
people
joined
Google
since
launch.
n People
are
2x-‐3x
times
more
likely
to
share
content
with
one
of
their
circles
than
to
make
a
public
post.
2011-10-27 CIKM 2011 Invited Talk 4
5. Social
Stream
Research
n Analytics
– Factors
impacting
retweetability
[Suh
et
al,
IEEE
Social
Computing
2010]
– Location
field
of
user
profiles
[Hecht
et
al,
CHI
2011]
– Organic
Q&A
behaviors
[Paul
et
al,
ICWSM’11]
– Languages
used
in
Twitter
[Hong
et
al,
ICWSM’11]
n Improving
Stream
Experience
– Topic-‐based
summarization
&
browsing
of
tweets
[Bernstein
et
al,
UIST2010]
– Tweet
recommendation
[Chen
et
al,
CHI2010
&
CHI2011]
2011-10-27 CIKM 2011 Invited Talk 5
6. Invisible
Brokerage
Signals
across
Language
Barriers
Joint
work
w/
Lichan
Hong,
Gregorio
Convertino
[Hong
et
al.,
ICWSM
July
2011]
2011-10-27 CIKM 2011 Invited Talk 6
7. Motivation
for
Studying
Languages
n Twitter
is
an
international
phenomenon
– Most
research
focused
on
English
users
– Question
about
generalization
to
non-‐English
– Understand
cross-‐language
usage
differences
– Design
implications
for
international
users
n Research
Questions:
– What
is
the
language
distribution
in
Twitter?
– How
do
users
of
different
languages
use
Twitter?
– How
do
bilingual
users
spread
information
across
languages?
2011-10-27 CIKM 2011 Invited Talk 7
8. Data
Collection
&
Processing
Twitter
stream
04/18/10-‐05/16/10
(4
weeks)
62M
tweets
Google
Language
API
&
LingPipe
104
languages
Top
10
languages
2011-10-27 CIKM 2011 Invited Talk 8
9. Top
10
Languages
in
Twitter
Language
Tweets
%
Users
English
31,952,964
51.1
5,282,657
Japanese
11,975,429
19.1
1,335,074
Portuguese
5,993,584
9.6
993,083
Indonesian
3,483,842
5.6
338,116
Spanish
2,931,025
4.7
706,522
Dutch
883,942
1.4
247,529
Korean
754,189
1.2
116,506
French
603,706
1.0
261,481
German
588,409
1.0
192,477
Malay
559,381
0.9
180,147
2011-10-27 CIKM 2011 Invited Talk 9
10. Human-‐Coding
Study
n 2,000
random
tweets
from
62M
tweets
n 2
human
judges
for
each
of
top
1o
languages
– native
speakers
or
proficient
– discuss
to
resolve
disagreement
n Hard
to
find
Indonesian
&
Malay
judges
n Presented
2,000
tweets
to
each
judge
n Judge
selected
tweets
in
his/her
language
2011-10-27 CIKM 2011 Invited Talk 10
11. Machine
vs.
Human
T-‐P:
true
positive,
T-‐N:
true
negative,
F-‐N:
false-‐negative,
F-‐P:
false
positive
Language
T-‐P
T-‐N
F-‐N
F-‐P
Cohen’s
Kappa
English
974
971
20
35
0.95
Japanese
370
1,595
0
35
0.94
Portuguese
170
1,803
19
8
0.92
Indonesian
106
1,875
15
4
0.91
Spanish
96
1,889
11
4
0.92
Dutch
18
1,978
2
2
0.90
Korean
24
1,976
0
0
1.00
French
13
1,980
0
7
0.79
German
12
1,979
2
7
0.72
Malay
8
1,979
4
9
0.55
2011-10-27 CIKM 2011 Invited Talk 11
12. Accuracy
of
Language
Detection
n Two
Types
of
Errors
– Got
ur
dirct
msg.i’m
lukng
4wrd
2
twt
wit
u
too.so,wat
doing
ha…(detected
as
Afrikaans)
– High
error
rate
for
tweets
of
1~2
words
2011-10-27 CIKM 2011 Invited Talk 12
13. Machine
vs.
Human
Language
T-‐P
T-‐N
F-‐N
F-‐P
Cohen’s
Kappa
French
13
1,980
0
7
0.79
German
12
1,979
2
7
0.72
Malay
8
1,979
4
9
0.55
• French:
5/7
F-‐P
have
2
words
• German:
1/2
F-‐N
has
1
word;
6/7
F-‐Ps
are
in
English
• Malay:
3/4
F-‐Ns
&
7/9
F-‐Ps
are
in
Indonesian
2011-10-27 CIKM 2011 Invited Talk 13
15. Use
of
URLs
in
62M
Tweets
Language
URLs
n Chi
Square
tests
confirmed
that
All
21%
differences
by
language
are
English
25%
significant.
Japanese
13%
Portuguese
13%
Indonesian
13%
Spanish
15%
Dutch
17%
Korean
17%
French
37%
German
39%
Malay
17%
2011-10-27 CIKM 2011 Invited Talk 15
16. Significant
Cross-‐Language
Differences
Language
URLs
Hashtags
Mentions
Replies
Retweets
All
21%
11%
49%
31%
13%
English
25%
14%
47%
29%
13%
Japanese
13%
5%
43%
33%
7%
Portuguese
13%
12%
50%
32%
12%
Indonesian
13%
5%
72%
20%
39%
Spanish
15%
11%
58%
39%
14%
Dutch
17%
13%
50%
35%
11%
Korean
17%
11%
73%
59%
11%
French
37%
12%
48%
36%
9%
German
39%
18%
36%
25%
8%
Malay
17%
5%
62%
23%
29%
Chi
Square
tests
confirmed
that
differences
by
language
are
significant
2011-10-27 CIKM 2011 Invited Talk 16
17. Implications
Language
URLs
Hashtags
Mentions
Replies
Retweets
All
21%
11%
49%
31%
13%
Korean
17%
11%
73%
59%
11%
German
39%
18%
36%
25%
8%
n Use
of
Twitter
for
social
networking
vs.
information
sharing
different
in
different
languages
n Design
of
recommendation
engines
– Korean
users:
promote
conversational
tweets
– German
users:
promote
tweets
with
URLs
2011-10-27 CIKM 2011 Invited Talk 17
18. Studying
Bilingual
Brokers
n Importance
of
brokers
– Structural
holes
(Burt’92),
LiveJournal
(Herring
et
al’07)
n Define
bilingual
brokers
as
Users
who
tweeted
in
a
pair
of
languages
n Caveat
– Under-‐estimated
due
to
4-‐week
time
limit
– Over-‐estimated
due
to
language
detection
errors
2011-10-27 CIKM 2011 Invited Talk 18
19. Number
of
Bilingual
Brokers
E
J
P
I
S
D
K
F
G
J
140,730
P
488,545
13,228
I
230,023
4,825
29,405
S
359,117
10,139
112,524
36,068
D
150,041
6,383
30,855
34,906
30,916
K
19,722
6,384
906
2,014
1,109
972
F
194,931
10,463
53,607
34,586
49,445
33,568
1,244
G 110,748
6,053
22,106
21,471
21,989
22,162
786
24,763
M 148,365
4,208
31,184
135,427
31,967
29,331
1,518
30,257
18,301
2011-10-27 CIKM 2011 Invited Talk 19
20. Sharing
URLs
Across
Languages
E
J
P
I
S
D
K
F
G
M
E 3,013
18,399
985
4,986
1,144
212
1,791
1,647
540
J
3,013
77
37
58
29
43
59
46
18
P 18,399
77
74
1,644
198
2
453
168
123
I
985
37
74
67
64
1
53
38
279
S 4,986
58
1,644
67
139
0
286
139
53
D 1,144
29
198
64
139
2
112
126
48
K 212
43
2
1
0
2
3
3
1
F
1,791
59
453
53
286
112
3
157
53
G 1,647
46
168
38
139
126
3
157
40
M 540
18
123
279
53
48
1
53
40
2011-10-27 CIKM 2011 Invited Talk 20
21. Sharing
Hashtags
Across
Languages
E
J
P
I
S
D
K
F
G
M
E 8,178
33,197
14,96 27,284
6,685
798
9,410
7,208
5,517
9
J
8,178
331
135
351
218
149
352
260
100
P 33,197
331
535
4,682
604
13
1,231
580
400
I
14,969
135
535
762
684
25
713
415
6,046
S 27,284
351
4,682
762
819
28
1,468
708
463
D 6,685
218
604
684
819
26
851
769
424
K 798
149
13
25
28
26
25
18
20
F
9,410
352
1,231
713
1,468
851
25
879
411
G 7,208
260
580
415
708
769
18
879
265
M 5,517
100
400
6,046
463
424
20
411
265
2011-10-27 CIKM 2011 Invited Talk 21
22. Implications
n Indicators
of
connection
strength
between
languages
– Number
of
bilingual
brokers
– Acts
of
brokerage:
sharing
URLs
&
hashtags
n English
well
connected
to
others,
and
may
function
as
a
hub
n Need
to
improve
cross-‐language
communications
2011-10-27 CIKM 2011 Invited Talk
? 22
23. Visible
Social
Signals
from
Shared
Items
Kudos
to
Jilin
Chen,
Rowan
Nairn
[Chen
et
al,
CHI2010]
[Chen
et
al.,
CHI2011]
2011-10-27 CIKM 2011 Invited Talk 23
25. Information
Gathering/Seeking
n The
Filtering
Problem:
– “I
get
1,000+
items
in
my
stream
daily
but
only
have
time
to
read
10
of
them.
Which
ones
should
I
read?”
n The
Discovery
Problem:
– “There
are
millions
of
URLs
posted
daily
on
Twitter.
Am
I
missing
something
important
there
outside
my
own
Twitter
stream?”
2011-10-27 CIKM 2011 Invited Talk 25
26. Stream
Recommender
n Zerozero88.com
– Twitter
as
the
platform
– URLs
as
the
medium
– Produces
your
personal
headlines
2011-10-27 CIKM 2011 Invited Talk 26
27. URL Sources
Topic Relevance
User Topic Profiles
Scores
Social Network Scores Local Social Network
Recommendation Engine
Ø Multiply scores
Ø Rank URLs using multiplied scores
Ø Recommend highest ranked URLs
2011-10-27 CIKM 2011 Invited Talk 27
28. URL
Sources
n Considering
all
URLs
was
impossible
n FoF:
URLs
from
followee-‐of-‐followees
– Social
Local
News
is
Better
n Popular:
URLs
that
are
popular
across
whole
Twitter
– Popular
News
is
Better
Component Possible Design Choices
URL Sources FoF (followee-of-followees)
Popular
2011-10-27 CIKM 2011 Invited Talk 28
29. URL Sources
Topic Relevance
User Topic Profiles
Scores
Social Network Scores Local Social Network
Recommendation Engine
Ø Multiply scores
Ø Rank URLs using multiplied scores
Ø Recommend highest ranked URLs
2011-10-27 CIKM 2011 Invited Talk 29
31. Topic
Profile
of
URLs
n Built
from
tweets
that
contain
the
URL
n However,
tweets
are
short
– term
vectors
for
URLs
are
often
too
sparse
n Adopt
a
term
expansion
technique
using
a
search
engine
Best
of
Show
CES
2011:
The
Motorola
Atrix
http://tcrn.ch/e0g3Oh
Add to
Profile
smartphone,
mobility, …
2011-10-27 CIKM 2011 Invited Talk 31
32. Topic
Profile
of
Users
n Self-‐Topic:
content
profile
based
on
my
posts
– My
Interest
as
Information
Producer
n Followee-‐Topic:
content
profile
based
on
my
followees’
posts
– My
Interest
as
Information
Gatherer
n None,
for
comparison
purpose
Component Possible Design Choices
Topic Self-Topic
Relevance Followee-Topic
Scores None
2011-10-27 CIKM 2011 Invited Talk 32
33. My
Followees
Profile Profile
Profile Profile
Collect & Profile
Profile
Profile Profile
Profile Profile
Profile
A term is weighted higher in your profile if Find Top
more of your followees have the term as Key Terms
their top key terms
Terms Terms
Terms Terms
Profile Aggregate Terms
Terms Terms
Terms Terms
Terms
2011-10-27 CIKM 2011 Invited Talk 33
34. URL Sources
Topic Relevance
User Topic Profiles
Scores
Social Network Scores Local Social Network
Recommendation Engine
Ø Multiply scores
Ø Rank URLs using multiplied scores
Ø Recommend highest ranked URLs
2011-10-27 CIKM 2011 Invited Talk 34
35. Social
Network
Scores
n “Popular
Vote”
in
among
my
followees-‐of-‐followees
– People
“vote”
a
URL
by
tweeting
it
– URLs
with
more
votes
in
total
are
assigned
higher
score
– Votes
are
weighted
using
social
network
structure
n None,
for
comparison
purpose
Component Possible Design Choices
Social Social Voting
Network None
Scores
2011-10-27 CIKM 2011 Invited Talk 35
36. The
Intuition:
Local
Influence
follow
15 People
follows
Whose URLs should be
weighted higher?
Me
follows
5 People follow
2011-10-27 CIKM 2011 Invited Talk 36
37. Possible
Recommender
Designs
Component Possible Design Choices
URL Sources FoF (followee-of-followees)
Popular
Topic Self-Topic
Relevance Followee-Topic Recommendation Engine
Scores None
Social Social Voting Ø Multiply scores
Network None Ø Rank URLs using multiplied scores
Scores Ø Recommend highest ranked URLs
• 2 (URL source) x 3 (topic score) x 2 (social score) = 12
possible algorithm designs in total"
• Random selection if for both scores we chose None"
2011-10-27 CIKM 2011 Invited Talk 37
38. Study
Design
n Within-‐subject
design
n Each
subject
evaluated
5
URL
recommendations
from
each
of
the
12
algorithms
– Show
60
URLs
in
random
order,
and
ask
for
binary
rating
– 60
ratings
x
44
subjects
=
2640
ratings
in
total
39. Summary
of
Results
Popular URLs
FoF URLs
Social Vote Only
Best Performing
2011-10-27 CIKM 2011 Invited Talk 39
39
40. Algorithms
Differ
Not
Only
in
Accuracy!
n Relevance
vs.
Serendipity
in
recommendations
n From
a
subject
in
the
pilot
interview
of
zerozero88:
– “There
is
a
tension
between
the
discovery
and
the
affirming
aspect
of
things.
I
am
getting
tweets
about
things
that
I
am
already
interested
in.
Something
I
crave
…,
is
an
element
of
surprise
or
whimsy.
...
I
am
getting
a
lot
of
things
I
am
interested
in,
but
that
is
not
necessarily
a
good
thing
for
me
personally”
2011-10-27 CIKM 2011 Invited Talk 40
41. Design
Rule
n Interaction
costs
determine
number
of
people
who
participate
# People willing to participate
– Surplus
of
attention
&
motivation
at
small
transaction
costs
n Therefore:
n Important
to
keep
interaction
costs
low
– Recommendation
– Summarization
Cost of participation
n Or
bring
new
benefits
2008-05-13 CSCL 2011 Keynote