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Ho John Lee’s Weblog » Wireless

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Slides from the Social Graph Symposium panel

Some introductory slides from a panel session at the Social Graph Symposium.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn . lee @ microsoft . com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn . lee @ microsoft . com
twitter.com/hjl

RESEARCH: Insights from the latest social graph studies
Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World
Speakers:
Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

Slides from the Social Graph Symposium panel

Some introductory slides from a panel session at the Social Graph Symposium.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn . lee @ microsoft . com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn . lee @ microsoft . com
twitter.com/hjl

RESEARCH: Insights from the latest social graph studies
Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World
Speakers:
Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

Slides from the Social Graph Symposium panel

Some introductory slides from a panel session at the Social Graph Symposium.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl

RESEARCH: Insights from the latest social graph studies
Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World
Speakers:
Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

Slides from the Social Graph Symposium panel

Some introductory slides from a panel session at the Social Graph Symposium.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl

RESEARCH: Insights from the latest social graph studies
Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World
Speakers:
Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

Slides from the Social Graph Symposium panel

Here are some introductory slides from a panel session at the Social Graph Symposium.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl

RESEARCH: Insights from the latest social graph studies
Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World
Speakers:
Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

Slides from the Social Graph Symposium panel

Here are some introductory slides from a panel session at the Social Graph Symposium last month.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
RESEARCH: Insights from the latest social graph studies

Moderator: Eric Siegel – President at Prediction Impact and Conference Chair at Predictive Analytics World

Speakers:

Sharad Goel – Research Scientist at Yahoo
Ho John Lee – Principal Program Manager at Microsoft
DJ Patil – Chief Scientist at LinkedIn
Marc Smith – Chief Social Scientist at Connected Action Consulting Group

Slides from the Social Graph Symposium panel

Here are some introductory slides from a panel session at the Social Graph Symposium last month.

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl

Introductory slides from the Social Graph Symposium panel

Here are slides from

Social Graph Symposium Panel – May 2010 – Presentation Transcript

1. Social Graph Symposium Panel
Ho John Lee | Principal Program Manager | Bing Social Search
2. About me:
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl
Past: Bing Twitter (v1), SocialQuant, trading, investing/consulting (China, India)
HP Labs, MIT, Stanford, Harvard
Current: Bing Social Search – graph and time series analysis, data mining
Twitter, Facebook, new products, technical planning
3. What can we do by observing social networks?
On the internet, no one knows you’re a dog.
But in social networks, we can tell if you act like a dog, what groups you belong to, and some of your interests
4. How many Twitter users are there?
from a search on twopular, May 2009
5. Graph analysis for relevance and ranking
Spam marketing campaign
(teeth whitening)
Naturally connected community (#smx)
Real time relevance needs data mining to filter and rank based on history
Spammy communities can be highly visible
Social graph, topic/concept graph, and behavior/gesture graphs are all useful tools
6. Information diffusion in the graph
Observed incidence network of retweets in Twitter
Kwak, Lee, et al, What is Twitter, a Social Network or a News Media? WWW2010
Information flow and behaviors form an implicit interaction graph
7. Topic / sentiment range, volume, trend analysis
What is the baseline rate of mentions / sentiment per unit time?
Look for changes in attention flow around a subject, location, topic
Watch for correlated signals from multiple sources
Consider source relevance and authority as well
8. Applying graph analysis
Attention flow vs information flow
Leads to utility functions, cost functions
Variable diffusion rates by actor / network / info type
Predicting interests and affiliations
Content creation follows attention
Self-organized communities of attention
If there’s no content, you can ask for some
Observable propagation of information
9. Clustering and fuzzing properties and identities
* Frequently used terms can identify interests, affinities, latent query intent
* But can potentially be used to identify likely individual users!
* Infochaff – fuzzing out identity, behavior, properties
10. Thank You
Ho John Lee
hojohn.lee@microsoft.com
twitter.com/hjl

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Rocketboom!


Lately I’ve been looking forward to watching the daily Rocketboom video blog, and have struggled to explain both Rocketboom and video blogging in general to non-blog-reading, TV-watching folks, i.e. most normal people. So until I get around to writing a longer introduction to video (and regular) blogging for my non-blogging / non-blog-reading friends, just go check it out .

Rocketboom features Amanda Congdon reading headlines and incorporating other video blog postings on the internet in a news-style format. It’s a little like Jon Stewart’s Daily Show with a bit of Jane Curtin’s old SNL Weekend Update thrown in, mixing up random video clips.

I enjoyed this clip posted earlier this week, which features David Letterman-style page tossing at the end of each article, David Lynch’s (of Eraserhead, Twin Peaks, Blue Velvet etc) daily weather report from L.A., and a vintage black-and-white television ad for Wham-O frisbees.

Rocketboom looks like it’s sort of a group project at Parsons School of Design in New York, produced by Andrew Baron, who teaches there, so it’s not exactly one person, a camcorder, and a home computer on the internet. But it has relatively good production value and is quite entertaining, for very little investment on their part.

Bookmarks for February 11th through February 12th

These are my links for February 11th through February 12th:

  • Home | Institute for the Study of War – The Institute for the Study of War (ISW) is a non-partisan, non-profit, public policy research organization. ISW advances an informed understanding of military affairs through reliable research, trusted analysis, and innovative education.

    We are committed to improving the nation’s ability to execute military operations and respond to emerging threats in order to achieve U.S. strategic objectives.

    ISW's current research programs focus on the conflicts in Iraq and Afghanistan. Our educational programs include a Military Speakers Series, Warfighters' Workshops, and other courses designed to educate and inform practitioners, policy makers, members of the media, and the public.

  • An Easy Way to Make a Treemap | FlowingData – Step by step instructions for making treemap / heatmap visualizations using R
  • Chip and PIN is Broken – Steven J. Murdoch, Saar Drimer, Ross Anderson, Mike Bond – 2010 IEEE Symposium on Security and Privacy – In this paper we describe and demonstrate a
    protocol flaw which allows criminals to use a genuine card
    to make a payment without knowing the card’s PIN, and
    to remain undetected even when the merchant has an online
    connection to the banking network. The fraudster performs a
    man-in-the-middle attack to trick the terminal into believing
    the PIN verified correctly, while telling the issuing bank that
    no PIN was entered at all. The paper considers how the
    flaws arose, why they remained unknown despite EMV’s wide
    deployment for the best part of a decade, and how they might
    be fixed. Because we have found and validated a practical
    attack against the core functionality of EMV, we conclude
    that the protocol is broken.

Bookmarks for February 11th through February 12th

These are my links for February 11th through February 12th:

  • An Easy Way to Make a Treemap | FlowingData – Step by step instructions for making treemap / heatmap visualizations using R
  • Chip and PIN is Broken – Steven J. Murdoch, Saar Drimer, Ross Anderson, Mike Bond – 2010 IEEE Symposium on Security and Privacy – In this paper we describe and demonstrate a
    protocol flaw which allows criminals to use a genuine card
    to make a payment without knowing the card’s PIN, and
    to remain undetected even when the merchant has an online
    connection to the banking network. The fraudster performs a
    man-in-the-middle attack to trick the terminal into believing
    the PIN verified correctly, while telling the issuing bank that
    no PIN was entered at all. The paper considers how the
    flaws arose, why they remained unknown despite EMV’s wide
    deployment for the best part of a decade, and how they might
    be fixed. Because we have found and validated a practical
    attack against the core functionality of EMV, we conclude
    that the protocol is broken.
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