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StayConnected app measures friend connectedness
- 1. Megan Cartwright StayConnected
- 2. Regan Sound Familiar?
- 3. Now there are two!?
- 4. Now there are two!? Sound Familiar?
- 5. How do you re-connect? Go to: See the friends Login with stayconnected- you can Facebook app.com reconnect with!
- 6. How do you measure connectedness? Likes on Number Number of your of Comments on tags in Comments photos messages your photos photos and on friends’ statuses status Likes on your Likes on friend’s status status Comments on Comments on your status friend’s photos PCA Likes on Common likes friend's photos
- 7. PCA determines the most meaningful interactions! Likes on Number Number of your of Comments on tags in Comments photos messages your photos photos and on friends’ statuses status Likes on your Likes on friend’s status status Comments on Comments on your status friend’s photos PCA Likes on Common likes friend's photos
- 8. Friend “Connectedness” Closest Friends Meaningful Interactions Total Interactions
- 9. Friend “Connectedness” Closest Friends Meaningful Interactions Less Close Friends Total Interactions
- 10. Friend “Connectedness” Closest Friends Meaningful Interactions Somewhat Close Friends Less Close Friends Total Interactions
- 11. Tools Used
- 12. About Megan. B.S. Physics UW, Seattle *GO HUSKIES! Ph.D. Space Physics, UCLA Postdoc, UC Berkeley You’ll find me: *Snowboarding *Traveling
- 13. Friend “Connectedness” Meaningful Interactions Positive Slope: Your friends communicate more with you Negative Slope: You communicate more with your friends Total Interactions
Notes de l'éditeur
- How do you re-connect? Which friends are the friends you haven’t talked to lately, that are still important to you?
- My friends mean a lot to me as I’m sure your friends mean a lot to you.We enjoy spending time with our friends, participating in each others lives, But life often gets in the way and despite best intentions we lose track of our friends.Social networks do a great job of making us feel connected, we can see what's going on in people’s livesBut how connected are we really?
- My friends mean a lot to me as I’m sure your friends mean a lot to you.We enjoy spending time with our friends, participating in each others lives, But life often gets in the way and despite best intentions we lose track of our friends.Social networks do a great job of making us feel connected, we can see what's going on in people’s livesBut how connected are we really?
- My friends mean a lot to me as I’m sure your friends mean a lot to you.We enjoy spending time with our friends, participating in each others lives, But life often gets in the way and despite best intentions we lose track of our friends.Social networks do a great job of making us feel connected, we can see what's going on in people’s livesBut how connected are we really?
- How do you re-connect? Which friends are the friends you haven’t talked to lately, that are still important to you?
- The app will measure how connected you are to your friends – but how does it do that?There are so many ways we interact with our friends on Facebook…what are the important features?Using a procedure called principal component analysis we can determine which features are most important.This PCA analysis uses an orthogonal transformation that converts a set of potentially correlated variables into a set of linearly uncorrelated variables called principal components, where the first principal component is the largest variance and so far down to the smallest variance component. This is based on a normal distribution, which is not necessarily the case here – but it is a convenient way to reduce the dimensionality of our features space (where we are finding a direction onto which to project the data which minimizes the error).
- This application found that the most important features (by an order of magnitude) are commenting and liking our statuses. The interesting thing about this is we can use that information to find how much we communicated with our friends and vice versa. (next plot)Describe how these red boxes are the meaningful interactions
- Using the most important features:I normalized them by Subtracting the number of your likes from the number of your friend likes for each friendNormalized this by using a sigmoid function (1 / 1 +exp(-constant*subtraction))Summed the two normalized features (# of likes and # of comments) = Ratio of CommunicationFound the k-means clusteringCluster at the top of my closest friends – zoom in that Cluster at the bottom of my least close friends – zoom in on thatBut these are the friends I most care about the ones in the middle
- Using the most important features:I normalized them by Subtracting the number of your likes from the number of your friend likes for each friendNormalized this by using a sigmoid function (1 / 1 +exp(-constant*subtraction))Summed the two normalized features (# of likes and # of comments) = Ratio of CommunicationFound the k-means clusteringCluster at the top of my closest friends – zoom in that Cluster at the bottom of my least close friends – zoom in on thatBut these are the friends I most care about the ones in the middle
- Using the most important features:I normalized them by Subtracting the number of your likes from the number of your friend likes for each friendNormalized this by using a sigmoid function (1 / 1 +exp(-constant*subtraction))Summed the two normalized features (# of likes and # of comments) = Ratio of CommunicationFound the k-means clusteringCluster at the top of my closest friends – zoom in that Cluster at the bottom of my least close friends – zoom in on thatBut these are the friends I most care about the ones in the middle
- Using the most important features:I normalized them by Subtracting the number of your likes from the number of your friend likes for each friendNormalized this by using a sigmoid function (1 / 1 +exp(-constant*subtraction))Summed the two normalized features (# of likes and # of comments) = Ratio of Communication
- Using the most important features:I normalized them by Subtracting the number of your likes from the number of your friend likes for each friendNormalized this by using a sigmoid function (1 / 1 +exp(-constant*subtraction))Summed the two normalized features (# of likes and # of comments) = Ratio of Communication
- Using the most important features:I normalized them by Subtracting the number of your likes from the number of your friend likes for each friendNormalized this by using a sigmoid function (1 / 1 +exp(-constant*subtraction))Summed the two normalized features (# of likes and # of comments) = Ratio of CommunicationFound the k-means clusteringCluster at the top of my closest friends – zoom in that Cluster at the bottom of my least close friends – zoom in on thatBut these are the friends I most care about the ones in the middle