By The Social Data Lab (socialdatalab.stanford.edu)
In today’s increasingly digitized world, we are creating data in unprecedented ways. Ubiquitous and conspicuous social and mobile connections have empowered social consumers to broadcast their locations, opinions, thoughts and emotions to the world in real-time with nothing more than a few clicks, many times a day, on multiple platforms.
by Jason Lee, Evelyn Larrubia, Sameh El Amawy and Michael Marcotte
In 1993, The New Yorker published a cartoon by Peter Steiner of two dogs at a computer that became an instant classic, tacked up on bulletin boards everywhere. The caption: “On the internet, no one knows you’re a dog.”
A recent survey of a group of 98 tech-savvy Stanford students shows that the world Steiner depicted may be behind us. Asked about how they behave—or would behave—in a variety of situations online, students painted a picture that looked surprisingly like real life.
Hi there, here is the 20-minute audio of the keynote on “The State of the Social Data Revolution” at the 2011 Predictive Analytics World in San Francisco.
Would love to get your comments. Thanks!
Hello, you can do four things with the speech I gave at the 2010 World Innovation Forum in New York:
1. Play or download the mp3 of the speech,
2. Leave your comments on the slides and see the annotations of others,
3. Leave your comments on the transcript and see the annotations of others, and
It was exciting to be part of the World Innovation Forum, an event packed with insights and a turnout of more than 800 thought leaders and a fantastic line-up of speakers. I had great company on stage, speaking between Chip Heath (who I went to grad school with) and Biz Stone (who co-founded Twitter).
I have put up the audio of my talk [mp3, 35 min, 32MB], the transcript [pdf | docx], and the slides [pdf | pptx]. And in terms of press commentary, check out what The Huffington Post, FastCompany, HSM, OnInnovation, and Steve Todd write about it, and please add your own thoughts via the comment box at the bottom of this post.
I am fortunate to present the insights on WIF2010 and the Social Data Revolution by two guest writers: Noah Burbank, a student in Stanford’s Social Data Revolution class this Spring, and Ted Shelton, the CEO of Open-First. And, as always, please do tell us what you think by leaving a comment below. Thanks!
WTF is WIF??
by Chuanyang Chee, Ron Chung, and Andreas Weigend
Curious about the best response to the question from IDEAS PROJECT last week?
1. Ron Chung won the prize for the “best answer”.
Here is what Ron said about a “friend” in the era of the social data revolution:
There are two types of ‘friends’, (i) real ‘close-to-heart’ personal friendships, and (ii) online social friendships.
(i) In real personal friendships you more carefully screen and maintain that relationships. In these situations, you provide more physical and emotional attention compared to online relationships.
(ii) Online social friendships form to maintain touchpoints with people we interact with (sort of like a large addressbook). In the context of consumer internet and social networks/media, an online ‘friend’ is someone you form a weak connection through some form of engagement. This engagement can occur through real world meeting or simply an online exchange (e.g. blog comments, Twitter message, etc).
Also, in these online friendships, there is ambiguity around bilateral versus unilateral ‘friendships’. For example, Twitter uses ‘followers’ & Facebook uses ‘fans’ to represent unidirection relationships and Facebook uses ‘friends’ to denote bilateral friendships. However, some Facebook ‘friendships’ are not truly bilateral. They are simply ways for one side to collect ‘friends’ for the sake of amassing a large audience. All of this points to a desire for people maintain touchpoints with people through online medium should they ever want to re-engage them.
In the end, online social friendships give us ambient awareness of what is going on with people, giving us a type of “reality-TV news” channel.
2. A few thoughts by Andreas Weigend:
Daphna Oyserman suggested:
- Someone whose happiness makes me happy and with whom I feel eager to share my own happiness (knowing that the feeling is mutual).
My favorite one-liner came from Jason Wei in my Stanford class:
- Someone I’m comfortable being myself with.
My own points (to the degree anyone can have their own points after reading through hundreds of responses) would be, that a friend to me is:
- Someone whose eyes I want to see the world through.
- Someone who can make me laugh until tears run down my cheeks.
- Someone who brings the best out of me, accepting me the way I am (or want to be).
- Someone who manages to pull me out of a (real or imagined) bad situation.
Please use the comment box below for your comments. Thanks!
3. Finally, Chuanyang Chee shares his insights on the longer SDR survey.
This survey on the Social Data Revolution was developed by Chuanyang and Andreas and taken by Spring 2010 students at Stanford’s The Social Data Revolution, and Tsinghua’s The Digital Networked Economy.
Finding that long-lost best friend from elementary school has become trivial ever since Facebook hit a total subscription of 400 million active users. But having not kept in touch for a couple of years or decades, what is the point of connecting now? Does he even still consider me a friend? Remember me? Read the rest of this entry »
Listen to the Conversation with Kai Ryssdal (Marketplace) on the Social Data Revolution: Companies get smart on Digital Data. Produced by American Public Media. Broadcast by NPR and Public Radio International on November 18, 200.
And please share what you think… Comment (via Facebook Connect) below!
Transcript (from http://bit.ly/dataNPR)
KAI RYSSDAL: The data trail that we create every day is only growing. Every time we go online, every time we use our cell phones, companies log our preferences. They make suggestions, and they remember what we do. Even though a lot of consumers have gotten used to that, a lot of businesses are still trying to figure out how to use our data to the best effect. One of the first companies to realize the social potential of consumer data was Amazon.com. And Andreas Weigend used to be the chief scientist there. Welcome to the program. Read the rest of this entry »
Download the mp3 of the World Marketing Forum keynote (45MB, 50 minutes, Mexico City, July 1, 2009).
Transcript:
Ladies and gentlemen, it is an honor for me to be here and to talk to you about what I think it the most interesting, the most exciting thing I can talk to you about. Read the rest of this entry »
In 2009, more data will be generated by individuals than in the entire history of mankind through 2008. Information overload is more serious than ever. What are the implications for marketing?
Check out this article at http://blogs.harvardbusiness.org/now-new-next/2009/05/the-social-data-revolution.html
by Ray Bradford and Andreas Weigend. Ray Bradford, currently a student at Stanford University’s Graduate School of Business, is taking Data Mining and E-Business (Stats 252)
You’re working on that big project when momentum stalls at 9:06 PM and you find yourself on Facebook staring at the news feed. You are confronted by a stream of updates from that melodramatic train wreck of a former high school classmate, whose friend request you accepted out of guilt last week. You couldn’t care less Read the rest of this entry »
Yesterday alone, Facebook users issued 21 million friend requests. 17 million requests were accepted. So many new connections, and yet they’re all treated the same—what an oversimplification!
All Facebook links are created equal. But links can differ in strength—for example, a close friend versus a casual acquaintance. Links can be in different categories, like your boss versus a random hookup. And links can be asymmetric—Amy may think that Bob is a good friend, yet Bob may not trust Amy at all! The world is not a binary place.
Discovering Discovery: Don’t ask, Do tell
How can we use data to investigate these different properties of links? Today’s social networks do a lousy job of leveraging our existing data. Why do you need to manually confirm my friend request if we’re already calling, IM-ing, and emailing each other all the time? These data sources should be able to make a good guess about the strength and type of our relationship. Why not use existing data sources to propose better default responses?
If we give our networks a richer structure for our links and relationships, we will also be able to discover interesting facts about ourselves. Why is this important? By investigating implicit relations, we can gain insight into our relationships and how they work. For example, I might be surprised to find out that whenever I email my friend John, he always writes me back promptly whereas I always take 10 times longer to respond to him! Armed with this knowledge, I would ask my system to tell me to get my act together and crank out that response if I’m getting too delinquent.
Facebook 1.0 has helped us create an intimate network of our 17,000 friends. Will Facebook 2.0 help us manage them?
Mind the Explicit, Mine the Implicit
What else can data tell us about the quality of our relationships? One way to use data is to figure out differential interest in budding relationships. It’s easy to do this by looking at communications patterns in email, for example—does one person spend hours crafting that perfect email, only to get a reply that took only a few minutes to write? Or has he suddenly acquired a brand new set of favorite books, movies, and music that just happens to match his new love interest? People leave rich traces on the web—we can discover much more about them than the data they explicitly give.
This is only possible if we can look at the user’s history. After all, we can only make inferences about our behavior if we have a past to compare it against. But this introduces new questions: how much would you pay to know how long Monty spent writing you that email? How much would you pay to keep your data private?
Trust Networks
Social networks are also great for learning about trust. Let’s say that I’m thinking of entering in a business deal with you, but I don’t know you too well. Should I trust you?
There’s an easy way to use the power of networks to answer this question. Let’s just look at all of your other connections: do they trust you? We can give people reputation scores by allowing users to rate their interactions with friends. To make the system even more powerful, we could allow users to link their reputations. To illustrate: let’s say I trust my friend Mike so much that I am willing to attach a trust coefficient of 0.9. This implies that if Mike’s rating goes up by 1, I should get a rating boost of 0.9. Conversely, if someone has a bad experience with Mike and downgrades his rating by 1, my rating will also go down by 0.9. Through the power of the community, reputation ratings would spread quickly. (What trust coefficient would you attach to the author of this post?)
Reward Content Generation
One of the best ways to engage users is to get them to understand how every bit of data they contribute will end up benefiting them. In the example of trust networks, people can improve their own reputations by linking themselves with others. In my previous post on communication, I talked about a system where providing feedback on an email’s relevance would directly benefit you in the future. Online social networks need to reward people to provide explicit data, too.
The Facebook Feed was a brilliant idea for surfacing relevant content created by friends. Ideally, the Feed would create a positive feedback loop: good content provided by friends would get high ratings, which would motivate them to post even more good content. However, an early system of allowing users to rate the submissions of their friends was poorly designed—only 21% of users used the feature. On a rainy day, April 15, 2008, Facebook turned off the feedback system. What a step backward! I wish Facebook instead had created a better machine learning system to reward its users to generate and surface good content.
Social networks based on mutually confirmed binary relations was Day One in evolution of social networks. Introducing, richer semantics, more expressive structures including trust coefficients are the beginning of Day Two. What will the second week bring?
Twitter: @aweigend
- Bye-bye Dublin... And Hello San Francisco!! So happy to be back home in this amazing weather!
- Xin Nian Hao to all my friends here in China and abroad.
- Insightful article on social media in the 29Dec Economist http://t.co/xRhGW8I4 "Too much buzz":