Facebook and Big Data. Prof. Lewis Friedland J201 April 26, 2016

Transcription

1 Facebook and Big Data Prof. Lewis Friedland J201 April 26, 2016

2 Today 1) Facebook and data 2) Growth of multisided marketplaces 3) Power Laws 4) Where to go?

3 Facebook Gigantic company; ~$17.93 bn annual revenue

4 What does it sell? It sells you. Your lives, your relationships, your photos, your interests, your Your time and attention The ultimate individual-level data repository

5 Where the data comes from What we tell Facebook Interests, Pages we like, our gender, age, what we post about What our friends tell Facebook What Facebook gathers about us directly When we log in, from where, what kind of device What Facebook gathers as we surf the web Using trackers or cookies

6 What Facebook knows about you ch?v=kjvauqs3 Ofg

7 And Facebook is not alone Ongoing arms race to learn more about us and deliver experiences that online platforms think we will like more so we will spend more time and attention and be available to advertisers Social media News (Google News) Search engines And more

8 New Strategy for Social Media Audiences now don t have to leave social media at all. Facebook hosts articles created by outside publishers, videos, live broadcasts. Snapchat, online media in miniature Rise of multisided marketplaces. Publications as multi-sided marketplaces: readers, advertisers, writers. Facebook: foursided marketplace: users, advertisers, third-party application developers and users of Facebook Connect.

9 Where Do Media Fit In? 5 th side: premium content providers. Have to play: no choice. Lowering of switching costs for advertisers. What happens when media providers join the FB platform?

10 Platforms Commoditize They make it very easy for the best to rise to the top. Make it very difficult for anyone who is not best (or cheapest). Winner-take all economy. Application of Power laws to content. Platforms force specialization.

11 Wealth, also known as net worth, is defined as the total value of everything someone owns minus any debt that he or she owes. A person's net worth includes his or her bank account savings plus the value of other things such as property, stocks, bonds, art, collections, etc., minus the value of things like loans and mortgages.

12 Example: Wealth Distribution

13 Michael Norton, Harvard Bus School Dan Ariely, Duke

14 Power Law Distributions or 80/20 Rules

15

16 Is this a good thing? Well, it sure can be convenient Can find whatever you want Can connect to many many people And Facebook offers a remarkable service that people want

17 But Are power law distributions of wealth and information good for society? As information becomes more essential to everyone jobs, education, life chances how do power laws effect them? How should we feel about so much information sitting in FB s servers? Are we losing some of the randomness and serendipity we need in a diverse society?

18 Eli Pariser, The Filter Bubble

19 Politics Big data is reinventing how political campaigns operate Key innovations: (1) data merging (2) culture of testing (3) nano-targeting

20 Data merging Often there is very useful data on people, but it s not in the same place So, merge it ß hard but effective Especially: Data from voter rolls Consumer data What does this tell you?

21 Testing ( A/B Testing ) Used to be very hard to know what messages worked Now, instant data Sanders wants to raise money, sends an to 2 million supporters Sends 4 different versions, sees which one gets the best response Uses that one on future appeals

22 Nanotargeting Narrowcasting to the next level Use fine-grained individual-level data and communication media to reach individuals (e.g., , social media, direct mail) Predict their positions on key issues Use testing to determine what appeals most likely to succeed

23 Example You re Ted Cruz trying to win Wisconsin A voter in Superior who fits a profile of being a hunter and a union member à Target this person with 2 nd amendment messages, NEVER mention economic policy A voter in Waukesha who may be fiscally conservative and socially liberal à Send anti-tax messages, NEVER mention abortion, gay rights,etc.

24 Concerns What does this do to our sense of being a nation? Is there any national conversation if we each are receiving different messages? Is there any chance we can come together on what is most important?

25 At the same time Some of the innovations may be spurring more face-to-face, grassroots participation Connected users to events, phone numbers, addresses

26 So what should you do? Don t be afraid of data Bad at math? Don t worry about it Still, get some basic skills Take some statistics Learn some simple programming: python, R Learn about researchmethods Learn about data visualization Read

27 Some sources Sasha Issenberg, The Victory Lab (2012) Nate Silver, The Signal and the Noise (2012) Eli Pariser, The Filter Bubble (2010) Daniel Kreiss, Taking Our Country Back (2012) James Gleick, The Information (2011)

28 On Facebook