The document discusses using emerging technologies like social media and mobile devices in survey research. It provides an overview of the evolution of the web from Web 1.0 to the more dynamic Web 2.0. Web 2.0 allows for interactive data through application programming interfaces (APIs) that give access to social media platforms. The document then focuses on Facebook and its API, which provides access to data on users' social networks. Finally, potential uses of social media data for research purposes are discussed, including analyzing trends on Twitter, measuring attitudes through sentiment analysis, and using Facebook for recruitment and surface measures.

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Mobile, Social, Global: Applications of
Emerging Technologies in Survey Research
Adam Sage and Robert Furberg
RTI International
RTI International is a trade name of Research Triangle Institute. www.rti.org

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GRAPH API

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Research in a Web 2.0 World
 The Evolution of the Web – 1.0 to 2.0
 Dynamic and Interactive Data Environments
 APIs and Data Capture
 Facebook API and the Social Graph
 Review of Social Networking Sites
 Facebook
 Surface measures (e.g., “likes,” comments, photos etc)
 Surface utilities (groups, pages, polling, ads)
 The Twitter API
 Trend/Sentiment Analysis
 Diaries
 Social Science 2.0

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What is Web 2.0?
 First introduced to a wide audience in 2003 at the
first Web 2.0 conference
 Associated with the collapse of the dot-com era
 Utilizes the web as a platform for development (as
oppose to releasing software with periodic updates)
 User-centered design – data sources that prop up
services become richer as more people use them
O’reilly, Tim. 2012. “What is Web 2.0” Pp. 32-52 in The Social Media Reader edited by Michael Mandiberg New
York and London, New York University Press..

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The Evolution of the Web
Web 1.0 Web 2.0
 AOL Profiles  Facebook
 Buddy Lists  Friends
 Chat Rooms  Groups/Pages
 Screen Scraping  APIs
 Personal Websites  Blogs
 Online Encyclopedia  Wikipedia
 Publishing  Sharing
 Banner Ads  Targeted Ads

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Research in a Web 2.0 World
 The internet experience is now more dynamic:
 Social
 Interactive
 User-generated and user-sustained
 The value in a Web 2.0 environment is in an
application’s ability to be self-sufficient
 Environments become sustainable when the data that
props-up structure is created and annotated by its users
(e.g., Facebook, Twitter, Wikipedia, Google Maps)

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Architectures of Participation and Communication
 Learn from users/respondents/participants
 Require constant monitoring and updates:
 What functions are used?
 What data is given and in what formats?
 How can we constantly improve user engagement?
 When/how is Ad Hoc Data Collection most conducive?
 How will platform changes require application changes?
 Functionality
 New Features (e.g., Timeline, check-ins)

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Dynamic Data Environments
The Facebook Example: The Facebook (2005)
Buck, Stephanie. 2011. “The Evolution of the Facebook Profile" Retrieved from
http://mashable.com/2011/09/22/facebook-profile-evolution/#270432011--Ticker.

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Dynamic Data Environments
The Facebook Example: The Mini-feed (2006)

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Dynamic Data Environments
The Facebook Example: Interaction (2007)

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Dynamic Data Environments
The Facebook Example: Apps/Pages (2007 – 2009)

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Dynamic Data Environments
The Facebook Example: The Redesign (2010)

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Dynamic Data Environments
The Facebook Example: The Ticker (2011)

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Dynamic Data Environments
The Facebook Example: Timeline (2011)

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API and the Web 2.0 culture
 Application Programming Interfaces (APIs)
 A defining characteristic of Web 2.0
 Web Application: software coded in a web language
(e.g., JavaScript) that is executable through a web browser
 APIs are the access portals to richer data environments
 APIs open the web and create a dynamic atmosphere
 Allows web applications to communicate
 Web applications can make utility of one another (e.g., sharing
Tweets, Pins, or Instagram photos to Facebook)
 Web applications can makes use of objects, processes etc
(e.g., social gaming, quizzes, and readers)

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Applications for Data Collection
 Interactive approach to data collection with opportunities
to:
 Administer surveys
 Measure Context (Network Data)
 Passively collect data (what I call “click and stream”)
 Create data
 Provide tasks with data collection components, much like a lab experiment
 Incorporate other device functions (e.g., location, photo, video
etc.)
 Tap into or integrate with other APIs for additional data
(e.g., Pinterest, Instagram etc.)
Sage, A. J. (2012, May). Facebook Application as a Data Collection Platform. Presented at American
Association for Public Opinion Research Annual Conference, Orlando, FL.
Stillwell, D., & Kosinski, M. (2011). MyPersonality project. Retrieved from
http://www.mypersonality.org/wiki/doku.php

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A Note on Data Types
 Digital vs Digitized Data
 Digital Data
 Native to the platform
 Occur only in a digital environment
 Examples:
 “Likes”
 Status Updates – can have digitized components but are digital by nature
 Tweets – the 140 character format is unique to the platform
 Digitized Data
 Native to the “real world”
 Communicated through digital mediums; can have digital characteristics
 Examples:
 MapMyRun or DailyMile – exercise
 “Tweet What You Eat” – eating behaviors
 GetGlue – TV programs you watch, books you read,

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Socially-integrated Apps
www.tweetwhatyoueat.com; “waterlogged” iPhone app; www.dailymile.com

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Facebook’s API and the Social Graph
 Facebook’s Social Graph
 The objects and connections inside (data)
 Graph API
 Gateway to the Social Graph
 Rich data source (requires authorization or permissions)
 Applications can be used to access Facebook’s Social Graph
 Provide additional utility of Facebook by incorporating aspects of
Facebook within its functionality
 Plugins incorporate Facebook utility into websites
 Users prefer minimal amount of usernames and passwords
 Draws from social graph to streamline Web experience and create a
more open and social Web

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GRAPH API

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GRAPH API

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Another Note on Data Types: Networks
 Social Network data is not new, but the volume is difficult
to ignore
 Social Network data can provide unique insights into the
processes of attitude formation and public opinion:
 Allows us to quantify context
 Allows us to measure some phenomena in new ways, including:
 Communication patterns, information flow
 Measures of influence
 Social positioning (e.g., social distance, in-degrees, out-degrees)

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My Facebook
Network

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Social Networking Sites
 What are the different social networking sites that are
“talking” to one another through APIs?
 Facebook
 Twitter
 Google+
 LinkedIn
 And the other “niche” networks
 Who uses them?

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Social Media Use
 Facebook
 955M MAUs (185M in US & Canada)
 543M Mobile MAUs
 552M DAUs (130M in US & Canada)
 83M fake, duplicate, mis-categorized profiles
 300M photos uploaded and 3.2B “likes” per day
Facebook. (2012). Form 10-Q. Retrieved from
http://www.sec.gov/Archives/edgar/data/1326801/000119312512325997/d371464d10q.htm#tx371464_14

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Social Media Use (cont)
 Twitter
 140M active users*
 140M Active Users (worldwide)
 15% of internet users in US are Twitter users* *
 8% are DAUs **
 9% of cell owners use Twitter on a mobile device**
 340M Tweets per day*
 Who is Tweeting?
 Younger, more urban and suburban, larger portions of
minorities**
*www.twitter.com
**Smith, A. and Brenner J. 2012. “Twitter Use 2012" Retrieved from
http://www.pewinternet.org/~/media//Files/Reports/2012/PIP_Twitter_Use_2012.pdf.

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Social Media Use (cont)
 Other significant social networking sites/apps
 Google+ (400M users, 100M MAUs)*
 LinkedIn (175M users)**
 Pinterest
 Photos/Images/video
 Instagram (owned by Facebook)
 Youtube
 Vimeo
 Color
 Location
 FourSquare (25M users)***
 Yelp
 Nagivation
 Waze (crowdsourced traffic updates)
 Music
 Spotify
*https://plus.google.com/u/0/+VicGundotra/posts/2YWhK1K3FA5
**http://press.linkedin.com/about
***https://foursquare.com/about/

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Who Uses Social Media?*
% of internet users Age
100%
80%
65+ is the fastest
growing age group
60% (150% from 2009
to 2011)
40%
Statistically
significant
20%
0%
18 - 29 30 - 49 50 - 64 65+

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Who Uses Social Media?*
% of internet users Gender
100%
80%
60%
Statistically
significant
40%
20%
0%
Men Women

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Who Uses Social Media?*
% of internet users Race/Ethnicity
100%
80%
60%
40%
20%
0%
White Black Hispanic
Madden, M., Zickuhr, K. 2011. “65% of online adults use social network sites” Retrieved from
http://pewinternet.org/~/media//Files/Reports/2011/PIP-SNS-Update-2011.pdf

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How else can social media be used?
 Facebook
 Surface measures
 Surface utilities
 Twitter
 Measuring
 Trends
 Public Opinion
 Attitudes
 Behaviors
 Other Web 2.0 Concepts

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How is Facebook being used for research?
 Surface Measures
 Status Updates
 Facebook Gross National Happiness Index – not a valid measure of
mood or well-being, but may play role in mood regulation*
 Indicative of the potential for network analysis
 Comments
 Offer similar utility to status updates, but are the unique as 1 of 2 types
of supplemental info for status updates
 Likes
 Can supplement status updates
 What is the meaning of a “like?” What does it translate to?
 Shares
 How does content (e.g., opinions and attitudes) resonate and become
viral?
*Wang, N., Kosinski, M., Stillwell, D.J. & Rust, J. (2012) Can well-being be measured using Facebook status
updates? Validation of Facebook’s Gross National Happiness Index. Social Indicators Research.

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How is Facebook being used for research?
 Surface Utilities
 Recruiting/Sample Building
 Ads
 Snowball*
 Polls**
 One question at a time
 Limitations include selection bias and FB use bias
 Groups as focus group environments
 No known research to date, but potential exists for
 Virtual, on-going focus groups with built-in measurement capabilities
(e.g., polling and comments) and a historical record of interaction
 Tracing***
*Bhutta, C. B. 2012. “Not by the Book: Facebook as a Sampling Frame” Sociological Methods and Research
published online http://smr.sagepub.com/content/early/2012/03/19/0049124112440795.full.pdf+html
**Chang, J. (2010). “How Voters Turned-out of Facebook” retrieved from
http://www.facebook.com/notes/facebook-data-team/how-voters-turned-out-on-facebook/451788333858
***Rhodes, B.B., & Marks, E.L. (2011). “Using Facebook to locate sample members.” Survey Practice, October.
Retrieved from http://surveypractice.wordpress.com/2011/10/24/using-facebook-to-locate-sample-members/

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Facebook Ads and Building Samples
 Targeted Ads
 My research indicates that for many
populations, Facebook ads can be more cost efficient
for developing non-probability samples*
 Well-suited for recruiting convenience samples (focus
groups, cognitive interviews etc.)
 More “traditional” methods (e.g., Craigslist, newspapers)
 Less precise and more vulnerable to “professional participants”
 Check out Brian Head’s paper at SAPOR
 Facebook Ads are still evolving
 Ad placement is now seen in newsfeed
 Mobile!
*Sage, A. J., Richards, A. K., & Dean, E. F. (2012, May). Facebook Ads: An Adaptive Convenience Sample-
Building Mechanism. Poster presented at American Association for Public Opinion Research Annual
Conference, Orlando, FL.

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Twitter API – Trends
 Trends and other pattern recognition
 Tracking phenomena (via text analysis, sentiment analysis)
 Tracking has revealed epidemics weeks prior to health officials*
 Supplementing Surveys**
 Matching against other trend data
 Google
 Longitudinal Surveys
*Chunara, R., Andrews, J. R., and Brownstein, J. S. 2012. “Social and News Media Enabled Estimation if
Epidemiological Patterns Early in the 2010 Haitian Cholera Outbreak. The American Journal of Tropical
Medicine and Hygiene 86:39-45
** Murphy, J., Kim, A., Hagood, H., Augustine, C., Kroutil., Sage, A. 2011. “Twitter Feeds and Google Search
Query Surveillance: Can They Supplement Survey Data Collection?” retrieved on September 17, 2012 from
http://www.rti.org/pubs/twitter_google_search_surveillance.pdf

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Twitter API – Public Opinion and Attitudes
 (Near) Real-time Public Opinion
 Some research suggests sentiment analysis can provide
information similar to polls, however limitations do exist*
 50% of URLs consumed from 20K “elite” users**
 Researchers have demonstrated ability to filter opinion-makers from
opinion-holders***
*O’Connor, B. 2012. “From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series” presented at
American Association for Public Opinion Research Annual Conference in Orlando, FL. Retrieved from
www.brecocon.com
**Wu, S., Hofman, J.M., Mason, M.A., Watts, D. J. 2011. “Who Says What to Whom on Twitter” presented at
20th Annual World Wide Web Conference, ACM, Hyderabad, India. Retrieved from
http://research.yahoo.com/pub/3386
***Finn, S., Mustafaraj, E. 2012. “Real-Time Filtering for Pulsing Public Opinion in Social Media” presented at
25th International Florida Artificial Intelligence Research Society. Retrieved from
http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/viewFile/4459/4762

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Twitter API – Measuring Behaviors
 Diaries*
 Using #hashtags and apps, participants can Tweet and track
 Attitudes
 Opinions
 Health
 Behaviors
 Moods
 Limitations
 140 character limit (although it could be a positive!)
 Privacy
*Cook, S., Richards, A., Dean, E., Haque, S. (2012). “What’s Happening? Twitter for Diary Studies” presented
67th annual American Association for Public Opinion Research (AAPOR) conference in Orlando, FL.

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Twitter API – Early Warning System
 The 5.9 magnitude earthquake that originated in
Mineral, Virginia
 P-waves travel ~ 1,000 miles/minute
 Retweets of the earthquake appeared well over 1,000 miles
away within 60 seconds
*Lotan, G. 2011. “All Shook Up: Mapping Earthquake News on Twitter from Virginia to Maine” Retrieved from
http://blog.socialflow.com/post/7120244132/all-shook-up-mapping-earthquake-news-on-twitter-from-virginia-to-
maine.

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A Note on Wikipedia and Wikis
 Wikis
 Used to create and edit content in a web environment
 Great example of research wiki:
http://mypersonality.org/wiki/doku.php
 Wikipedia
 Crowdsourced, user-generated encyclopedia
 Open sourced (anyone can edit)
 The principle of many-to-many – the wisdom of the crowd
 Crowd Curating
 Wikis and Wikipedia are examples of how survey methodologists
can utilize the knowledge of many to optimize the development and
evolution of methodologies and question types

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Going Mobile
 543M of Facebook’s 955M MAUs are Mobile MAUs
 9% of cell owners use Twitter on a mobile device
 Social Networking platforms are going mobile
– FourSquare, Instagram, Waze are native to mobile
– Facebook’s biggest IPO concern is monetizing mobile
Social Media Use Smartphone Ownership*
100% 80%
80%
60%
60%
40%
40%
20% 20%
0% 0%
18 - 29 30 - 49 50 - 64 65+ 18 - 29 30 - 49 50 - 64 65+
*Raine, Lee. 2012. “Smartphone Ownership Update: September 2012” retrieved from
http://pewinternet.org/Reports/2012/Smartphone-Update-Sept-2012/Findings.aspx

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Feel free to contact me!
Adam Sage
asage@rti.org
@AdamSage
www.linkedin.com/in/adamsage
www.facebook.com/mradamsage