website: https://www.chrisbail.net
github: https://github.com/cbail
Twitter: https://www.twitter.com/chris_bail

Incomplete
Inaccessible
Non-Representative
Drifting
Algorithmic Confounding
Dirty
Sensitive

-Survey response rates continue to drop

-Survey response rates continue to drop

-Many of the most important questions require longitudinal/relational/qualitative data

-Survey response rates continue to drop

-Many of the most important questions require longitudinal/relational/qualitative data

-Digital trace data have a number of major advantages that conventional sources do not (big, always on, non-reactive)

Incomplete
Inaccessible
Non-Representative
Drifting
Algorithmic Confounding
Dirty
Sensitive

A web or mobile-based tool built by a researcher in order to:

A web or mobile-based tool built by a researcher in order to:

a) collect public and/or private data produced by social media users from an API;

A web or mobile-based tool built by a researcher in order to:

a) collect public and/or private data produced by social media users from an API;

b) collect supplemental information from such users (e.g. demographics) using more conventional survey methods;

A web or mobile-based tool built by a researcher in order to:

a) collect public and/or private data produced by social media users from an API;

b) collect supplemental information from such users (e.g. demographics) using more conventional survey methods;

c) offer something back to the user as an incentive to share their data (e.g. analysis or financial incentives)

A web or mobile-based tool built by a researcher in order to:

a) collect public and/or private data produced by social media users from an API;

b) collect supplemental information from such users (e.g. demographics) using more conventional survey methods;

c) offer something back to the user as an incentive to share their data (e.g. analysis or financial incentives)

For additional information, see:

Bail, Christopher A. 2015. “Taming Big Data: Using App Technology to Study Organizational Behavior on Social Media.” Sociological Methods and Research: 1-29

1. Approximately 40% of all organizations contacted installed the app (across two fields)

Bail, Christopher A. 2016. “Combining Network Analysis and Natural Language Processing to Examine how Ad- vocacy Organizations Stimulate Conversation on Social Media.” Proceedings of the National Academy of Sciences, 113:42 11823-11828

Bail, Christopher A. 2016. “Emotional Feedback and the Viral Spread of Social Media Messages about Autism Spectrum Disorders.” American Journal of Public Health 106(7): 1173-1180

Bail, Christopher A., Taylor W. Brown, and Marcus Mann. 2017. “Channeling Hearts and Minds: Cognitive- Emotional Currents and Public Deliberation on Social Media.” American Sociological Review, 82(6) 1188-1213

1. Significant coding skills required (html, css, cloud-computing, reactive programming)

2. Competitive environment for attention (apps are no longer “new”)

3. Concerns about data sharing/privacy

4. Compelling incentives are hard to identify- and particularly challenging for studies of sensitive topics. But financial incentives may be an important option going forward.

Shiny is a (relatively) new tool that enables people to build, compile, and host interactive apps natively within RStudio

Global.R
server.R
ui.R

-The “memory” of the app. Stores things you want to call from other parts of the app (usually data but could also be functions and other types of objects)

-The “brains” of the app- runs the analysis you want to show the user, but can also store data generated by the user, or expose different users to different types of information (good for experimentation)

-The “face” of the app. Determines what user will see (e.g. what types of visualizations, check boxes or word boxes, fonts, etc.) Can load fancy images, logos, etc. to improve the overall appeal of the app.

There are now many Shiny apps out there. Check out the templates (with code) here.

-Check out the googledrive package for loading and storing data.

-High-volume app hosting is available via RStudio.

Munger, Kevin. 2017. “Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment.” Political Behavior. 398:629-649.

Bail, Christopher, Lisa Argyle, Taylor Brown, John Bumpuss, Haohan Chen, M.B. Fallin Hun- zaker, Jaemin Lee, Marcus Mann, Friedolin Merhout, and Alexander Volfovsky. Conditionally Accepted. “Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media.” Proceedings of the National Academy of Sciences.

for (i in 1:24){
  #Search for 50 recent tweets about computational social science
  css_tweets<-search_tweets("Computational Social Science", n=50, include_rts = FALSE)
  #Randomly pick one of them, which appears in the `text` variable with the `css_tweets` dataframe
  lucky_tweet<-sample(css_tweets$text, 1)
  post_tweet(lucky_tweet)
  Sys.sleep(3600)
  #3600 seconds is 60 minutes
}