In weeks past we’ve looked at how we might combine traditional survey techniques with new computational methods and digital trace data. This week, we’ll be investigating how the standard experimental design might be extended to the digital domain.
The articles assigned for essential reading deploy bots and other automated techniques in order to manufacture a laboratory-like environment. In the first case, this is by using bots as experimental “confederates;” in the second we are experimenting in the manipulation of the YouTube algorithm.
Essential reading:
Munger (2016)
Haroon et al. (2022)
Additional reading:
Siegel and Badaan (2020)
Bond et al. (2012)
Slides
Slides for this week are available here
Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. 2012.
“A 61-Million-Person Experiment in Social Influence and Political Mobilization.” Nature 489 (7415): 295–98.
https://doi.org/10.1038/nature11421.
Haroon, Muhammad, Anshuman Chhabra, Xin Liu, Prasant Mohapatra, Zubair Shafiq, and Magdalena Wojcieszak. 2022.
“YouTube, the Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations.” https://doi.org/10.48550/ARXIV.2203.10666.
Munger, Kevin. 2016.
“Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment.” Political Behavior 39 (3): 629–49.
https://doi.org/10.1007/s11109-016-9373-5.
Siegel, Alexandra A, and Vivienne Badaan. 2020. “#No2Sectarianism: Experimental Approaches to Reducing Sectarian Hate Speech Online.” Am. Polit. Sci. Rev. 114 (3): 837855.