Experiment Design Under Interference: Warning Label for Suspected Fake-News Reshares

Context

You are testing a pre-reshare warning label for links suspected to be misinformation. Because both exposure and behavior propagate along the social graph (e.g., seeing friends' reshares, feed ranking, social reinforcement), the Stable Unit Treatment Value Assumption (SUTVA) is violated: one user's treatment can affect others.

Design a cluster-randomized experiment that can identify total, direct, and spillover (indirect) effects despite interference.

Task

Describe a design that includes:

(a) Cluster construction

• How will you partition the graph into clusters to minimize cross-cluster interference? Outline practical constraints (e.g., cluster size bounds) and algorithms.

(b) Exposure mapping

• Define what counts as a treated user vs an indirectly-exposed user under partial interference. State any thresholds or functions of neighbors' treatment you will use.

(c) Outcomes

• Specify primary outcomes, such as reshare rate and unique reach. Include a system-level metric for misinformation prevalence reduction.

(d) Estimation strategy and power

• Describe estimators for total, direct, and spillover effects (e.g., Horvitz–Thompson or difference-in-means with cluster weights). Discuss how cluster size and intra-cluster correlation influence power.

(e) Sensitivity analyses

• Propose checks for contamination (cross-cluster edges) and robustness to exposure-mapping choices.

Finally, explain how you will detect strategic adversarial adaptation (e.g., attempts to circumvent the label) during the test.