Measure fake-news interventions under network interference

Q: Measure fake-news interventions under network interference

This question evaluates a data scientist's competency in causal inference and experiment design under network interference, covering cluster-randomized trials, exposure mapping, estimation of total, direct and spillover effects, power analysis, robustness checks, and detection of adversarial adaptation.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Question

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.

Measure fake-news interventions under network interference

Quick Overview

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

Context

Task

Solution

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