This topic integrates the theoretical, ethical, and methodological strands of Computational Social Network Analysis (CSNA) into the process of designing an independent research project. The formulation of a computationally testable research question is presented as the foundation of scientific inquiry, requiring that broad curiosities be refined into measurable hypotheses aligned with available data and appropriate metrics. The alignment between dataset characteristics, analytical methods, and ethical feasibility is emphasized, ensuring that computational ambition does not override data integrity or research ethics.

The proposal structure is described as a concise roadmap linking objectives, data, and algorithms, with justification for every methodological choice to prevent algorithmic overreach. Peer review is reframed as a process of accountability and methodological refinement, where transparency in design becomes a safeguard for integrity. The topic concludes that scientific rigor in CSNA is achieved not solely through computation but through ethical reflection, collective feedback, and the disciplined articulation of one’s analytical reasoning.

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