I trained as an economist, and statistics was my first language: models, estimation, causal inference. But the questions that held me — identity, ideology, how political communities form and fracture — kept slipping through the categories that language could express. So I went the other way, into qualitative inquiry: reading texts closely, taking interpretation seriously, learning that what a document means is not something you can tabulate.
Each tradition exposed the other’s limit, and the limits pointed somewhere. Close reading does not scale; counting does not interpret. Computational text analysis was the bridge — treating text as data without abandoning the interpretive questions. And once texts became data, a further step suggested itself: texts are produced by people in relation to one another, and the relations carry as much politics as the words. That is what pulled me into network analysis — the idea that structure itself is measurable, and that it reveals what individual attitudes cannot.
The threads fused in 2018, at the Summer Institute in Computational Social Science: the point where a set of borrowed methods became a discipline I could stand in, and where I stopped only using instruments and started building them. The most recent turn follows the same logic one step further. Machine learning entered my work as an instrument — annotation and measurement at scale — and then became a subject: interpretability, asking of the model what I had learned to ask of texts and networks. What does it attend to? What does it encode? Does it measure what we think it measures?
Each layer kept the ones beneath it: statistics under everything, interpretation disciplining measurement, texts and networks as the substance, models now both tool and object. (A more personal telling of this story is on Substack, in Turkish: Hesaplamalı Sosyal Bilimlere nasıl başladım?)
Before the PhD I worked in the space between research and policy: as a research assistant at a non-partisan foreign-policy think tank in Ankara, and later building the Turkish Foreign Policy Barometer on Twitter, a German Marshall Fund–funded project tracking elite discourse. Those years taught me that measurement is never just technical — what you count shapes what governments and publics believe is happening.
The program this became — every paper placed in it — is on the Research page.