Question 1

Which countries lead Computational and Text Analysis Methods research?

Accepted Answer

By volume, the United States and China publish the most (1.1k and 944 works in 2022-now).

Question 2

Which institutions lead Computational and Text Analysis Methods research?

Accepted Answer

By volume in 2022-now, Queensland University of Technology publishes the most Computational and Text Analysis Methods research, followed by Leibniz Association and University of Chicago.

Question 3

Who are the leading researchers in Computational and Text Analysis Methods?

Accepted Answer

The most-cited researchers publishing on Computational and Text Analysis Methods include Bach Xuan Tran and James Thomas.

Question 4

Where is Computational and Text Analysis Methods research done?

Accepted Answer

The largest centres of Computational and Text Analysis Methods research in 2022-now are Beijing (China), Seoul (South Korea), Shanghai (China) and London (United Kingdom). Among places with at least 20 works in it, it is an unusually large share of all research in Mannheim.

Question 5

Where is the best place to study Computational and Text Analysis Methods?

Accepted Answer

Among universities, judged by research, Stanford University, New York University and University of Amsterdam score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

Question 6

Is Computational and Text Analysis Methods research growing?

Accepted Answer

Output in 2022-2025 was 156% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Computational and Text Analysis Methods.

Question 7

What are the most cited papers in Computational and Text Analysis Methods?

Accepted Answer

The most cited paper in Computational and Text Analysis Methods is "Techniques to Identify Themes" (Gery W. Ryan, H. Russell Bernard, 2003), with 5,495 citations. Next come "Answering the Call for a Standard Reliability Measure for Coding Data" (4,263) and "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts" (3,332).