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Topic · General Social Sciences

Computational and Text Analysis Methods

Computational and Text Analysis Methods is a research topic within General Social Sciences. Science Explorer counts 9.3k research works in it since 1950. 10.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of computational text analysis methods, such as topic modeling, machine learning, and natural language processing, in social science research. It explores the use of these methods for quantitative analysis of textual data, automated content classification, and uncovering patterns and trends in large text corpora.

  • Computational Text Analysis
  • Topic Modeling
  • Machine Learning
  • Social Science Research
  • Text Data Methods
  • Quantitative Analysis
  • Content Analysis
  • Natural Language Processing
  • Big Data
  • Automated Text Classification
Research works
9.3k
fractional, since 1950
In the world top 10%
941
per year above
Top-10% rate
10.1%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+156%
the tick is no change
Largest producer
United States
30% of the 10 largest, 2022-now
Computational and Text Analysis Methods

Which countries lead Computational and Text Analysis Methods research?

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

By volume, 2022-now

  1. 1 United States 1.1k works
  2. 2 China 944 works
  3. 3 Germany 393 works
  4. 4 India 335 works
  5. 5 United Kingdom 283 works
  6. 6 Indonesia 194 works
  7. 7 South Korea 167 works
  8. 8 Canada 137 works
  9. 9 Australia 136 works
  10. 10 Japan 136 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 29.6%China: 24.4%Germany: 10.2%India: 8.6%6 others listed: 27.2%30%largest
United States1,149 · 29.6%China944 · 24.4%Germany393 · 10.2%India335 · 8.6%6 others listed1,053 · 27.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Computational and Text Analysis Methods research?

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.

By volume, 2022-now

  1. 1 Queensland University of TechnologyAustralia 37 works
  2. 2 Leibniz AssociationGermany 35 works
  3. 3 University of ChicagoUnited States 33 works
  4. 4 Centre National de la Recherche ScientifiqueFrance 24 works
  5. 5 Chinese Academy of SciencesChina 22 works
  6. 6 Ludwig-Maximilians-Universität MünchenGermany 20 works
  7. 7 Telkom UniversityIndonesia 18 works
  8. 8 University of AmsterdamNetherlands 18 works
  9. 9 Binus UniversityIndonesia 18 works
  10. 10 Stanford UniversityUnited States 18 works

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

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

  1. 1 Bach Xuan Tran Vietnam 263k citations
  2. 2 James Thomas United Kingdom 242k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

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

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).

Most cited papers
#PaperReleasedCitations
1 Techniques to Identify Themes Gery W. Ryan, H. Russell Bernard · Field Methods Feb 2003 5,495
2 Answering the Call for a Standard Reliability Measure for Coding Data Andrew F. Hayes, Klaus Krippendorff · Communication Methods and Measures Apr 2007 4,263
3 Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts Justin Grimmer, Brandon Stewart · Political Analysis Open access 2013 3,332
4 Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability Matthew Lombard, Jennifer Snyder‐Duch, Cheryl Campanella Bracken · Human Communication Research Oct 2002 2,961
5 Big Data, new epistemologies and paradigm shifts Rob Kitchin · Big Data & Society Open access Apr 2014 2,346
All 100 most cited papers, with filters and sorting

Where is Computational and Text Analysis Methods research done?

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.

Largest cities, 2022-now

  1. 1 BeijingChina 176 works
  2. 2 SeoulSouth Korea 89 works
  3. 3 ShanghaiChina 75 works
  4. 4 LondonUnited Kingdom 69 works
  5. 5 TokyoJapan 55 works
  6. 6 New YorkUnited States 55 works
  7. 7 NanjingChina 55 works
  8. 8 GuangzhouChina 55 works
  9. 9 MoscowRussia 54 works
  10. 10 BrisbaneAustralia 45 works

Where it is the local speciality

  1. MannheimDE · 22.2 works17×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Computational and Text Analysis Methods than the world average.

See Computational and Text Analysis Methods on the map

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

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.

0%10%20%30%mean 19.85%fractional works in this field (log) →share of work in the world top 10% →New York UniversityStanford UniversityUniversity of Amster…University of Vienna
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Stanford UniversityUnited States 67.621.0%2.5×18 +26.7%
2New York UniversityUnited States 64.922.8%2.9×16 +54.5%
3University of AmsterdamNetherlands 64.417.0%6.1×18 +9.3%
4University of ViennaAustria 61.016.3%5.2×13 +130.4%
5University of WashingtonUnited States 60.923.7%2.2×12 +102.8%
6Georgia Institute of TechnologyUnited States 59.526.5%3.1×9 +543.5%
7University of ChicagoUnited States 59.34.6%8.1×33 +356.2%
8Pennsylvania State UniversityUnited States 58.623.4%2.5×14 +106.0%
9University of MichiganUnited States 57.719.6%2.1×16 +138.1%
10Tsinghua UniversityChina 57.523.6%1.4×15 +151.9%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Computational and Text Analysis Methods, country by country

Is Computational and Text Analysis Methods research growing?

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

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Computational and Text Analysis Methods as a percentage of every work published that year, 0.021% in 2024.

still counting0.010%0.020%0.030%Computational and Text Analysis Methods0.030% in 202519801990200020102020

Fractional works of Computational and Text Analysis Methods over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Computational and Text Analysis Methods9.9% in 202520102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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