--- title: "General Social Sciences — papers" description: "General Social Sciences: 66 papers of the Elamur Research library · page 1 of 2 · 1–25, the most cited first — our summary of each, its ideas, references…" canonical: "https://research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) / General Social Sciences # 🤝 General Social Sciences 66 papers · page 1 of 2 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) General Social Sciences is a subfield of Social Sciences in the OpenAlex classification of science. The topics most of its papers here are on: «Computational and Text Analysis Methods». ## Papers **1** [2](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/page/2/) 1. [**Latent Dirichlet Allocation**David M. Blei, Andrew Y. Ng, Michael I. Jordan*2003 · 28,297 citations*](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/) 2. [**Accurate Methods for the Statistics of Surprise and Coincidence**Ted Dunning*1993 · 3,060 citations*](https://md.research.elamur.ai/free/en/papers/dunning-1993-accurate-methods-statistics-surprise-coincidence/) 3. [**RCV1: A New Benchmark Collection for Text Categorization Research**David D. Lewis, Yiming Yang, Tony G. Rose, et al.*2004 · 2,742 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2004-rcv1-benchmark-collection-text-categorization/) 4. [**Dynamic Topic Models**David M. Blei, John D. Lafferty*2006 · 2,508 citations*](https://md.research.elamur.ai/free/en/papers/blei-2006-dynamic-topic-models/) 5. [**Assessing Agreement on Classification Tasks: The Kappa Statistic**Jean Carletta*1996 · 2,462 citations*](https://md.research.elamur.ai/free/en/papers/carletta-1996-assessing-agreement-classification-tasks-kappa/) 6. [**Exploring the Space of Topic Coherence Measures**Michael Röder, Andreas Both, Alexander Hinneburg*2015 · 2,368 citations*](https://md.research.elamur.ai/free/en/papers/roder-2015-exploring-space-topic-coherence-measures/) 7. [**Probabilistic Latent Semantic Analysis**Thomas Hofmann*1999 · 2,145 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-1999-probabilistic-latent-semantic-analysis/) 8. [**Reading Tea Leaves: How Humans Interpret Topic Models**Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, et al.*2009 · 2,037 citations*](https://md.research.elamur.ai/free/en/papers/chang-2009-reading-tea-leaves-humans-interpret-topic/) 9. [**Survey Article: Inter-Coder Agreement for Computational Linguistics**Ron Artstein, Massimo Poesio*2008 · 1,770 citations*](https://md.research.elamur.ai/free/en/papers/artstein-2008-survey-article-inter-coder-agreement/) 10. [**BERTopic: Neural topic modeling with a class-based TF-IDF procedure**Maarten Grootendorst*2022 · 1,729 citations*](https://md.research.elamur.ai/free/en/papers/grootendorst-2022-bertopic-neural-topic-modeling-class/) 11. [**ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks**Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli*2023 · 1,532 citations*](https://md.research.elamur.ai/free/en/papers/gilardi-2023-chatgpt-outperforms-crowd-workers-text/) 12. [**NLTK: The Natural Language Toolkit**Steven Bird*2006 · 1,372 citations*](https://md.research.elamur.ai/free/en/papers/bird-2006-nltk-natural-language-toolkit/) 13. [**Optimizing Semantic Coherence in Topic Models**David Mimno, Hanna Wallach, Edmund Talley, et al.*2011 · 1,334 citations*](https://md.research.elamur.ai/free/en/papers/mimno-2011-optimizing-semantic-coherence-topic-models/) 14. [**Out of One, Many: Using Language Models to Simulate Human Samples**Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, et al.*2022 · 942 citations*](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/) 15. [**Topic Modeling in Embedding Spaces**Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei*2020 · 914 citations*](https://md.research.elamur.ai/free/en/papers/dieng-2020-topic-modeling-embedding-spaces/) 16. [**Automatic Evaluation of Topic Coherence**David Newman, Jey Han Lau, Karl Grieser, et al.*2010 · 823 citations*](https://md.research.elamur.ai/free/en/papers/newman-2010-automatic-evaluation-topic-coherence/) 17. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 776 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) 18. [**A Joint Model of Text and Aspect Ratings for Sentiment Summarization**Ivan Titov, Ryan McDonald*2008 · 729 citations*](https://md.research.elamur.ai/free/en/papers/titov-2008-joint-model-text-aspect-ratings/) 19. [**Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality**Jey Han Lau, David Newman, Timothy Baldwin*2014 · 699 citations*](https://md.research.elamur.ai/free/en/papers/lau-2014-machine-reading-tea-leaves-automatically/) 20. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 650 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/) 21. [**Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora**William L. Hamilton, Kevin Clark, Jure Leskovec, et al.*2016 · 431 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/) 22. [**Recognizing Stances in Ideological On-Line Debates**Swapna Somasundaran, Janyce Wiebe*2010 · 416 citations*](https://md.research.elamur.ai/free/en/papers/somasundaran-2010-recognizing-stances-ideological-line-debates/) 23. [**Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations**Aida Mostafazadeh Davani, Mark Díaz, Vinodkumar Prabhakaran*2022 · 415 citations*](https://md.research.elamur.ai/free/en/papers/davani-2022-dealing-disagreements-looking-beyond-majority/) 24. [**The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 404 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/) 25. [**Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation**Lora M. Aroyo, Chris Welty*2015 · 385 citations*](https://md.research.elamur.ai/free/en/papers/aroyo-2015-truth-lie-crowd-truth-seven/) **1** [2](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/page/2/) Updated 23 October 2026 - [← previous subfield Gender Studies](https://md.research.elamur.ai/free/en/shelves/social-sciences/gender-studies/) - [next subfield → Geography, Planning and Development](https://md.research.elamur.ai/free/en/shelves/social-sciences/geography-planning-and-development/) - [↑ back to Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/)