--- title: "General Social Sciences — papers, page 2" description: "General Social Sciences: 66 papers of the Elamur Research library · page 2 of 2 · 26–66, the most cited first — our summary of each, its ideas…" canonical: "https://research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/page/2/" --- [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/page/2/) [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](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) / page 2 # 🤝 General Social Sciences 66 papers · page 2 of 2 · 26–66 · 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 [← prev](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) [1](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) **2** 26. [**Political Ideology Detection Using Recursive Neural Networks**Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, et al.*2014 · 368 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2014-political-ideology-detection-recursive-neural/) 27. [**Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence**Federico Bianchi, Silvia Terragni, Dirk Hovy*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2021-pre-training-is-hot-topic/) 28. [**The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation**Barbara Plank*2022 · 364 citations*](https://md.research.elamur.ai/free/en/papers/plank-2022-problem-human-label-variation-ground/) 29. [**The Media Frames Corpus: Annotations of Frames Across Issues**Dallas Card, Amber E. Boydstun, Justin H. Gross, et al.*2015 · 359 citations*](https://md.research.elamur.ai/free/en/papers/card-2015-media-frames-corpus-annotations-frames/) 30. [**Get out the vote: Determining support or opposition from Congressional floor-debate transcripts**Matt Thomas, Bo Pang, Lillian Lee*2006 · 307 citations*](https://md.research.elamur.ai/free/en/papers/thomas-2006-get-out-vote-determining-support/) 31. [**S2ORC: The Semantic Scholar Open Research Corpus**Kyle Lo, Lucy Lu Wang, Mark Neumann, et al.*2019 · 297 citations*](https://md.research.elamur.ai/free/en/papers/lo-2019-s2orc-semantic-scholar-open-research/) 32. [**A Bayesian Mixed Effects Model of Literary Character**David Bamman, Ted Underwood, Noah A. Smith*2014 · 285 citations*](https://md.research.elamur.ai/free/en/papers/bamman-2014-bayesian-mixed-effects-model-literary/) 33. [**Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations**Emily Allaway, Kathleen McKeown*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/allaway-2020-zero-shot-stance-detection-dataset/) 34. [**Revisiting Readability: A Unified Framework for Predicting Text Quality**Emily Pitler, Ani Nenkova*2008 · 253 citations*](https://md.research.elamur.ai/free/en/papers/pitler-2008-revisiting-readability-unified-framework-predicting/) 35. [**Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations**Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, et al.*2023 · 242 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-synthetic-data-generation-large-language/) 36. [**Stance Classification using Dialogic Properties of Persuasion**Marilyn Walker, Pranav Anand, Rob Abbott, et al.*2012 · 240 citations*](https://md.research.elamur.ai/free/en/papers/walker-2012-stance-classification-dialogic-properties-persuasion/) 37. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning**Petter Tornberg*2023 · 230 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/) 38. [**Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers**Chenglei Si, Diyi Yang, Tatsunori Hashimoto*2024 · 182 citations*](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/) 39. [**On Releasing Annotator-Level Labels and Information in Datasets**Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, Mark Diaz*2021 · 168 citations*](https://md.research.elamur.ai/free/en/papers/prabhakaran-2021-releasing-annotator-level-labels-information/) 40. [**Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter**Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, et al.*2020 · 151 citations*](https://md.research.elamur.ai/free/en/papers/conforti-2020-will-they-won-t-they/) 41. [**ConvoKit: A Toolkit for the Analysis of Conversations**Jonathan P. Chang, Caleb Chiam, Liye Fu, et al.*2020 · 137 citations*](https://md.research.elamur.ai/free/en/papers/chang-2020-convokit-toolkit-analysis-conversations/) 42. [**POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection**Yujian Liu, Xinliang Frederick Zhang, David Wegsman, et al.*2022 · 70 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-politics-pretraining-same-story-article/) 43. [**The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse**Pere-Lluís Huguet Cabot, Verna Dankers, David Abadi, et al.*2020 · 64 citations*](https://md.research.elamur.ai/free/en/papers/cabot-2020-pragmatics-behind-politics-modelling-metaphor/) 44. [**Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure**Kelsey Allen, Giuseppe Carenini, Raymond Ng*2014 · 60 citations*](https://md.research.elamur.ai/free/en/papers/allen-2014-detecting-disagreement-conversations-pseudo-monologic/) 45. [**Can Large Language Models Address Open-Target Stance Detection?**Abu Ubaida Akash, Ahmed Fahmy, Amine Trabelsi*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/akash-2025-can-large-language-models-address/) 46. [**Large Language Models Are Good Term Extractors: A Systematic Evaluation**Rigouts Terryn, Ayla*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/terryn-2026-large-language-models-are-good/) 47. [**Bridging the Socioeconomic Gap in Education: A Hybrid AI and Human Annotation Approach**Nahed Abdelgaber, Labiba Jahan, Arham Vinit Doshi, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/abdelgaber-2025-bridging-socioeconomic-gap-education-hybrid/) 48. [**Creation of a Numerical Scoring System to Objectively Measure and Compare the Level of Rhetoric in Arabic Texts: A Feasibility Study, and A Working Prototype**Mandar Marathe*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/marathe-2025-creation-numerical-scoring-system-objectively/) 49. [**HeBID: Detecting Social Identities in Hebrew-language Political Text**Guy Mor-Lan, Naama Rivlin-Angert, Yael R. Kaplan, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/lan-2025-hebid-detecting-social-identities-hebrew/) 50. [**Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis**Latifah Almurqren, Ryan Hodgson, Alexandra Cristea*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/almurqren-2024-arabic-text-sentiment-analysis-reinforcing/) 51. [**Dotless Representation of Arabic Text: Analysis and Modeling**Maged S. Al-Shaibani, Irfan Ahmad*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/al-shaibani-2023-dotless-representation-arabic-text-analysis/) 52. [**A Corpus of Persuasion Techniques in Slavic Languages**Jakub Piskorski, Dimitar Iliyanov Dimitrov, Marina Ernst, et al.*2026*](https://md.research.elamur.ai/free/en/papers/piskorski-2026-corpus-persuasion-techniques-slavic-languages/) 53. [**Automatic Analysis of Collaboration through Human Conversational Data Resources: A Review**Yi Yu, Maria Boritchev, Chloé Clavel*2026*](https://md.research.elamur.ai/free/en/papers/yu-2026-automatic-analysis-collaboration-through-human/) 54. [**Construction of Japanese Prefectural Assembly Minutes Datasets across Three Electoral Terms: Comparative Analysis of 2011, 2015, and 2019 Four-Year Periods**Keiichi Takamaru, Hokuto Ototake, Yuzu Uchida, et al.*2026*](https://md.research.elamur.ai/free/en/papers/takamaru-2026-construction-japanese-prefectural-assembly-minutes/) 55. [**Detecting Experiential Intertextuality Across Migration Routes: Beyond Surface Similarity in French Narratives**Sakayo Toadoum Sari, Nelly Robin, Michelle Auzanneau, et al.*2026*](https://md.research.elamur.ai/free/en/papers/sari-2026-detecting-experiential-intertextuality-across-migration/) 56. [**Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models**Salma Mekaoui, Hiba Sofyan, Imane Benchrif, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mekaoui-2026-efficient-topic-extraction-graph-based/) 57. [**Explore Political Discourse with Transformers. Emergent Paradigmatic and Syntagmatic Representations.**Laurent Vanni, Damon Mayaffre*2026*](https://md.research.elamur.ai/free/en/papers/vanni-2026-explore-political-discourse-transformers-emergent/) 58. [**From Noise to Signal: When Outliers Seed New Topics**Evangelia Zve, Gauvain Bourgne, Benjamin Icard, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zve-2026-noise-signal-when-outliers-seed/) 59. [**Integrating AI into Arabic Linguistics: A Methodological Framework for Reproducible and Interpretable Research**Ismail Akzam, Mohammad Ikhwan Bin Abdullah*2026*](https://md.research.elamur.ai/free/en/papers/akzam-2026-integrating-ai-arabic-linguistics-methodological/) 60. [**LLMs as Annotators: Evaluating Model–Human Alignment in Detecting Contentious Language in Historical Corpora**Yahui Zhao, Clemencia Siro, Laura Hollink*2026*](https://md.research.elamur.ai/free/en/papers/zhao-2026-llms-as-annotators-evaluating-model/) 61. [**Predicting Topic (Co-)Occurrence Using Topic Networks Built from the Project Gutenberg Corpus**Bhuvanesh Verma, Alexander Mehler*2026*](https://md.research.elamur.ai/free/en/papers/verma-2026-predicting-topic-co-occurrence-topic/) 62. [**Sentiment Classification of Gaza War Headlines: A Comparative Analysis of Large Language Models and Arabic Fine-Tuned BERT Models**Amr Eleraqi; Hager H. Mustafa;, Abdul Hadi N. Ahmed*2026*](https://md.research.elamur.ai/free/en/papers/eleraqi-2026-sentiment-classification-gaza-war-headlines/) 63. [**The Potential for Misleading Results in Text Sanitisation with Standard Evaluation Metrics**Dan Zhang, Mark Anderson*2026*](https://md.research.elamur.ai/free/en/papers/zhang-2026-potential-misleading-results-text-sanitisation/) 64. [**When Numbers Tell Half the Story: Human-Metric Alignment in Topic Model Evaluation**Thibault Prouteau, Francis Lareau, Nicolas Dugue, et al.*2026*](https://md.research.elamur.ai/free/en/papers/prouteau-2026-when-numbers-tell-half-story/) 65. [**An Information-Theoretic Approach to Identifying Formulaic Clusters in Textual Data**Gideon Yoffe, Yair Segev, Barak Sober*2025*](https://md.research.elamur.ai/free/en/papers/yoffe-2025-information-theoretic-approach-identifying-formulaic/) 66. [**Uncovering Conspiratorial Narratives within Arabic Online Content**Djamila Mohdeb, Meriem Laifa, Zineb Guemraoui, et al.*2025*](https://md.research.elamur.ai/free/en/papers/mohdeb-2025-uncovering-conspiratorial-narratives-within-arabic/) [← prev](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) [1](https://md.research.elamur.ai/free/en/shelves/social-sciences/general-social-sciences/) **2**