mlsanityData to 7 Oct 2026

Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections

arXiv 2305.05094 ↗First posted 8 May 2023cs.CL cs.HCcited by 15

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Abstract (arXiv)

Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniques such as topic models, or by following a manual theme discovery process. In this paper, we expand the definition of a theme to account for more than just a word distribution, and include generalized concepts deemed relevant by domain experts. Then, we propose an interactive framework that receives and encodes expert feedback at different levels of abstraction. Our framework strikes a balance between automation and manual coding, allowing experts to maintain control of their study while reducing the manual effort required.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 9 of its 51 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

9 of 51 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  4. 2019Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootingscited by 67
  5. 2021Putting Humans in the Natural Language Processing Loop: A Surveycited by 40
  6. 2021Modeling Framing in Immigration Discourse on Social Mediacited by 37
  7. 2021Identifying Morality Frames in Political Tweets using Relational Learningcited by 26
  8. 2022A Holistic Framework for Analyzing the COVID-19 Vaccine Debatecited by 18
  9. 2020Modeling Content and Context with Deep Relational Learningcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 6; 2025: 5; 2026: 1.

15 papers.

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  1. 2023Natural Language Decompositions of Implicit Content Enable Better Text Representationscited by 18
  2. 2025HICode: Hierarchical Inductive Coding with LLMscited by 12
  3. 2024Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategycited by 9
  4. 2023An Interactive Framework for Profiling News Media Sourcescited by 4
  5. 2023Interactively Learning Social Media Representations Improves News Source Factuality Detectioncited by 3
  6. 2024Framing in the Presence of Supporting Data: A Case Study in U.S. Economic Newscited by 2
  7. 2024Facilitating Opinion Diversity through Hybrid NLP Approachescited by 2
  8. 2024LLM-Human Pipeline for Cultural Context Grounding of Conversationscited by 2
  9. 2024Interactive Topic Models with Optimal Transportcited by 1
  10. 2025Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Mediacited by 1
  11. 2024Effects of Collaboration on the Performance of Interactive Theme Discovery Systemscited by 0
  12. 2025CoLa: Learning to Interactively Collaborate with Large Language Modelscited by 0
3 more
  1. 2025Splits! Flexible Sociocultural Linguistic Investigation at Scalecited by 0
  2. 2025ConceptCarve: Dynamic Realization of Evidencecited by 0
  3. 2026Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotationcited by 0