# Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections

arXiv: https://arxiv.org/abs/2305.05094 · HTML: https://mlsanity.com/paper/2305.05094/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Putting Humans in the Natural Language Processing Loop: A Survey](https://mlsanity.com/paper/2103.04044/) (2021)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Modeling Content and Context with Deep Relational Learning](https://mlsanity.com/paper/2010.10453/) (2020)

## Built on by (most cited first, 15 of 15)

- [Natural Language Decompositions of Implicit Content Enable Better Text Representations](https://mlsanity.com/paper/2305.14583/) (2023)
- [HICode: Hierarchical Inductive Coding with LLMs](https://mlsanity.com/paper/2509.17946/) (2025)
- [Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy](https://mlsanity.com/paper/2404.10259/) (2024)
- [An Interactive Framework for Profiling News Media Sources](https://mlsanity.com/paper/2309.07384/) (2023)
- [Interactively Learning Social Media Representations Improves News Source Factuality Detection](https://mlsanity.com/paper/2309.14966/) (2023)
- [Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News](https://mlsanity.com/paper/2402.14224/) (2024)
- [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024)
- [LLM-Human Pipeline for Cultural Context Grounding of Conversations](https://mlsanity.com/paper/2410.13727/) (2024)
- [Interactive Topic Models with Optimal Transport](https://mlsanity.com/paper/2406.19928/) (2024)
- [Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media](https://mlsanity.com/paper/2502.01991/) (2025)
- [Effects of Collaboration on the Performance of Interactive Theme Discovery Systems](https://mlsanity.com/paper/2408.09030/) (2024)
- [CoLa: Learning to Interactively Collaborate with Large Language Models](https://mlsanity.com/paper/2504.02965/) (2025)
- [Splits! Flexible Sociocultural Linguistic Investigation at Scale](https://mlsanity.com/paper/2504.04640/) (2025)
- [ConceptCarve: Dynamic Realization of Evidence](https://mlsanity.com/paper/2504.07228/) (2025)
- [Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation](https://mlsanity.com/paper/2604.09626/) (2026)

## 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.