# Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review

arXiv: https://arxiv.org/abs/2312.02901 · HTML: https://mlsanity.com/paper/2312.02901/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Dynamic Language Models for Continuously Evolving Content](https://mlsanity.com/paper/2106.06297/) (2021)
- [NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/1904.01546/) (2019)
- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale](https://mlsanity.com/paper/1605.05797/) (2016)
- [Tackling Concept Shift in Text Classification using Entailment-style Modeling](https://mlsanity.com/paper/2311.03320/) (2023)
- [ASSED -- A Framework for Identifying Physical Events through Adaptive Social Sensor Data Filtering](https://mlsanity.com/paper/1909.07596/) (2019)

## Built on by (most cited first, 5 of 5)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget](https://mlsanity.com/paper/2505.24149/) (2025)
- [Importance-aware Topic Modeling for Discovering Public Transit Risk from Noisy Social Media](https://mlsanity.com/paper/2512.06293/) (2025)
- [Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams](https://mlsanity.com/paper/2403.15455/) (2024)
- [Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams](https://mlsanity.com/paper/2603.19250/) (2026)

## Abstract (arXiv)

The society produces textual data online in several ways, e.g., via reviews and social media posts. Therefore, numerous researchers have been working on discovering patterns in textual data that can indicate peoples' opinions, interests, etc. Most tasks regarding natural language processing are addressed using traditional machine learning methods and static datasets. This setting can lead to several problems, e.g., outdated datasets and models, which degrade in performance over time. This is particularly true regarding concept drift, in which the data distribution changes over time. Furthermore, text streaming scenarios also exhibit further challenges, such as the high speed at which data arrives over time. Models for stream scenarios must adhere to the aforementioned constraints while learning from the stream, thus storing texts for limited periods and consuming low memory. This study presents a systematic literature review regarding concept drift adaptation in text stream scenarios. Considering well-defined criteria, we selected 48 papers published between 2018 and August 2024 to unravel aspects such as text drift categories, detection types, model update mechanisms, stream mining tasks addressed, and text representation methods and their update mechanisms. Furthermore, we discussed drift visualization and simulation and listed real-world datasets used in the selected papers. Finally, we brought forward a discussion on existing works in the area, also highlighting open challenges and future research directions for the community.