# Multi-View Active Learning for Short Text Classification in User-Generated Data

arXiv: https://arxiv.org/abs/2112.02611 · HTML: https://mlsanity.com/paper/2112.02611/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 70 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Active Learning by Acquiring Contrastive Examples](https://mlsanity.com/paper/2109.03764/) (2021)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study](https://mlsanity.com/paper/1808.05697/) (2018)
- [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Processing Social Media Messages in Mass Emergency: A Survey](https://mlsanity.com/paper/1407.7071/) (2014)
- [Cartography Active Learning](https://mlsanity.com/paper/2109.04282/) (2021)
- [Transformer Based Multi-Source Domain Adaptation](https://mlsanity.com/paper/2009.07806/) (2020)
- [Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media](https://mlsanity.com/paper/1802.09130/) (2018)
- [Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space](https://mlsanity.com/paper/2004.08046/) (2020)
- [Predicting Anti-Asian Hateful Users on Twitter during COVID-19](https://mlsanity.com/paper/2109.07296/) (2021)
- [Semi-Supervised Text Classification via Self-Pretraining](https://mlsanity.com/paper/2109.15300/) (2021)
- [Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media](https://mlsanity.com/paper/2004.10201/) (2020)
- [Deep Bayesian Active Learning for Multiple Correct Outputs](https://mlsanity.com/paper/1912.01119/) (2019)
- [Mining Coronavirus (COVID-19) Posts in Social Media](https://mlsanity.com/paper/2004.06778/) (2020)
- [Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets](https://mlsanity.com/paper/2003.12139/) (2020)
- [View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data](https://mlsanity.com/paper/2105.11354/) (2021)

## Built on by (most cited first, 3 of 3)

- [Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets](https://mlsanity.com/paper/2401.00575/) (2023)
- [Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches](https://mlsanity.com/paper/2605.23910/) (2026)
- [Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning](https://mlsanity.com/paper/2504.21685/) (2025)

## Abstract (arXiv)

Mining user-generated data often suffers from the lack of enough labeled data, short document lengths, and the informal user language. In this paper, we propose a novel active learning model to overcome these obstacles in the tasks tailored for query phrases--e.g., detecting positive reports of natural disasters. Our model has three novelties: 1) It is the first approach to employ multi-view active learning in this domain. 2) It uses the Parzen-Rosenblatt window method to integrate the representativeness measure into multi-view active learning. 3) It employs a query-by-committee strategy, based on the agreement between predictors, to address the usually noisy language of the documents in this domain. We evaluate our model in four publicly available Twitter datasets with distinctly different applications. We also compare our model with a wide range of baselines including those with multiple classifiers. The experiments testify that our model is highly consistent and outperforms existing models.