# Multilingual Detection of Personal Employment Status on Twitter

arXiv: https://arxiv.org/abs/2203.09178 · HTML: https://mlsanity.com/paper/2203.09178/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 95 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [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)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Conversational flow in Oxford-style debates](https://mlsanity.com/paper/1604.03114/) (2016)
- [Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning](https://mlsanity.com/paper/2012.02462/) (2020)
- [On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks](https://mlsanity.com/paper/2010.05103/) (2020)
- [Multi-class Text Classification using BERT-based Active Learning](https://mlsanity.com/paper/2104.14289/) (2021)
- [LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition](https://mlsanity.com/paper/2001.02524/) (2020)
- [Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas](https://mlsanity.com/paper/1910.03206/) (2019)

## Built on by (most cited first, 8 of 8)

- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages](https://mlsanity.com/paper/2211.03263/) (2022)
- [NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data](https://mlsanity.com/paper/2403.19260/) (2024)
- [Automated Privacy Information Annotation in Large Language Model Interactions](https://mlsanity.com/paper/2505.20910/) (2025)
- [Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI](https://mlsanity.com/paper/2412.15047/) (2024)
- [Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey](https://mlsanity.com/paper/2503.09701/) (2025)
- [Challenges and Opportunities of NLP for HR Applications: A Discussion Paper](https://mlsanity.com/paper/2405.07766/) (2024)
- [Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models](https://mlsanity.com/paper/2406.09206/) (2024)

## Abstract (arXiv)

Detecting disclosures of individuals' employment status on social media can provide valuable information to match job seekers with suitable vacancies, offer social protection, or measure labor market flows. However, identifying such personal disclosures is a challenging task due to their rarity in a sea of social media content and the variety of linguistic forms used to describe them. Here, we examine three Active Learning (AL) strategies in real-world settings of extreme class imbalance, and identify five types of disclosures about individuals' employment status (e.g. job loss) in three languages using BERT-based classification models. Our findings show that, even under extreme imbalance settings, a small number of AL iterations is sufficient to obtain large and significant gains in precision, recall, and diversity of results compared to a supervised baseline with the same number of labels. We also find that no AL strategy consistently outperforms the rest. Qualitative analysis suggests that AL helps focus the attention mechanism of BERT on core terms and adjust the boundaries of semantic expansion, highlighting the importance of interpretable models to provide greater control and visibility into this dynamic learning process.