# Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets

arXiv: https://arxiv.org/abs/1805.06289 · HTML: https://mlsanity.com/paper/1805.06289/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 9 of 9)

- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Robust Training of Graph Neural Networks via Noise Governance](https://mlsanity.com/paper/2211.06614/) (2022)
- [A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and method](https://mlsanity.com/paper/2410.08814/) (2024)
- [DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank](https://mlsanity.com/paper/2310.14577/) (2023)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles](https://mlsanity.com/paper/2307.08721/) (2023)
- [CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification](https://mlsanity.com/paper/2310.14627/) (2023)
- [LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification](https://mlsanity.com/paper/2605.08448/) (2026)
- [Towards reliable multimodal disaster severity assessment through preference optimization and explainable vision-language reasoning](https://mlsanity.com/paper/2609.00879/) (2026)

## Abstract (arXiv)

During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real-world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data.