# Few-shot Learning for Multi-modal Social Media Event Filtering

arXiv: https://arxiv.org/abs/2211.10340 · HTML: https://mlsanity.com/paper/2211.10340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response](https://mlsanity.com/paper/2004.11838/) (2020)
- [Protest Activity Detection and Perceived Violence Estimation from Social Media Images](https://mlsanity.com/paper/1709.06204/) (2017)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Active Learning for Event Detection in Support of Disaster Analysis Applications](https://mlsanity.com/paper/1909.12601/) (2019)

## Built on by (most cited first, 2 of 2)

- [C-CLIP: Contrastive Image-Text Encoders to Close the Descriptive-Commentative Gap](https://mlsanity.com/paper/2309.03921/) (2023)
- [Interactive Event Sifting using Bayesian Graph Neural Networks](https://mlsanity.com/paper/2410.05359/) (2024)

## Abstract (arXiv)

Social media has become an important data source for event analysis. When collecting this type of data, most contain no useful information to a target event. Thus, it is essential to filter out those noisy data at the earliest opportunity for a human expert to perform further inspection. Most existing solutions for event filtering rely on fully supervised methods for training. However, in many real-world scenarios, having access to large number of labeled samples is not possible. To deal with a few labeled sample training problem for event filtering, we propose a graph-based few-shot learning pipeline. We also release the Brazilian Protest Dataset to test our method. To the best of our knowledge, this dataset is the first of its kind in event filtering that focuses on protests in multi-modal social media data, with most of the text in Portuguese. Our experimental results show that our proposed pipeline has comparable performance with only a few labeled samples (60) compared with a fully labeled dataset (3100). To facilitate the research community, we make our dataset and code available at https://github.com/jdnascim/7Set-AL.