# Continuously Reliable Detection of New-Normal Misinformation: Semantic Masking and Contrastive Smoothing in High-Density Latent Regions

arXiv: https://arxiv.org/abs/2301.07981 · HTML: https://mlsanity.com/paper/2301.07981/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://mlsanity.com/paper/2004.06100/) (2020)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Understanding the Difficulty of Training Transformers](https://mlsanity.com/paper/2004.08249/) (2020)
- [To Trust Or Not To Trust A Classifier](https://mlsanity.com/paper/1805.11783/) (2018)
- [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019)
- [Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models](https://mlsanity.com/paper/1909.11299/) (2019)
- [ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis](https://mlsanity.com/paper/2004.01113/) (2020)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [Temporal Effects on Pre-trained Models for Language Processing Tasks](https://mlsanity.com/paper/2111.12790/) (2021)
- [NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2102.04567/) (2021)
- [MASKER: Masked Keyword Regularization for Reliable Text Classification](https://mlsanity.com/paper/2012.09392/) (2020)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [The King is Naked: on the Notion of Robustness for Natural Language Processing](https://mlsanity.com/paper/2112.07605/) (2021)
- [Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection](https://mlsanity.com/paper/2111.07819/) (2021)
- [ODIN: Automated Drift Detection and Recovery in Video Analytics](https://mlsanity.com/paper/2009.05440/) (2020)
- [MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detection](https://mlsanity.com/paper/2205.09817/) (2022)
- [Evaluating Generalizability of Fine-Tuned Models for Fake News Detection](https://mlsanity.com/paper/2205.07154/) (2022)
- [EdnaML: A Declarative API and Framework for Reproducible Deep Learning](https://mlsanity.com/paper/2211.06783/) (2022)
- [Time-Aware Datasets are Adaptive Knowledgebases for the New Normal](https://mlsanity.com/paper/2211.12508/) (2022)

## Abstract (arXiv)

Toxic misinformation campaigns have caused significant societal harm, e.g., affecting elections and COVID-19 information awareness. Unfortunately, despite successes of (gold standard) retrospective studies of misinformation that confirmed their harmful effects after the fact, they arrive too late for timely intervention and reduction of such harm. By design, misinformation evades retrospective classifiers by exploiting two properties we call new-normal: (1) never-seen-before novelty that cause inescapable generalization challenges for previous classifiers, and (2) massive but short campaigns that end before they can be manually annotated for new classifier training. To tackle these challenges, we propose UFIT, which combines two techniques: semantic masking of strong signal keywords to reduce overfitting, and intra-proxy smoothness regularization of high-density regions in the latent space to improve reliability and maintain accuracy. Evaluation of UFIT on public new-normal misinformation data shows over 30% improvement over existing approaches on future (and unseen) campaigns. To the best of our knowledge, UFIT is the first successful effort to achieve such high level of generalization on new-normal misinformation data with minimal concession (1 to 5%) of accuracy compared to oracles trained with full knowledge of all campaigns.