# Towards Detecting Harmful Agendas in News Articles

arXiv: https://arxiv.org/abs/2302.00102 · HTML: https://mlsanity.com/paper/2302.00102/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 54 references that are in the map.
- First posted on arXiv 31 Jan 2023.

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

- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Learning to Faithfully Rationalize by Construction](https://mlsanity.com/paper/2005.00115/) (2020)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation](https://mlsanity.com/paper/2011.04088/) (2020)
- [NELA-GT-2020: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2102.04567/) (2021)
- [Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News](https://mlsanity.com/paper/2004.01732/) (2020)
- [A Framework of Severity for Harmful Content Online](https://mlsanity.com/paper/2108.04401/) (2021)
- [Detecting Media Bias in News Articles using Gaussian Bias Distributions](https://mlsanity.com/paper/2010.10649/) (2020)
- [Combating Disinformation in a Social Media Age](https://mlsanity.com/paper/2007.07388/) (2020)
- [Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features](https://mlsanity.com/paper/1709.01189/) (2017)
- [Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues](https://mlsanity.com/paper/1910.01160/) (2019)
- [Characterizing networks of propaganda on Twitter: a case study](https://mlsanity.com/paper/2005.10004/) (2020)

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

- [A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization](https://mlsanity.com/paper/2403.01152/) (2024)
- [Neural Authorship Attribution: Stylometric Analysis on Large Language Models](https://mlsanity.com/paper/2308.07305/) (2023)
- [Social Convos: Capturing Agendas and Emotions on Social Media](https://mlsanity.com/paper/2402.15571/) (2024)

## Abstract (arXiv)

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, NewsAgendas, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.