# Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media

arXiv: https://arxiv.org/abs/1904.00542 · HTML: https://mlsanity.com/paper/1904.00542/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 26 of 26)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
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- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
- [Reliability Estimation of News Media Sources: Birds of a Feather Flock Together](https://mlsanity.com/paper/2404.09565/) (2024)
- [GREENER: Graph Neural Networks for News Media Profiling](https://mlsanity.com/paper/2211.05533/) (2022)
- [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://mlsanity.com/paper/2301.11850/) (2023)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models](https://mlsanity.com/paper/2410.18122/) (2024)
- [Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts](https://mlsanity.com/paper/2506.12552/) (2025)
- [Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection](https://mlsanity.com/paper/2405.18974/) (2024)
- [Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions](https://mlsanity.com/paper/2410.17655/) (2024)
- [PolBiX: Detecting LLMs' Political Bias in Fact-Checking through X-phemisms](https://mlsanity.com/paper/2509.15335/) (2025)
- [Learning Unbiased News Article Representations: A Knowledge-Infused Approach](https://mlsanity.com/paper/2309.05981/) (2023)
- [Target-Aware Contextual Political Bias Detection in News](https://mlsanity.com/paper/2310.01138/) (2023)
- ["Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection](https://mlsanity.com/paper/2503.20797/) (2025)
- [Tell Me Who Your Friends Are: Using Content Sharing Behavior for News Source Veracity Detection](https://mlsanity.com/paper/2101.10973/) (2021)
- [Political Ideology and Polarization of Policy Positions: A Multi-dimensional Approach](https://mlsanity.com/paper/2106.14387/) (2021)
- [Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims](https://mlsanity.com/paper/2108.03731/) (2021)
- [Modeling Political Orientation of Social Media Posts: An Extended Analysis](https://mlsanity.com/paper/2311.12323/) (2023)
- [Enhancing Language Models for Robust Greenwashing Detection](https://mlsanity.com/paper/2601.21722/) (2026)
- [A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis](https://mlsanity.com/paper/2605.01336/) (2026)
- [MEDIAREF: A Public Knowledge Store for Media Background Checks](https://mlsanity.com/paper/2607.02383/) (2026)
- [The MADRS Pipeline: Supporting Depression Assessment in Clinical Trials](https://mlsanity.com/paper/2607.28190/) (2026)
- [Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media](https://mlsanity.com/paper/2608.17987/) (2026)

## Abstract (arXiv)

In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyperpartisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.