@inproceedings{manzoor-etal-2026-multi,
title = "A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis",
author = "Manzoor, Muhammad Arslan and
Azizov, Dilshod and
Orel, Daniil and
Siddique, Umer and
Mujahid, Zain Muhammad and
Hou, Yufang and
Nakov, Preslav",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1264/",
pages = "25265--25286",
ISBN = "979-8-89176-395-1",
abstract = "News outlets shape public opinion on a scale, which makes automated detection of political bias and factuality essential. Yet, the field still lacks unified resources, comprehensive evaluations in diverse approaches, and systematic analyzes of the representations and fusion strategies that matter the most, especially under label sparsity and dataset diversity. In addition, there is little empirical work that reports broad observation driven findings about what consistently works, what fails, and why. We address these gaps with four contributions: (i) MBFC-2025, a large-scale label set that covers {\textasciitilde}2,600 outlets from Media Bias/Fact Check (MBFC); (ii) multi-view representations for ACL-2020 {\textasciitilde}900 outlets and MBFC-2025, spanning Alexa graphs, hyperlink graphs, LLM-derived graphs, articles, and Wikipedia descriptions; (iii) systematic evaluation and analysis of embedding views and fusion strategies, including an RL-based fusion variant; and (iv) extensive experiments that achieve state-of-the-art results on ACL-2020 and establish strong benchmarks on MBFC-2025."
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%0 Conference Proceedings
%T A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis
%A Manzoor, Muhammad Arslan
%A Azizov, Dilshod
%A Orel, Daniil
%A Siddique, Umer
%A Mujahid, Zain Muhammad
%A Hou, Yufang
%A Nakov, Preslav
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F manzoor-etal-2026-multi
%X News outlets shape public opinion on a scale, which makes automated detection of political bias and factuality essential. Yet, the field still lacks unified resources, comprehensive evaluations in diverse approaches, and systematic analyzes of the representations and fusion strategies that matter the most, especially under label sparsity and dataset diversity. In addition, there is little empirical work that reports broad observation driven findings about what consistently works, what fails, and why. We address these gaps with four contributions: (i) MBFC-2025, a large-scale label set that covers ~2,600 outlets from Media Bias/Fact Check (MBFC); (ii) multi-view representations for ACL-2020 ~900 outlets and MBFC-2025, spanning Alexa graphs, hyperlink graphs, LLM-derived graphs, articles, and Wikipedia descriptions; (iii) systematic evaluation and analysis of embedding views and fusion strategies, including an RL-based fusion variant; and (iv) extensive experiments that achieve state-of-the-art results on ACL-2020 and establish strong benchmarks on MBFC-2025.
%U https://aclanthology.org/2026.findings-acl.1264/
%P 25265-25286
Markdown (Informal)
[A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis](https://aclanthology.org/2026.findings-acl.1264/) (Manzoor et al., Findings 2026)
ACL
- Muhammad Arslan Manzoor, Dilshod Azizov, Daniil Orel, Umer Siddique, Zain Muhammad Mujahid, Yufang Hou, and Preslav Nakov. 2026. A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25265–25286, San Diego, California, United States. Association for Computational Linguistics.