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Multilingual NLP models detect harmful and verifiable social media posts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed multilingual transformer-based NLP models capable of detecting social media posts that contain verifiable factual claims and harmful content. This study involved dataset collection, pre-processing, model training, and testing, with a focus on models that can process both English and low-resource languages like Arabic, Bulgarian, Dutch, Polish, Czech, and Slovak. The developed multi-label classification models demonstrated robustness when compared to state-of-the-art approaches, offering an efficient way to identify harmful and factually verifiable posts simultaneously. AI

IMPACT Enhances the ability to moderate social media content across multiple languages, improving safety and information integrity.

RANK_REASON The cluster describes a research paper detailing the development and evaluation of NLP models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Multilingual NLP models detect harmful and verifiable social media posts

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The cluster describes a research paper detailing the development and evaluation of NLP models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sebastian Kula ·

    Multilingual Models for Check-Worthy Social Media Posts Detection

    arXiv:2408.06737v2 Announce Type: replace Abstract: This work presents an extensive study of transformer-based NLP models application for detection of social media posts that contain verifiable factual claims and harmful claims. The study covers various activities, including data…

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