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Authors: João Pereira 1 ; Wenderson Wanzeller 1 and António Rosado da Cruz 1 ; 2

Affiliations: 1 ADiT-LAB, Instituto Politécnico de Viana do Castelo, 4900-348 Viana do Castelo, Portugal ; 2 ALGORITMI Research Lab, Universidade do Minho, Guimarães, Portugal

Keyword(s): AI, Artificial Intelligence, News, Newspaper, LLM, Large Language Models, Journalist, Breaking News.

Abstract: With easy access to the internet, anyone can search for the latest news. However, the news found on the internet, especially on social media, are often of dubious origin. This article explores how new technologies can help journalists in their day-to-day work. We therefore sought to create a platform for generating hot news content using Artificial Intelligence, namely Large Language Models (LLMs), combined with Search Engine Optimization (SEO). We investigate how LLMs impact content production, analyzing their ability to create compelling and accurate narratives in real time. Additionally, we examine how SEO integration can optimize the visibility and relevance of this content in search engines. This work highlights the importance of strategically combining these technologies to improve efficiency in disseminating news, adapting to the dynamism of online information and the demands of a constantly evolving audience.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Pereira, J.; Wanzeller, W. and Rosado da Cruz, A. (2024). Generation of Breaking News Contents Using Large Language Models and Search Engine Optimization. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7; ISSN 2184-4992, SciTePress, pages 888-893. DOI: 10.5220/0012739200003690

@conference{iceis24,
author={João Pereira. and Wenderson Wanzeller. and António {Rosado da Cruz}.},
title={Generation of Breaking News Contents Using Large Language Models and Search Engine Optimization},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={888-893},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012739200003690},
isbn={978-989-758-692-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Generation of Breaking News Contents Using Large Language Models and Search Engine Optimization
SN - 978-989-758-692-7
IS - 2184-4992
AU - Pereira, J.
AU - Wanzeller, W.
AU - Rosado da Cruz, A.
PY - 2024
SP - 888
EP - 893
DO - 10.5220/0012739200003690
PB - SciTePress