Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85549 |
Resumo: | Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms. |
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Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunitiesData scienceDigital journalismText miningSystematic literature reviewMedia analyticsMachine LearningCiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyDigital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020.Pergamon-Elsevier Science LtdUniversidade do MinhoFernandes, ElizabethMoro, SergioCortez, Paulo20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85549engFernandes, E., Moro, S., & Cortez, P. (2023, July). Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities. Expert Systems with Applications. Elsevier BV. http://doi.org/10.1016/j.eswa.2023.1197950957-417410.1016/j.eswa.2023.119795https://www.sciencedirect.com/science/article/pii/S0957417423002968info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-16T01:18:51Zoai:repositorium.sdum.uminho.pt:1822/85549Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:17:53.357865Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
title |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
spellingShingle |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities Fernandes, Elizabeth Data science Digital journalism Text mining Systematic literature review Media analytics Machine Learning Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
title_short |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
title_full |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
title_fullStr |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
title_full_unstemmed |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
title_sort |
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities |
author |
Fernandes, Elizabeth |
author_facet |
Fernandes, Elizabeth Moro, Sergio Cortez, Paulo |
author_role |
author |
author2 |
Moro, Sergio Cortez, Paulo |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Fernandes, Elizabeth Moro, Sergio Cortez, Paulo |
dc.subject.por.fl_str_mv |
Data science Digital journalism Text mining Systematic literature review Media analytics Machine Learning Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
topic |
Data science Digital journalism Text mining Systematic literature review Media analytics Machine Learning Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
description |
Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/85549 |
url |
https://hdl.handle.net/1822/85549 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fernandes, E., Moro, S., & Cortez, P. (2023, July). Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities. Expert Systems with Applications. Elsevier BV. http://doi.org/10.1016/j.eswa.2023.119795 0957-4174 10.1016/j.eswa.2023.119795 https://www.sciencedirect.com/science/article/pii/S0957417423002968 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Pergamon-Elsevier Science Ltd |
publisher.none.fl_str_mv |
Pergamon-Elsevier Science Ltd |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132632169054208 |