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Pregled bibliografske jedinice broj: 1260450

Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method


Barbosa, Belem; Ramón Saura, Jose; Borovac Zekan, Senka; Ribeiro-Soriano, Domingo
Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method // Annals of Operations Research, 322 (2023), 2; 1-26 doi:10.1007/s10479-023-05261-1 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1260450 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method

Autori
Barbosa, Belem ; Ramón Saura, Jose ; Borovac Zekan, Senka ; Ribeiro-Soriano, Domingo

Izvornik
Annals of Operations Research (0254-5330) 322 (2023), 2; 1-26

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
content marketing, user behavior, sentiment analysis, topic modelling, user generated content

Sažetak
Content marketing involves producing and distributing content effectively and initially through digital channels. However, digital marketing strategies and business models can succeed only if content marketing is developed correctly. This study aims to develop a relevant theoretical framework linked to content marketing and identify the leading techniques and uses linked to its development. In this context, we developed an innovative data-driven methodology consisting of three steps. In the first phase, sentiment analysis that works with machine learning was conducted with Textblob, and four experiments were performed using support vector classifier, multinomial naïve bayes, logistic regression, and random forest classifier. First, we aimed to increase the accuracy of sentiment analysis (negative, neutral and positive) of a sample of user-generated content collected from the social network Twitter. Second, a mathematical topic- modelling algorithm known as latent dirichlet allocation was used to divide the database into topics. Finally, a textual analysis was developed using the Python programming language. Based on the results, we identified 11 topics, of which four were positive (Smart Content, Video Marketing, Podcast, and Influencer Marketing). Six of them were neutral (Content Personalization, Social Media Posts, Blogging, search engine optimization, Advergames, and NFTs), and one was negative (Email Marketing). Our results suggest that companies should use content personalisation ethically, mainly when AI- based techniques are used to predict user behaviors. While content marketing strategies are a fundamental part of digital marketing tactics, they can elicit changes in user online behavior when Big Data or AI algorithms are used. This fact raises concerns about the non-ethical design of online strategies in digital environments and the imperative that content marketing strategies should not be developed with purely economic and profitability interests.

Izvorni jezik
Engleski



POVEZANOST RADA


Ustanove:
Sveučilište u Splitu,
Sveučilište u Splitu Sveučilišni odjel za stručne studije

Profili:

Avatar Url Senka Borovac Zekan (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi doi.org

Poveznice na istraživačke podatke:

link.springer.com

Citiraj ovu publikaciju:

Barbosa, Belem; Ramón Saura, Jose; Borovac Zekan, Senka; Ribeiro-Soriano, Domingo
Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method // Annals of Operations Research, 322 (2023), 2; 1-26 doi:10.1007/s10479-023-05261-1 (međunarodna recenzija, članak, znanstveni)
Barbosa, B., Ramón Saura, J., Borovac Zekan, S. & Ribeiro-Soriano, D. (2023) Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method. Annals of Operations Research, 322 (2), 1-26 doi:10.1007/s10479-023-05261-1.
@article{article, author = {Barbosa, Belem and Ram\'{o}n Saura, Jose and Borovac Zekan, Senka and Ribeiro-Soriano, Domingo}, year = {2023}, pages = {1-26}, DOI = {10.1007/s10479-023-05261-1}, keywords = {content marketing, user behavior, sentiment analysis, topic modelling, user generated content}, journal = {Annals of Operations Research}, doi = {10.1007/s10479-023-05261-1}, volume = {322}, number = {2}, issn = {0254-5330}, title = {Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method}, keyword = {content marketing, user behavior, sentiment analysis, topic modelling, user generated content} }
@article{article, author = {Barbosa, Belem and Ram\'{o}n Saura, Jose and Borovac Zekan, Senka and Ribeiro-Soriano, Domingo}, year = {2023}, pages = {1-26}, DOI = {10.1007/s10479-023-05261-1}, keywords = {content marketing, user behavior, sentiment analysis, topic modelling, user generated content}, journal = {Annals of Operations Research}, doi = {10.1007/s10479-023-05261-1}, volume = {322}, number = {2}, issn = {0254-5330}, title = {Defining content marketing and its influence on online user, behavior: a data-driven prescriptive analytics method}, keyword = {content marketing, user behavior, sentiment analysis, topic modelling, user generated content} }

Citati:





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