Data Science, Machine learning and big data in Digital Journalism - A survey of state-of-the-art, challenges and opportunities

Aug 5, 2026

Digital journalism faces rapid transformation as media companies must apply data science algorithms to remain competitive in a Big Data environment. Machine learning and artificial intelligence have accelerated research and adoption, particularly for personalization and recommendation. This paper provides a critical survey using a systematic literature review that integrates bibliometric search, text mining, and qualitative analysis. It synthesizes major DS application areas in digital journalism, identifies research gaps, and outlines challenges and opportunities for future studies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Introduction

Motivation and industry context

Systematic literature review methodology

Data collection and analysis

Research domains identified

Expert Systems With Applications 221 (2023) 119795 Contents lists available at ScienceDirect Expert Systems With Applications journal [homepage:](homepage: www.elsevier.com/locate/eswa)[ www.elsevier.com/locate/eswa](homepage: www.elsevier.com/locate/eswa) | Review
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities | | | | | --- | --- | --- | --- | | Elizabeth Fernandes a, *, S´ergio Morob, Paulo Cortez c
a ISCTE – Instituto Universit´ario de Lisboa (ISCTE-IUL), ISTAR, Avenida das Forças Armadas, Edifício II, D615, 1649-026 LISBOA, Portugal b Instituto Universit´ario de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal
c ALGORITMI Research Centre, University of Minho, Guimar˜aes, Portugal | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Data science
Digital journalism
Text mining
Systematic literature review Media analytics
Machine Learning | | Digital journalism has faced a dramatic change and media companies are challenged to use data science algorithms 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 opportunities for future studies. Through a systematic literature review integrating bibliometric search, text mining, 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 journalism solutions, and improving paywall mechanisms. | | 1. Introduction Digital innovation introduced a dramatic change in media companies. The decline of print advertising revenue, the distribution of free digital content and the change of reader’s behavior induced a need of new sources of revenue (Arrese, 2016; Rußell et al., 2020). Subscription business models, usually in the form of paywall models (Pattabhiramaiah et al., 2019; Rußell et al., 2020), become a solution to assure companies’ sustainability (Davoudi & Edall, 2018; Simon & Graves, 2019). Consequently, high level data-based Expert Systems models have emerged (Davoudi et al., 2018). Currently, each second of time results in millions of readers interacting on digital platforms, which provides huge volumes of data to be collected and stored by media companies (Lewis, 2015). This new Big Data era in Journalism demanded the development of new technologies and brought Data Science (DS) and Artificial Intelligence (AI) capabilities to the newsroom (Borges et al., 2021). Moreover, the adoption of Machine Learning (ML) methods is mentioned in the Reuters Digital Report as the new trend in media companies, especially for personalization and content recommendation (Newman et al., 2019; Yeung & Yang, 2010; Zihayat et al., 2019). Comment analysis, event mining, and journalism automation have attracted a great attention and nowadays continue being an outstanding research area. Currently, ML

FAQs

What is the main goal of the survey on digital journalism?

To provide a critical literature review synthesizing the main application areas of data science in digital journalism, while highlighting research gaps, challenges, and opportunities.

How is the literature review conducted in the study?

Through a systematic literature review integrating bibliometric search, text mining, and qualitative discussion, using a transparent and reproducible process.

Which research domains are highlighted by the analysis?

The study identifies six domains including text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis with some machine learning approaches.

Aug 5, 2026

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