Unit:
Department of Communication and Media StudiesLibrary of the Faculties of Political Science and Public Administration, Communication and Mass Media Studies, Turkish and Modern Asian Studies, Sociology
Author:
Sotirakou Aikaterini-Alexandra
Dissertation committee:
Κωνσταντίνος Μουρλάς, Αναπληρωτής Καθηγητής, Τμήμα Επικοινωνίας και Μέσων Μαζικής Ενημέρωσης, Σχολή Οικονομικών και Πολιτικών Επιστημών, ΕΚΠΑ
Αντώνης Αρμενάκης, Επίκουρος Καθηγητής, Τμήμα Επικοινωνίας και Μέσων Μαζικής Ενημέρωσης, Σχολή Οικονομικών και Πολιτικών Επιστημών, ΕΚΠΑ
Hajo Boomgaarden, Καθηγητής, Τμήμα Επικοινωνίας του Πανεπιστημίου της Βιέννης
Στυλιανός Παπαθανασόπουλος, Καθηγητής, Τμήμα Επικοινωνίας και Μέσων Μαζικής Ενημέρωσης, Σχολή Οικονομικών και Πολιτικών Επιστημών, ΕΚΠΑ
Σπύρος Μοσχονάς, Καθηγητής, Τμήμα Επικοινωνίας και Μέσων Μαζικής Ενημέρωσης, Σχολή Οικονομικών και Πολιτικών Επιστημών, ΕΚΠΑ
Μαρίνα Ρήγου, Επίκουρη Καθηγήτρια, Τμήμα Επικοινωνίας και Μέσων Μαζικής Ενημέρωσης, Σχολή Οικονομικών και Πολιτικών Επιστημών, ΕΚΠΑ
Παναγιώτης Σταματόπουλος, Επίκουρος Καθηγητής, Τμήμα Πληροφορικής και Τηλεπικοινωνιών, Σχολή Οικονομικών και Πολιτικών Επιστημών, ΕΚΠΑ
Original Title:
Re-defining Quality in Journalism and Audience Engagement in the Digital Era: A Computational Approach Using Big Data and AI
Translated title:
Re-defining Quality in Journalism and Audience Engagement in the Digital Era: A Computational Approach Using Big Data and AI
Summary:
The way news is consumed and circulated has undergone significant changes, with the proliferation of the internet and social media platforms providing new avenues for news organizations to explore. Publishers are under increasing pressure to demonstrate their reach and to engage with their audiences online, allowing news consumers’ preferences to influence their daily agenda. Additionally, technology giants and algorithmic curation challenge the survival of news organizations that must compete with various actors on social media, all while remaining committed to reporting the truth and producing high-quality journalism.
In today’s hybrid media environment where traditional values like objectivity have shifted allowing space for more intimacy in journalism, social media engagement has become an important metric for the success of a news piece. In the pursuit of the magic recipe for writing quality and compelling news stories, this dissertation will conceptualize quality and engagement based on various communication theories, and utilize data journalism and artificial intelligence techniques to identify certain attributes in news articles that could influence their perceived quality and engagement.
Throughout the studies and discussions, the research explores various quality dimensions in different settings such as blogging platforms, online news outlets, and social media. Furthermore, textual, visual and contextual information are taken into consideration to provide a deeper understanding of how the elements of a news story influence its quality and its potential to become engaging.
This dissertation explores the use of quantitative and computational methods, specifically explainable artificial intelligence, in the social study of journalism. The focus is on identifying hidden relationships within data and using those insights to inform the creation of high-quality news stories that are engaging for readers. The findings show that characteristics such as the depth of a story, its diversity, the conveyed emotions, readability, and the choice of images can predict both the quality and social media engagement of a news article.
The objective of this research is to provide guidance for journalists on how to create captivating content using effective techniques and images while maintaining journalistic standards. This work has the potential to be applied in academia and the media industry, both advancing the theoretical foundations of journalism studies and providing practical applications for news production.
Main subject category:
Social, Political and Economic sciences
Keywords:
News, data journalism, digital journalism, news quality, computational social science, audience engagement, emotions, artificial intelligence, machine learning, explainable AI, image recognition.
Number of references:
629
