TY - CHAP
T1 - Decoding News Avoidance
T2 - 10th International Conference on Human Aspects of IT for the Aged Population, ITAP 2024, held as part of the 26th HCI International Conference, HCII 2024
AU - Pita, Manuel
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Understanding the patterns and mechanisms behind news consumption and avoidance is crucial for fostering democratic participation and informed societies. This methodological paper introduces an approach designed to study news avoidance, addressing the limitations and biases associated with traditional self-report surveys and digital-trace data collection. We propose an intelligent, dialogical news delivery application that simulates a real-world news consumption environment. This application segments content to provide nuanced interaction data while controlling for self-report response biases. Thus, the proposed method allows for the integration of behavioural and self-report data, leveraging the strengths of these divergent data types to offer a more comprehensive understanding of news engagement dynamics. By enabling controlled yet naturalistic interactions with news content, our approach seeks to unveil the multifaceted reasons behind news avoidance across different demographics, with a particular focus on understanding inter-generational dynamics. This paper underscores the importance of developing robust methodological tools in media studies to derive scientifically valid and replicable inferences that explain news consumption behaviours.
AB - Understanding the patterns and mechanisms behind news consumption and avoidance is crucial for fostering democratic participation and informed societies. This methodological paper introduces an approach designed to study news avoidance, addressing the limitations and biases associated with traditional self-report surveys and digital-trace data collection. We propose an intelligent, dialogical news delivery application that simulates a real-world news consumption environment. This application segments content to provide nuanced interaction data while controlling for self-report response biases. Thus, the proposed method allows for the integration of behavioural and self-report data, leveraging the strengths of these divergent data types to offer a more comprehensive understanding of news engagement dynamics. By enabling controlled yet naturalistic interactions with news content, our approach seeks to unveil the multifaceted reasons behind news avoidance across different demographics, with a particular focus on understanding inter-generational dynamics. This paper underscores the importance of developing robust methodological tools in media studies to derive scientifically valid and replicable inferences that explain news consumption behaviours.
KW - digital-trace data
KW - news avoidance
KW - research methods
KW - self-report data
UR - http://www.scopus.com/inward/record.url?scp=85195850584&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-61543-6_28
DO - 10.1007/978-3-031-61543-6_28
M3 - Chapter
AN - SCOPUS:85195850584
SN - 9783031615429
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 398
EP - 416
BT - HCI (42)
A2 - Gao, Qin
A2 - Zhou, Jia
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 29 June 2024 through 4 July 2024
ER -
