Theo Lynn

dblp:135/3110 · DBLP profile ↗
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6ranked-venue papers in the field
1as first author
5since 2021 · last 2024
0000-0001-9284-7580ORCID · verified

Domains — venue-derived; a paper can count in several

Data Mining & Knowledge Discovery · 2Business Process & Enterprise Data · 2Information Retrieval & Web Search · 1Knowledge Engineering, Semantic Web & Information Systems · 1 (1 first)
YearPublicationVenuePosition
2024 Categorising Corruption in the Vaccine Discourse: A General Taxonomy, Data Set, and Evaluation of LLMs for Classifying Corruption Dialogue in Social Media
Vitor Gaboardi Dos Santos, Guto Leoni Santos, Antonia Egli, Estatira Kahvazadeh, Bill Doolin, Patricia Takako Endo, Theo Lynn
ASONAM (1)7
2024 Detecting Homophobic Speech in Soccer Tweets Using Large Language Models and Explainable AI
Guto Leoni Santos, Vitor Gaboardi Dos Santos, Colm Kearns, Gary Sinclair, Jack Black, Mark Doidge, Thomas Fletcher, Dan Kilvington, Katie Liston, Patricia Takako Endo, Theo Lynn
ASONAM (1)11
2024 Kicking Prejudice: Large Language Models for Racism Classification in Soccer Discourse on Social Media
Guto Leoni Santos, Vitor Gaboardi Dos Santos, Colm Kearns, Gary Sinclair, Jack Black, Mark Doidge, Thomas Fletcher, Dan Kilvington, Patricia Takako Endo, Katie Liston, Theo Lynn
CAiSE11
2024 Identifying Citizen-Related Issues from Social Media Using LLM-Based Data Augmentation
Vitor Gaboardi Dos Santos, Guto Leoni Santos, Theo Lynn, Boualem Benatallah
CAiSE3
2022 Examining the determinants of acceptance and use of mobile contact tracing applications in Brazil: An extended privacy calculus perspective
abstract
Abstract Mobile contact tracing applications have emerged as a potential solution to track and reduce the transmission of viruses such as Covid‐19. These applications require the disclosure of potentially sensitive personal information thus generating understandable implications for personal privacy. This research aims to determine the factors driving acceptance of these applications, with acceptance represented by three distinct variables, namely usage intentions, willingness to disclose personal data, and willingness to rely on health advice. The study examines the influence of perceived privacy, social influence, and benefits on acceptance of contact tracing applications among a sample of 1,114 Brazilian citizens. The study leverages social contract theory to demonstrate the importance of perceived control and perceived surveillance in the formation of individuals' perceptions of privacy. Integrating privacy calculus theory with social contract theory to include reciprocity and social influence, our findings suggest that perceived privacy, reciprocal benefits, and social influence all positively influence individuals' intentions to download or continue the use of contact tracing applications, while intentions to disclose information are influenced by adoption intentions, perceived privacy, and reciprocal benefits and individuals' willingness to rely on contact tracing applications for health advice is influenced by reciprocal benefits and disclosure intentions.
Grace Fox, Lisa van der Werff, Pierangelo Rosati, Patricia Takako Endo, Theo Lynn
J. Assoc. Inf. Sci. Technol.5
2016 Development of a Cloud Trust Label: A Delphi Approach
abstract
The purpose of this research is to identify the potential information components of an online, real-time trust label, which is proposed as a communication mechanism to encourage trust in cloud service providers and cloud computing products. An online Delphi process was used with 28 cloud computing experts (including vendors, software providers, and legal and business representatives). The proposed label contains 81 information components, covering the cloud service provider (e.g. physical location, legal jurisdiction), the cloud service itself (e.g. data location, security, backup, certification), and a historical service-level summary (e.g. uptime data, support response times). The potential benefits of such a label to encourage trustworthiness perceptions and trust behaviors in the cloud computing environment are explored. Limitations of the study are highlighted, and further research studies are suggested to test the concept of the label and to refine the components of the label itself.
Theo Lynn, Lisa van der Werff, Graham Hunt, Philip D. Healy
J. Comput. Inf. Syst.1