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publications
Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach
Published in 54th Hawaii International Conference on System Sciences, 2021
This study develops a model to predict the helpfulness of online product reviews using signaling and social influence theories, incorporating review order and time intervals. Applying deep learning to 239,297 reviews, the model outperforms existing approaches and offers insights for sorting new reviews.
Recommended citation: Namvar, M., Boyce, J., Sarna, J., Zheng, Y., Chua, A. Y. K., & Ameli, S. (2021, January). Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach. In HICSS (pp. 1-9).
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Exploring the Relationship between Influencers’ Sentiment and Cryptocurrency Fluctuation through Microblogs
Published in Australasian Conference on Information Systems 2022, Melbourne, 2022
This research explores the relationship between microblog messages, particularly those from influencers, and bitcoin fluctuations, using natural language processing and hypothesis testing. Preliminary findings suggest that while extreme sentiment in influencers’ tweets is generally not linked to future bitcoin fluctuations, in-depth and unique extreme tweets moderate this negative relationship. The study highlights that influencers’ sentiment can predict bitcoin fluctuation, but the impact of tweets varies.
Recommended citation: Namvar, Morteza; Li, Jingqi; Boyce, James; Akhlaghpour, Saeed; and Indulska, Marta, "Exploring the Relationship between Influencers’ Sentiment and Cryptocurrency Fluctuation through Microblogs" (2022). ACIS 2022 Proceedings. 61.
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Iterative Seed Word Generation for Interactive Topic Modelling: a Mixed Text Processing and Qualitative Content Analysis Approach
Published in 43rd International Conference on Information Systems, Copenhagen 2022, 2022
This study introduces a novel method for improving topic modeling by iteratively generating seed words to guide the process, addressing the challenge of unsupervised models producing unclear topics. It applies this method to analyze user satisfaction with Contact Tracing Mobile Applications (CTMAs) during the COVID-19 pandemic, showing that it outperforms traditional topic modeling in capturing relevant research variables.
Recommended citation: Namvar, Morteza; Akhlaghpour, Saeed; Boyce, James; and Sharifi Khajedehi, Salma, "Iterative Seed Word Generation for Interactive Topic Modelling: a Mixed Text Processing and Qualitative Content Analysis Approach" (2022). ICIS 2022 Proceedings. 6.
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The Interplay of Heuristic and Systematic Processing of Information in News Validation: A Natural Language Processing Approach
Published in 58th Hawaii International Conference on System Sciences, 2025
This study explores how text features, such as content similarity, loaded language, and sentiment intensity, influence users’ perceptions of online news validity, offering insights for improving news credibility assessment on social media platforms.
Recommended citation: Boyce J, Namvar M, Lyu B, Liu Y, Mensforth M, Tao HWA (2024) The Interplay of Heuristic and Systematic Processing of Information in News Validation: A Natural Language Processing Approach, 58th Hawaii International Conference on System Sciences (HICSS), Hawaii, USA
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talks
The Matrix of Influence: Generative AI Digital Nudging Permalink
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This talk explores the use of Generative AI (GAI), combined with Natural Language Processing (NLP) and Machine Learning (ML), to create dynamic, personalized digital nudges that adapt to user behavior in real time. Unlike traditional predefined nudges, GAI-driven nudges offer greater flexibility and responsiveness, enhancing user engagement and decision-making. The research aims to bridge AI personalization with practical applications in Information Systems and explore ethical considerations in AI-driven user experience design.
teaching
The University of Queensland
Undergraduate/Postgraduate Courses, The University of Queensland, UQ Business School, 2021 to present
In the BISM3206 and BISM7217 courses, I teach postgraduate and undergraduate students how to apply Machine Learning using Python, while encouraging critical thinking about real-world applications. The courses have received excellent feedback, with students appreciating the course material, my clear explanations, approachability, and support in understanding complex concepts.