Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

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
Download Paper

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.
Download Paper

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.
Download Paper

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).
Download Paper