My publications can also be found at: GoogleScholar | Academia.edu | ResearchGate | Orchid ID | Researcher ID

G-Scholar citations: 3427 h-index: 31 i10-index: 64

 

Note: list of publications is generated automatically with Zotpress. Thanks to Katie Seaborn for the useful plug-in!

Categorization of publications may be slightly incorrect. Journal Article contain both refereed (most) and non-refereed articles.  Conference Paper section includes long, short, and workshop papers. Thesis section includes patents. 

Clicking on “(CITE” will upload citation to your bibliography management tool or allow you to save a .RIS file. 

 

Jia, C., & Gwizdka, J. (2021). An eye-tracking study of differences in reading between automated and human-written news. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. Randolph, & T. Fischer (Eds.), NeuroIS’2020. Cite
Bhattacharya, N., Rakshit, S., & Gwizdka, J. (2020). Towards Real-time Webpage Relevance Prediction UsingConvex Hull Based Eye-tracking Features. In ACM Symposium on Eye Tracking Research and Applications (pp. 1–10). Stuttgart, Germany: Association for Computing Machinery. https://doi.org/10.1145/3379157.3391302 Cite
Bhattacharya, N., Rakshit, S., Gwizdka, J., & Kogut, P. (2020). Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (pp. 223–233). Vancouver BC, Canada: Association for Computing Machinery. https://doi.org/10.1145/3343413.3377960 Cite
Gwizdka, J., & Dillon, A. (2020). Eye-Tracking as a Method for Enhancing Research on Information Search. In W. T. Fu & H. van Oostendorp (Eds.), Understanding and Improving Information Search: A Cognitive Approach (pp. 161–181). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-38825-6_9 Cite
Gwizdka, J., & Chang, Y.-S. (2020). Search Results Viewing Behavior vis-à-vis Relevance Criteria. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. Randolph, & T. Fischer (Eds.), Information Systems and Neuroscience (pp. 181–188). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-28144-1_20 Cite
Bhattacharya, N., Rakshit, S., & Gwizdka, J. (2020). Towards real-time webpage relevance prediction using convex hull based eye-tracking features. In Proceedings of the 2020 symposium on eye tracking research and applications (ETRA ’20 adjunct). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3379157.3391302 Cite
Sun, Y., Zhang, Y., Gwizdka, J., & Trace, C. B. (2019). Consumer Evaluation of the Quality of Online Health Information: Systematic Literature Review of Relevant Criteria and Indicators. Journal of Medical Internet Research, 21(5), e12522. https://doi.org/10.2196/12522 Cite
Gwizdka, J., Zhang, Y., & Dillon, A. (2019). Using the eye-tracking method to study consumer online health information search behaviour. Aslib Journal of Information Management, 71(6), 739–754. https://doi.org/10.1108/AJIM-02-2019-0050 Cite
Gwizdka, J. (2019). Exploring Eye-Tracking Data for Detection of Mind-Wandering on Web Tasks. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & A. B. Randolph (Eds.), Information Systems and Neuroscience (pp. 47–55). Springer International Publishing. https://doi.org/10.1007/978-3-030-01087-4_6 Cite
Gwizdka, J., Moshfeghi, Y., & Wilson, M. L. (2019). Introduction to the special issue on neuro-information science. Journal of the Association for Information Science and Technology, 70(9), 911–916. https://doi.org/10.1002/asi.24263 Cite
Ebeid, I. A., Bhattacharya, N., Gwizdka, J., & Sarkar, A. (2019). Analyzing Gaze Transition Behavior Using Bayesian Mixed Effects Markov Models. In Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications (pp. 5:1–5:5). New York, NY, USA: ACM. https://doi.org/10.1145/3314111.3319839 Cite
Bhattacharya, N., & Gwizdka, J. (2019). Measuring Learning During Search: Differences in Interactions, Eye-Gaze, and Semantic Similarity to Expert Knowledge. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval (pp. 63–71). New York, NY, USA: ACM. https://doi.org/10.1145/3295750.3298926 Cite
Bilal, D., & Gwizdka, J. (2018). Children’s query types and reformulations in Google search. Information Processing & Management, 54(6), 1022–1041. https://doi.org/10.1016/j.ipm.2018.06.008 Cite
Chang, Y.-S., & Gwizdka, J. (2018). Relevance criteria dynamics: A study of online news selection on SERPs. Proceedings of the Association for Information Science and Technology, 55(1), 768–769. https://doi.org/10.1002/pra2.2018.14505501108 Cite
Gwizdka, J. (2018). Inferring Web Page Relevance Using Pupillometry and Single Channel EEG. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & A. B. Randolph (Eds.), Information Systems and Neuroscience (pp. 175–183). Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-67431-5_20 Cite
Gwizdka, J. (2018). Neuro-physiological data as a source of evaluation metrics for personalized IR. In Proceedings of the Workshop on Evaluation of Personalisation in Information Retrieval - WEPIR’2018 held at ACM SIGIR CHIIR’2018. New Brunswick, NJ, USA. Cite
Bhattacharya, N., & Gwizdka, J. (2018). Relating Eye-tracking Measures with Changes in Knowledge on Search Tasks. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (pp. 62:1–62:5). New York, NY, USA: ACM. https://doi.org/10.1145/3204493.3204579 Cite
Ebeid, I. A., & Gwizdka, J. (2018). Real-time Gaze Transition Entropy. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (pp. 94:1–94:3). New York, NY, USA: ACM. https://doi.org/10.1145/3204493.3208340 Cite
Gwizdka, J., Hosseini, R., Cole, M., & Wang, S. (2017). Temporal dynamics of eye-tracking and EEG during reading and relevance decisions. Journal of the Association for Information Science and Technology, 68(10), 2299–2312. https://doi.org/10.1002/asi.23904 Cite
Eickhoff, C., Gwizdka, J., Hauff, C., & He, J. (2017). Introduction to the special issue on search as learning. Information Retrieval Journal, 20(5), 399–402. https://doi.org/10.1007/s10791-017-9315-9 Cite
Smith, C. L., Gwizdka, J., & Feild, H. (2017). The use of query auto-completion over the course of search sessions with multifaceted information needs. Information Processing & Management, 53(5), 1139–1155. https://doi.org/10.1016/j.ipm.2017.05.001 Cite
Singh, V. K., Shah, C., Gwizdka, J., Joho, H., & Gurrin, C. (2017). From sensors to sense-making: Opportunities and challenges for information science. In Proceedings of the Association for Information Science and Technology (Vol. 54, pp. 599–602). https://doi.org/10.1002/pra2.2017.14505401083 Cite
Ye, Z., Gwizdka, J., Lopes, C. T., & Zhang, Y. (2017). Towards understanding consumers’ quality evaluation of online health information: A case study. In Proceedings of the Association for Information Science and Technology (Vol. 54, pp. 838–839). https://doi.org/10.1002/pra2.2017.14505401178 Cite
Gwizdka, J. (2017). I Can and So I Search More: Effects Of Memory Span On Search Behavior. In Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval (pp. 341–344). New York, NY, USA: ACM. https://doi.org/10.1145/3020165.3022148 Cite
Gwizdka, J., & Bilal, D. (2017). Analysis of Children’s Queries and Click Behavior on Ranked Results and Their Thought Processes in Google Search. In Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval (pp. 377–380). New York, NY, USA: ACM. https://doi.org/10.1145/3020165.3022157 Cite
Gwizdka, J., & Mostafa, J. (2017). NeuroIIR 2017: Challenges in Bringing Neuroscience to Research in Human-Information Interaction. In Proceedings of the 2017 ACM on Conference on Human Information Interaction and Retrieval. New York, NY, USA: ACM. https://doi.org/10.1145/3020165.3022165 Cite
Gwizdka, J. (2017). Differences in Reading Between Word Search and Information Relevance Decisions: Evidence from Eye-Tracking. In D. F. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, & B. A. Randolph (Eds.), Information Systems and Neuroscience: Gmunden Retreat on NeuroIS 2016 (pp. 141–147). Gmunden, Austria: Springer International Publishing. https://doi.org/10.1007/978-3-319-41402-7_18 Cite
Wang, S., Gwizdka, J., & Chaovalitwongse, W. A. (2016). Using Wireless EEG Signals to Assess Memory Workload in the n-Back Task. IEEE Transactions on Human-Machine Systems, 46(3), 424–435. https://doi.org/10.1109/THMS.2015.2476818 Cite
Beheshti, J., Bilal, D., Mackey, T. P., Limberg, L., Bartlett, J. C., Gwizdka, J., … Ishimura, Y. (2016). Information literacy: Bridging the gap between theory and practice. In Proceedings of the Association for Information Science and Technology (Vol. 53, pp. 1–6). https://doi.org/10.1002/pra2.2016.14505301019 Cite
Gwizdka, J., & Mostafa, J. (2016). NeuroIR 2015: SIGIR 2015 Workshop on Neuro-Physiological Methods in IR Research. SIGIR Forum, 49, 83–88. https://doi.org/10.1145/2888422.2888435 Cite
Gwizdka, J., Hansen, P., Hauff, C., He, J., & Kando, N. (Eds.). (2016). Proceedings of the Second International Workshop on Search as Learning co-located with the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016) (Vol. 1647). Aachen, Germany: CEUR. Retrieved from http://ceur-ws.org/Vol-1647/ Cite
Gwizdka, J., Hansen, P., Hauff, C., He, J., & Kando, N. (2016). Search As Learning (SAL) Workshop 2016. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1249–1250). New York, NY, USA: ACM. https://doi.org/10.1145/2911451.2917766 Cite
Gwizdka, J., & Chen, X. (2016). Towards Observable Indicators of Learning on Search. In Proceedings of the Second International Workshop on Search as Learning co-located with the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016). Pisa, Italy: CEUR. Retrieved from http://ceur-ws.org/Vol-1647/SAL2016_paper_19.pdf Cite
Zhang, Y., & Gwizdka, J. (2016). Rethinking the Cost of Information Search Behavior. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 969–972). New York, NY, USA: ACM. https://doi.org/10.1145/2911451.2914742 Cite
Smith, C. L., Gwizdka, J., & Feild, H. (2016). Exploring the Use of Query Auto Completion: Search Behavior and Query Entry Profiles. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval (pp. 101–110). New York, NY, USA: ACM. https://doi.org/10.1145/2854946.2854975 Cite
Mostafa, J., & Gwizdka, J. (2016). Deepening the Role of the User: Neuro-Physiological Evidence As a Basis for Studying and Improving Search. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval (pp. 63–70). New York, NY, USA: ACM. https://doi.org/10.1145/2854946.2854979 Cite
Bilal, D., & Gwizdka, J. (2016). Children’s Eye-fixations on Google Search Results. In Proceedings of the 79th ASIS&T Annual Meeting (Vol. 79, pp. 89:1–89:6). Silver Springs, MD, USA: American Society for Information Science. https://doi.org/10.1002/pra2.2016.14505301089 Cite
Gwizdka, J., & Zhang, Y. (2015). Towards Inferring Web Page Relevance – An Eye-Tracking Study. In Proceedings of iConference’2015 (p. 5). Retrieved from https://www.ideals.illinois.edu/handle/2142/73709 Cite
O’Brien, H. L., Gwizdka, J., Lopatovska, I., & Mostafa, J. (2015). Psycho-physiological Methods in Information Science: Fit or Fad? In iConference 2015 Proceedings. iSchools.org. Retrieved from https://www.ideals.illinois.edu/handle/2142/73773 Cite
Gwizdka, J., Jose, J., Mostafa, J., & Wilson, M. (2015). NeuroIR 2015: Neuro-Physiological Methods in IR Research. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1151–1153). New York, NY, USA: ACM. https://doi.org/10.1145/2766462.2767856 Cite
Gwizdka, J., & Zhang, Y. (2015). Differences in Eye-Tracking Measures Between Visits and Revisits to Relevant and Irrelevant Web Pages. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 811–814). New York, NY, USA: ACM. https://doi.org/10.1145/2766462.2767795 Cite
Gwizdka, J. (2014). Tracking Information Relevance. In Gmunden Retreat on NeuroIS 2014 (pp. 29–31). Gmunden, Austria. Retrieved from http://legacy.neurois.org/papers/2014/2014%20Proceedings%20Gmunden%20Retreat%20on%20NeuroIS%20paper%2013.pdf Cite
Zhang, Y., & Gwizdka, J. (2014). Effects of tasks at similar and different complexity levels. In Proceedings of the American Society for Information Science and Technology (Vol. 51, pp. 1–4). https://doi.org/10.1002/meet.2014.14505101078 Cite
Gwizdka, J. (2014). News Stories Relevance Effects on Eye-movements. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 283–286). New York, NY, USA: ACM. https://doi.org/10.1145/2578153.2578198 Cite
Freund, L., He, J., Gwizdka, J., Kando, N., Hansen, P., & Rieh, S. Y. (2014). Searching As Learning (SAL) Workshop 2014. In Proceedings of the 5th Information Interaction in Context Symposium (pp. 7–7). New York, NY, USA: ACM. https://doi.org/10.1145/2637002.2643203 Cite
Gwizdka, J. (2014). Characterizing Relevance with Eye-tracking Measures. In Proceedings of the 5th Information Interaction in Context Symposium (pp. 58–67). New York, NY, USA: ACM. https://doi.org/10.1145/2637002.2637011 Cite
Wei, X., Zhang, Y., & Gwizdka, J. (2014). YASFIIRE: Yet Another System for IIR Evaluation. In Proceedings of the 5th Information Interaction in Context Symposium (pp. 316–319). New York, NY, USA: ACM. https://doi.org/10.1145/2637002.2637051 Cite
Zhang, Y., Zhang, J., Lease, M., & Gwizdka, J. (2014). Multidimensional Relevance Modeling via Psychometrics and Crowdsourcing. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 435–444). New York, NY, USA: ACM. https://doi.org/10.1145/2600428.2609577 Cite
Freund, L., Gwizdka, J., Hansen, P., Kando, N., & Rieh, S. Y. (2013). From Searching to Learning. In M. Agosti, N. Fuhr, E. Toms, & P. Vakkari (Eds.), Evaluation Methodologies in Information Retrieval (Vol. 13441, pp. 102–105). Retrieved from http://drops.dagstuhl.de/opus/volltexte/2014/4433 Cite
Cole, M. J., Gwizdka, J., Liu, C., Belkin, N. J., & Zhang, X. (2013). Inferring user knowledge level from eye movement patterns. Information Processing & Management, 49(5), 1075–1091. https://doi.org/10.1016/j.ipm.2012.08.004 Cite