publications

Tim Draws. 2023. “Understanding Viewpoint Biases in Web Search Results.” PhD Thesis, Delft University of Technology. DOI: 10.4233/uuid:1b177026-6af7-48f3-ba04-ab7109db3c36. Full Version (PDF) | Compact Version (PDF)

🥈 Inel, Oana, Tim Draws, and Lora Aroyo. 2023. “Collect, Measure, Repeat: Reliability Factors for Responsible Data Collection.” In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. HCOMP ’23.

Wu, Zhangyi, Tim Draws, Federico Cau, Francesco Barile, Alisa Rieger, and Nava Tintarev. 2023. “Explaining Search Result Stances to Opinionated People.” In XAI World Conference.

Barile, Francesco, Tim Draws, Oana Inel, Alisa Rieger, Shabnam Najafian, Amir Ebrahimi Fard, Rishav Hada, and Nava Tintarev. 2023. “Evaluating Explainable Social Choice-Based Aggregation Strategies for Group Recommendation.” User Modeling and User-Adapted Interaction, June. https://doi.org/10.1007/s11257-023-09363-0.

Bink, Markus, Sebastian Schwarz, Tim Draws, and David Elsweiler. 2023. “Investigating the Influence of Featured Snippets on User Attitudes.” In ACM SIGIR Conference on Human Information Interaction and Retrieval. CHIIR ’23. New York, NY, USA: ACM. https://doi.org/10.1145/3576840.3578323.

Draws, Tim, Karthikeyan Natesan Ramamurthy, Ioana Baldini, Amit Dhurandhar, Inkit Padhi, Benjamin Timmermans, and Nava Tintarev. 2023. “Explainable Cross-Topic Stance Detection for Search Results.” In ACM SIGIR Conference on Human Information Interaction and Retrieval. CHIIR ’23. New York, NY, USA: ACM. https://doi.org/10.1145/3576840.3578296.

Draws, Tim, Nirmal Roy, Oana Inel, Alisa Rieger, Rishav Hada, Mehmet Orcun Yalcin, Benjamin Timmermans, and Nava Tintarev. 2023. “Viewpoint Diversity in Search Results.” In Advances in Information Retrieval, edited by Jaap Kamps, Lorraine Goeuriot, Fabio Crestani, Maria Maistro, Hideo Joho, Brian Davis, Cathal Gurrin, Udo Kruschwitz, and Annalina Caputo, 13980:279–97. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-28244-7_18.

Wu, Zhangyi, Tim Draws, Francesco Barile, and Nava Tintarev. 2023. “Explaining Search Result Stances to Opinionated People.” In XAI World Conference.

🏆 Yurrita, Mireia, Tim Draws, Agathe Balayn, Dave Murray-Rust, Nava Tintarev, and Alessandro Bozzon. 2023. “Disentangling Fairness Perceptions in Algorithmic Decision-Making: The Effects of Explanations, Human Oversight, and Contestability.” In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. CHI ’23. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3544548.3581161.

Draws, Tim, David La Barbera, Michael Soprano, Kevin Roitero, Davide Ceolin, Alessandro Checco, and Stefano Mizzaro. 2022. “The Effects of Crowd Worker Biases in Fact-Checking Tasks.” In 2022 ACM Conference on Fairness, Accountability, and Transparency, 2114–24. Seoul, Republic of Korea: ACM. https://doi.org/10.1145/3531146.3534629.

Van Lissa, Caspar J., Wolfgang Stroebe, Michelle R. vanDellen, N. Pontus Leander, Maximilian Agostini, Tim Draws, Andrii Grygoryshyn, Ben Gützgow, and others. 2022. “Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors during the Early Phase of the Pandemic.” Patterns 3 (4): 100482. https://doi.org/10.1016/j.patter.2022.100482.

🏆 Draws, Tim, Oana Inel, Nava Tintarev, Christian Baden, and Benjamin Timmermans. 2022. “Comprehensive Viewpoint Representations for a Deeper Understanding of User Interactions With Debated Topics.” In ACM SIGIR Conference on Human Information Interaction and Retrieval, 135–45. Regensburg Germany: ACM. https://doi.org/10.1145/3498366.3505812.

Sarafoglou, Alexandra, Anna van der Heijden, Tim Draws, Joran Cornelisse, Eric-Jan Wagenmakers, and Maarten Marsman. 2022. “Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project.” Psychology Learning & Teaching, February, 1–13. https://doi.org/10.1177/14757257221077307.

Hoogeveen, Suzanne, Alexandra Sarafoglou, Balazs Aczel, Yonathan Aditya, Alexandra J. Alayan, Peter J. Allen, Sacha Altay, et al. 2022. “A Many-Analysts Approach to the Relation between Religiosity and Well-Being.” Religion, Brain & Behavior 0 (0): 1–47. https://doi.org/10.1080/2153599X.2022.2070255.

Draws, Tim, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, and Benjamin Timmermans. 2021a. “This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics.” In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 295–305. SIGIR ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3404835.3462851.

Giunchiglia, Fausto, Styliani Kleanthous, Jahna Otterbacher, and Tim Draws. 2021. “Transparency Paths – Documenting the Diversity of User Perceptions.” In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 415–20. UMAP ’21. Utrecht Netherlands: ACM. https://doi.org/10.1145/3450614.3463292.

Doorn, Johnny van, Don van den Bergh, Udo Böhm, Fabian Dablander, Koen Derks, Tim Draws, Alexander Etz, et al. 2021. “The JASP Guidelines for Conducting and Reporting a Bayesian Analysis.” Psychonomic Bulletin & Review 28 (3): 813–26. https://doi.org/10.3758/s13423-020-01798-5.

Draws, Tim, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, and Benjamin Timmermans. 2021b. “Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics.” ACM SIGKDD Explorations Newsletter 23 (1): 50–58. https://doi.org/10.1145/3468507.3468515.

Barile, Francesco, Shabnam Najafian, Tim Draws, Oana Inel, Alisa Rieger, Rishav Hada, and Nava Tintarev. 2021. “Toward Benchmarking Group Explanations: Evaluating the Effect of Aggregation Strategies versus Explanation.” Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES 2021). http://ceur-ws.org/Vol-2955/paper11.pdf.

Draws, Tim. 2021. “Understanding How Algorithmic and Cognitive Biases in Web Search Affect User Attitudes on Debated Topics.” In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2709. SIGIR ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3404835.3463273.

🏆 Draws, Tim, Alisa Rieger, Oana Inel, Ujwal Gadiraju, and Nava Tintarev. 2021. “A Checklist to Combat Cognitive Biases in Crowdsourcing.” Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, HCOMP ’21, 9 (1): 48–59.

Draws, Tim, Zoltán Szlávik, Benjamin Timmermans, Nava Tintarev, Kush R. Varshney, and Michael Hind. 2021. “Disparate Impact Diminishes Consumer Trust Even for Advantaged Users.” In Persuasive Technology, edited by Raian Ali, Birgit Lugrin, and Fred Charles, 12684:135–49. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-79460-6_11.

Najafian, Shabnam, Tim Draws, Francesco Barile, Marko Tkalcic, Jie Yang, and Nava Tintarev. 2021. “Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations.” In Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 155–64. HT ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3465336.3475104.

🏆 Rieger, Alisa, Tim Draws, Nava Tintarev, and Mariet Theune. 2021. “This Item Might Reinforce Your Opinion: Obfuscation and Labeling of Search Results to Mitigate Confirmation Bias.” In Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 189–99. HT ’21. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3465336.3475101.

Draws, Tim, Jody Liu, and Nava Tintarev. 2020. “Helping Users Discover Perspectives: Enhancing Opinion Mining with Joint Topic Models.” In 2020 International Conference on Data Mining Workshops (ICDMW), 23–30. Sorrento, Italy: IEEE. https://doi.org/10.1109/ICDMW51313.2020.00013.

Bergh, Don van den, Johnny van Doorn, Maarten Marsman, Tim Draws, Erik-Jan van Kesteren, Koen Derks, Fabian Dablander, et al. 2020. “A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP.” Annee Psychologique 120 (1): 73–96. https://doi.org/10.3917/anpsy1.201.0073.