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Featured 2024 Journal Article Cognitive biases in fact-checking and their countermeasures: a reviewSoprano, Michael, Roitero, Kevin, La Barbera, David, Ceolin, Davide, Spina, Damiano, Demartini, Gianluca and Mizzaro, Stefano (2024). Cognitive biases in fact-checking and their countermeasures: a review. Information Processing and Management, 61 (3) 103672. doi: 10.1016/j.ipm.2024.103672 |
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Featured 2024 Conference Publication CoLAL: Co-learning active learning for text classificationLe, Linh, Zhao, Genghong, Zhang, Xia, Zuccon, Guido and Demartini, Gianluca (2024). CoLAL: Co-learning active learning for text classification. Thirty-Eighth AAAI Conference on Artificial Intelligence, Vancouver, BC Canada, 20–27 February 2024. Washington, DC United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i12.29235 |
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Featured 2024 Journal Article Who Determines What Is Relevant? Humans or AI? Why Not Both? : A spectrum of human–AI collaboration in assessing relevanceFaggioli, Guglielmo, Dietz, Laura, Clarke, Charles, Demartini, Gianluca, Hagen, Matthias, Hauff, Claudia, Kando, Noriko, Kanoulas, Evangelos, Potthast, Martin, Stein, Benno and Wachsmuth, Henning (2024). Who Determines What Is Relevant? Humans or AI? Why Not Both? : A spectrum of human–AI collaboration in assessing relevance. Communications of the ACM, 67 (4), 31-34. doi: 10.1145/3624730 |
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Featured 2023 Journal Article Data bias managementDemartini, Gianluca, Roitero, Kevin and Mizzaro, Stefano (2023). Data bias management. Communications of the ACM, 67 (1), 28-32. doi: 10.1145/3611641 |
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Featured 2023 Journal Article Human-AI cooperation to tackle misinformation and polarizationSpina, Damiano, Sanderson, Mark, Angus, Daniel, Demartini, Gianluca, Mckay, Dana, Saling, Lauren L. and White, Ryen W. (2023). Human-AI cooperation to tackle misinformation and polarization. Communications of the ACM, 66 (7), 40-45. doi: 10.1145/3588431 |
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Featured 2022 Conference Publication Preferences on a budget: prioritizing document pairs when crowdsourcing relevance judgmentsRoitero, Kevin, Checco, Alessandro, Mizzaro, Stefano and Demartini, Gianluca (2022). Preferences on a budget: prioritizing document pairs when crowdsourcing relevance judgments. ACM Web Conference, Virtual/Lyon, France, 25-29 April 2022. New York, United States: Association for Computing Machinery. doi: 10.1145/3485447.3511960 |
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Featured 2020 Journal Article CrowdCO-OP: sharing risks and rewards in crowdsourcingFan, Shaoyang, Gadiraju, Ujwal, Checco, Alessandro and Demartini, Gianluca (2020). CrowdCO-OP: sharing risks and rewards in crowdsourcing. Proceedings of the ACM on Human-Computer Interaction, 4 (CSCW2) 132, 1-24. doi: 10.1145/3415203 |
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Featured 2020 Conference Publication Can the crowd identify misinformation objectively?: The effects of judgment scale and assessor's backgroundRoitero, Kevin, Soprano, Michael, Fan, Shaoyang, Spina, Damiano, Mizzaro, Stefano and Demartini, Gianluca (2020). Can the crowd identify misinformation objectively?: The effects of judgment scale and assessor's background. International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, China, 25-30 July 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401112 |
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Featured 2020 Conference Publication On understanding data worker interaction behaviorsHan, Lei, Chen, Tianwa, Demartini, Gianluca, Indulska, Marta and Sadiq, Shazia (2020). On understanding data worker interaction behaviors. SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, Virtual Event China, July 2020. New York, United States: Association for Computing Machinery. doi: 10.1145/3397271.3401059 |
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Featured 2020 Journal Article Adversarial attacks on crowdsourcing quality controlChecco, Alessandro, Bates, Jo and Demartini, Gianluca (2020). Adversarial attacks on crowdsourcing quality control. Journal of Artificial Intelligence Research, 67, 375-408. doi: 10.1613/jair.1.11332 |
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Featured 2019 Conference Publication Scalpel-CD: leveraging crowdsourcing and deep probabilistic modeling for debugging noisy training dataYang, Jie, Demartini, Gianluca, Smirnova, Alisa, Lu, Yuan, Yang, Dingqi and Cudré-Mauroux, Philippe (2019). Scalpel-CD: leveraging crowdsourcing and deep probabilistic modeling for debugging noisy training data. World Wide Web Conference (WWW), San Francisco, CA, United States, 13-17 May 2019. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3308558.3313599 |
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Featured 2015 Conference Publication Understanding malicious behavior in crowdsourcing platforms: the case of online surveysGadiraju, Ujwal, Kawase, Ricardo, Dietze, Stefan and Demartini, Gianluca (2015). Understanding malicious behavior in crowdsourcing platforms: the case of online surveys. 33rd Annual CHI Conference on Human Factors in Computing Systems - CHI'15, Seoul, South Korea, 18 - 23 April 2015. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2702123.2702443 |
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Featured 2015 Conference Publication The dynamics of micro-task crowdsourcing: the case of amazon MTurkDifallah, Djellel Eddine, Catasta, Michele, Demartini, Gianluca, Ipeirotis, Panagiotis G. and Cudré-Mauroux, Philippe (2015). The dynamics of micro-task crowdsourcing: the case of amazon MTurk. 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, 18 - 22 May 2015. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2736277.2741685 |
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2024 Conference Publication Optimizing LLMs with direct preferences: a data efficiency perspectiveBernardelle, Pietro and Demartini, Gianluca (2024). Optimizing LLMs with direct preferences: a data efficiency perspective. SIGIR-AP 2024: 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY, United States: ACM. doi: 10.1145/3673791.3698411 |
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2024 Conference Publication Influence of metadata on quality evaluation of unstructured information artefactsZhou, Hui, Demartini, Gianluca, Indulska, Marta and Sadiq, Shazia (2024). Influence of metadata on quality evaluation of unstructured information artefacts. The Australasian Conference on Information Systems (ACIS 2024), Canberra, ACT Australia, 4-6 December, 2024. AIS Electronic Library. |
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2024 Other Outputs Multimodal Entity Linking Evaluation Dataset for Art (Version 3.0)Demartini, Gianluca, Le, Thai Linh, Krestel, Ralf and Sierra, Alejandro (2024). Multimodal Entity Linking Evaluation Dataset for Art (Version 3.0). The University of Queensland. (Dataset) doi: 10.48610/8a1ccdf |
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2024 Journal Article Crowdsourced Fact-checking: Does It Actually Work?Barbera, David La, Maddalena, Eddy, Soprano, Michael, Roitero, Kevin, Demartini, Gianluca, Ceolin, Davide, Spina, Damiano and Mizzaro, Stefano (2024). Crowdsourced Fact-checking: Does It Actually Work?. Information Processing & Management, 61 (5) 103792, 103792. doi: 10.1016/j.ipm.2024.103792 |
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2024 Conference Publication Hate speech detection with generalizable target-aware fairnessChen, Tong, Wang, Danny, Liang, Xurong, Risius, Marten, Demartini, Gianluca and Yin, Hongzhi (2024). Hate speech detection with generalizable target-aware fairness. KDD '24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 25-29 August 2024. New York, NY, United States: ACM. doi: 10.1145/3637528.3671821 |
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2024 Journal Article Longitudinal Loyalty: Understanding The Barriers To Running Longitudinal Studies On Crowdsourcing PlatformsSoprano, Michael, Roitero, Kevin, Gadiraju, Ujwal, Maddalena, Eddy and Demartini, Gianluca (2024). Longitudinal Loyalty: Understanding The Barriers To Running Longitudinal Studies On Crowdsourcing Platforms. ACM Transactions on Social Computing, 7 (1-4), 1-49. doi: 10.1145/3674884 |
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2024 Journal Article Human-AI cooperation to tackle misinformation and polarizationSpina, Damiano, Sanderson, Mark, Angus, Daniel, Demartini, Gianluca, McKay, Dana, Saling, Lauren L. and White, Ryen W. (2024). Human-AI cooperation to tackle misinformation and polarization. Communications of the ACM, 67 (7), 40-45. doi: 10.1145/3588431 |
