Pubblicazioni

@article{maddalena2024iir,

title = {Report on the 14th Italian Information Retrieval Workshop (IIR 2024)},

author = {Eddy Maddalena and Stefano Mizzaro and Kevin Roitero and Marco Viviani and David La Barbera and Sandip Modha and Gabriella Pasi and Francesca Da Ros and Michael Soprano},

url = {https://sigir.org/wp-content/uploads/2025/01/p15.pdf},

issn = {0163-5840},

year  = {2025},

date = {2025-01-16},

urldate = {2024-01-01},

journal = {SIGIR Forum},

volume = {58},

number = {2},

pages = {1–13},

publisher = {Association for Computing Machinery (ACM)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{spina2024responsibleai,

title = {Report on the Hands-On PhD Course on Responsible AI from the Lens of an Information Access Researcher},

author = {Damiano Spina and Kevin Roitero and Stefano Mizzaro and Vincenzo Della Mea and Francesca Da Ros and Michael Soprano and Hafsa Akebli and Alex Falcon and Mehdi Fasihi and Alessio Fiorin and David La Barbera and Daniele Lizzio Bosco and Riccardo Lunardi and Alberto Marturano and Zaka-Ud-Din Muhammad and Francesco Nascimben and Moritz Nottebaum and Massimiliano Pascoli and Mihai Horia Popescu and Laura Rasotto and Mubashara Rehman and Francesco Taverna and Biagio Tomasetig and Alessandro Tremamunno},

url = {https://sigir.org/wp-content/uploads/2025/01/p07.pdf},

issn = {0163-5840},

year  = {2025},

date = {2025-01-14},

urldate = {2024-01-16},

journal = {SIGIR Forum},

volume = {58},

number = {2},

pages = {1–61},

publisher = {Association for Computing Machinery (ACM)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3674884,

title = {Longitudinal Loyalty: Understanding The Barriers To Running Longitudinal Studies On Crowdsourcing Platforms},

author = {Michael Soprano and Kevin Roitero and Ujwal Gadiraju and Eddy Maddalena and Gianluca Demartini},

editor = {ACM},

url = {https://doi.org/10.1145/3674884},

doi = {10.1145/3674884},

issn = {2469-7818},

year  = {2024},

date = {2024-08-11},

urldate = {2024-08-11},

journal = {ACM Transactions on Social Computing},

volume = {1},

number = {1},

issue = {1},

pages = {50},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Crowdsourcing tasks have been widely used to collect a large number of human labels at scale. While some of these tasks are deployed by requesters and performed only once by crowd workers, others require the same worker to perform the same task or a variant of it more than once, thus participating in a so-called longitudinal study. Despite the prevalence of longitudinal studies in crowdsourcing, there is a limited understanding of factors that influence worker participation in them across different crowdsourcing marketplaces. We present results from a large-scale survey of 300 workers on 3 different micro-task crowdsourcing platforms: Amazon Mechanical Turk, Prolific and Toloka. The aim is to understand how longitudinal studies are performed using crowdsourcing. We collect answers about 547 experiences and we analyze them both quantitatively and qualitatively. We synthesize 17 take-home messages about longitudinal studies together with 8 recommendations for task requesters and 5 best practices for crowdsourcing platforms to adequately conduct and support such kinds of studies. We release the survey and the data at: https://osf.io/h4du9/.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BARBERA2024103792b,

title = {Crowdsourced Fact-checking: Does It Actually Work?},

author = {David La Barbera and Eddy Maddalena and Michael Soprano and Kevin Roitero and Gianluca Demartini and Davide Ceolin and Damiano Spina and Stefano Mizzaro},

url = {https://www.sciencedirect.com/science/article/pii/S0306457324001523},

doi = {10.1016/j.ipm.2024.103792},

issn = {0306-4573},

year  = {2024},

date = {2024-05-31},

urldate = {2024-05-31},

journal = {Information Processing & Management},

volume = {61},

number = {5},

pages = {103792},

abstract = {There is an important ongoing effort aimed to tackle misinformation and to perform reliable fact-checking by employing human assessors at scale, with a crowdsourcing-based approach. Previous studies on the feasibility of employing crowdsourcing for the task of misinformation detection have provided inconsistent results: some of them seem to confirm the effectiveness of crowdsourcing for assessing the truthfulness of statements and claims, whereas others fail to reach an effectiveness level higher than automatic machine learning approaches, which are still unsatisfactory. In this paper, we aim at addressing such inconsistency and understand if truthfulness assessment can indeed be crowdsourced effectively. To do so, we build on top of previous studies; we select some of those reporting low effectiveness levels, we highlight their potential limitations, and we then reproduce their work attempting to improve their setup to address those limitations. We employ various approaches, data quality levels, and agreement measures to assess the reliability of crowd workers when assessing the truthfulness of (mis)information. Furthermore, we explore different worker features and compare the results obtained with different crowds. According to our findings, crowdsourcing can be used as an effective methodology to tackle misinformation at scale. When compared to previous studies, our results indicate that a significantly higher agreement between crowd workers and experts can be obtained by using a different, higher-quality, crowdsourcing platform and by improving the design of the crowdsourcing task. Also, we find differences concerning task and worker features and how workers provide truthfulness assessments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jimaging10050108,

title = {Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification},

author = {Giulia Lucrezia Baroni and Laura Rasotto and Kevin Roitero and Angelica Tulisso and Carla Di Loreto and Vincenzo Della Mea},

editor = {MDPI},

url = {https://www.mdpi.com/2313-433X/10/5/108},

doi = {10.3390/jimaging10050108},

issn = {2313-433X},

year  = {2024},

date = {2024-05-28},

urldate = {2024-04-30},

journal = {Journal of Imaging},

volume = {10},

number = {5},

abstract = {This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fiorin2024,

title = {Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets},

author = {Alessio Fiorin and Carlos López Pablo and Marylène Lejeune and Ameer Hamza Siraj and Vincenzo Della Mea},

url = {https://doi.org/10.1007/s10278-024-01043-8},

doi = {10.1007/s10278-024-01043-8},

issn = {2948-2933},

year  = {2024},

date = {2024-05-01},

journal = {Journal of Imaging Informatics in Medicine},

abstract = {The field of immunology is fundamental to our understanding of the intricate dynamics of the tumor microenvironment. In particular, tumor-infiltrating lymphocyte (TIL) assessment emerges as essential aspect in breast cancer cases. To gain comprehensive insights, the quantification of TILs through computer-assisted pathology (CAP) tools has become a prominent approach, employing advanced artificial intelligence models based on deep learning techniques. The successful recognition of TILs requires the models to be trained, a process that demands access to annotated datasets. Unfortunately, this task is hampered not only by the scarcity of such datasets, but also by the time-consuming nature of the annotation phase required to create them. Our review endeavors to examine publicly accessible datasets pertaining to the TIL domain and thereby become a valuable resource for the TIL community. The overall aim of the present review is thus to make it easier to train and validate current and upcoming CAP tools for TIL assessment by inspecting and evaluating existing publicly available online datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SOPRANO2024103672,

title = {Cognitive Biases in Fact-Checking and Their Countermeasures: A Review},

author = {Michael Soprano and Kevin Roitero and David La Barbera and Davide Ceolin and Damiano Spina and Gianluca Demartini and Stefano Mizzaro},

url = {https://www.sciencedirect.com/science/article/pii/S0306457324000323},

doi = {10.1016/j.ipm.2024.103672},

issn = {0306-4573},

year  = {2024},

date = {2024-02-11},

urldate = {2024-01-01},

journal = {Information Processing & Management},

volume = {61},

number = {3},

pages = {103672},

abstract = {The increase of the amount of misinformation spread every day online is a huge threat to the society. Organizations and researchers are working to contrast this misinformation plague. In this setting, human assessors are indispensable to correctly identify, assess and/or revise the truthfulness of information items, i.e., to perform the fact-checking activity. Assessors, as humans, are subject to systematic errors that might interfere with their fact-checking activity. Among such errors, cognitive biases are those due to the limits of human cognition. Although biases help to minimize the cost of making mistakes, they skew assessments away from an objective perception of information. Cognitive biases, hence, are particularly frequent and critical, and can cause errors that have a huge potential impact as they propagate not only in the community, but also in the datasets used to train automatic and semi-automatic machine learning models to fight misinformation. In this work, we present a review of the cognitive biases which might occur during the fact-checking process. In more detail, inspired by PRISMA – a methodology used for systematic literature reviews – we manually derive a list of 221 cognitive biases that may affect human assessors. Then, we select the 39 biases that might manifest during the fact-checking process, we group them into categories, and we provide a description. Finally, we present a list of 11 countermeasures that can be adopted by researchers, practitioners, and organizations to limit the effect of the identified cognitive biases on the fact-checking activity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DaRos2024,

title = {Supporting Fair and Efficient Emergency Medical Services in a Large Heterogeneous Region},

author = {Francesca Da Ros and Luca Di Gaspero and Kevin Roitero and David La Barbera and Stefano Mizzaro and Vincenzo Della Mea and Francesca Valent and Laura Deroma},

url = {https://doi.org/10.1007/s41666-023-00154-1},

doi = {10.1007/s41666-023-00154-1},

issn = {2509-498X},

year  = {2024},

date = {2024-01-09},

urldate = {2024-01-09},

journal = {Journal of Healthcare Informatics Research},

abstract = {Emergency Medical Services (EMS) are crucial in delivering timely and effective medical care to patients in need. However, the complex and dynamic nature of operations poses challenges for decision-making processes at strategic, tactical, and operational levels. This paper proposes an action-driven strategy for EMS management, employing a multi-objective optimizer and a simulator to evaluate potential outcomes of decisions. The approach combines historical data with dynamic simulations and multi-objective optimization techniques to inform decision-makers and improve the overall performance of the system. The research focuses on the Friuli Venezia Giulia region in north-eastern Italy. The region encompasses various landscapes and demographic situations that challenge fairness and equity in service access. Similar challenges are faced in other regions with comparable characteristics. The Decision Support System developed in this work accurately models the real-world system and provides valuable feedback and suggestions to EMS professionals, enabling them to make informed decisions and enhance the efficiency and fairness of the system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3611641,

title = {Data Bias Management},

author = {Gianluca Demartini and Kevin Roitero and Stefano Mizzaro},

url = {https://doi.org/10.1145/3611641},

doi = {10.1145/3611641},

issn = {0001-0782},

year  = {2023},

date = {2023-12-21},

urldate = {2023-12-01},

journal = {Commun. ACM},

volume = {67},

number = {1},

pages = {28–32},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Envisioning a unique approach toward bias and fairness research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3597201,

title = {How Many Crowd Workers Do I Need? On Statistical Power When Crowdsourcing Relevance Judgments},

author = {Kevin Roitero and David La Barbera and Michael Soprano and Gianluca Demartini and Stefano Mizzaro and Tetsuya Sakai},

doi = {10.1145/3597201},

issn = {1046-8188},

year  = {2023},

date = {2023-08-18},

urldate = {2023-01-01},

journal = {ACM Transactions on Information Systems},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {To scale the size of Information Retrieval collections, crowdsourcing has become a common way to collect relevance judgments at scale. Crowdsourcing experiments usually employ 100-10,000 workers, but such a number is often decided in a heuristic way. The downside is that the resulting dataset does not have any guarantee of meeting predefined statistical requirements as, for example, have enough statistical power to be able to distinguish in a statistically significant way between the relevance of two documents. We propose a methodology adapted from literature on sound topic set size design, based on t-test and ANOVA, which aims at guaranteeing the resulting dataset to meet a predefined set of statistical requirements. We validate our approach on several public datasets. Our results show that we can reliably estimate the recommended number of workers needed to achieve statistical power, and that such estimation is dependent on the topic, while the effect of the relevance scale is limited. Furthermore, we found that such estimation is dependent on worker features such as agreement. Finally, we describe a set of practical estimation strategies that can be used to estimate the worker set size, and we also provide results on the estimation of document set sizes.},

note = {Journal Ranks: Journal Citation Reports (JCR) Q1 (2021), Scimago (SJR) Q1 (2021)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Xie2023b,

title = {The Dark Side of Recruitment in Crowdsourcing: Ethics and Transparency in Micro-Task Marketplaces},

author = {Haoyu Xie and Eddy Maddalena and Rehab Qarout and Alessandro Checco},

url = {https://doi.org/10.1007/s10606-023-09464-9},

doi = {10.1007/s10606-023-09464-9},

issn = {1573-7551},

year  = {2023},

date = {2023-07-28},

urldate = {2023-09-01},

journal = {Computer Supported Cooperative Work (CSCW)},

volume = {32},

number = {3},

pages = {439-474},

abstract = {Micro-task crowdsourcing marketplaces like Figure Eight (F8) connect a large pool of workers to employers through a single online platform, by aggregating multiple crowdsourcing platforms (channels) under a unique system. This paper investigates the F8 channels' demographic distribution and reward schemes by analysing more than 53k crowdsourcing tasks over four years, collecting survey data and scraping marketplace metadata. We reveal an heterogeneous per-channel demographic distribution, and an opaque channel commission scheme, that varies over time and is not communicated to the employer when launching a task: workers often will receive a smaller payment than expected by the employer. In addition, the impact of channel commission schemes on the relationship between requesters and crowdworkers is explored. These observations uncover important issues on ethics, reliability and transparency of crowdsourced experiment when using this kind of marketplaces, especially for academic research.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3604940,

title = {Qrowdsmith: Enhancing Paid Microtask Crowdsourcing with Gamification and Furtherance Incentives},

author = {Eddy Maddalena and Luis-Daniel Ibáñez and Neal Reeves and Elena Simperl},

url = {https://doi.org/10.1145/3604940},

doi = {10.1145/3604940},

issn = {2157-6904},

year  = {2023},

date = {2023-06-01},

journal = {ACM Trans. Intell. Syst. Technol.},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Microtask crowdsourcing platforms are social intelligence systems in which volunteers, called crowdworkers, complete small, repetitive tasks in return for a small fee. Beyond payments, task requesters are considering non-monetary incentives such as points, badges and other gamified elements to increase performance and improve crowdworker experience. In this paper, we present Qrowdsmith, a platform for gamifying microtask crowdsourcing. To design the system, we explore empirically a range of gamified and financial incentives and analyse their impact on how efficient, effective, and reliable the results are. To maintain participation over time and save costs, we propose furtherance incentives, which are offered to crowdworkers to encourage additional contributions in addition to the fee agreed upfront. In a series of controlled experiments we find that while gamification can work as furtherance incentives, it impacts negatively on crowdworkers performance, both in terms of the quantity and quality of work, as compared to a baseline where they can continue to contribute voluntarily. Gamified incentives are also less effective than paid bonus equivalents. Our results contribute to the understanding of how best to encourage engagement in microtask crowdsourcing activities, and design better crowd intelligence systems.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Roitero2023,

title = {Automated ICF Coding of Rehabilitation Notes for Low-Resource Languages via Continual Training of Language Models},

author = {Kevin Roitero and Andrea Martinuzzi and Maria Teresa Armellin and Gabriella Paparella and Alberto Maniero and Vincenzo Della Mea},

editor = {IOS Press},

doi = {10.3233/SHTI230262},

issn = {1879-8365},

year  = {2023},

date = {2023-05-18},

urldate = {2023-05-18},

journal = {Studies in Health Technology and Informatics},

volume = {302},

pages = {763–767},

publisher = {IOS Press},

abstract = {The coding of medical documents and in particular of rehabilitation notes using the International Classification of Functioning, Disability and Health (ICF) is a difficult task showing low agreement among experts. Such difficulty is mainly caused by the specific terminology that needs to be used for the task. In this paper, we address the task developing a model based on a large language model, BERT. By leveraging continual training of such a model using ICF textual descriptions, we are able to effectively encode rehabilitation notes expressed in Italian, an under-resourced language.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10129181,

title = {Detection of Wastewater Pollution Through Natural Language Generation With a Low-Cost Sensing Platform},

author = {Kevin Roitero and Beatrice Portelli and Giuseppe Serra and Vincenzo Della Mea and Stefano Mizzaro and Gianni Cerro and Michele Vitelli and Mario Molinara},

doi = {10.1109/ACCESS.2023.3277535},

issn = {2169-3536},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE Access},

volume = {11},

pages = {50272–50284},

abstract = {The detection of contaminants in several environments (e.g., air, water, sewage systems) is of paramount importance to protect people and predict possible dangerous circumstances. Most works do this using classical Machine Learning tools that act on the acquired measurement data. This paper introduces two main elements: a low-cost platform to acquire, pre-process, and transmit data to classify contaminants in wastewater; and a novel classification approach to classify contaminants in wastewater, based on deep learning and the transformation of raw sensor data into natural language metadata. The proposed solution presents clear advantages against state-of-the-art systems in terms of higher effectiveness and reasonable efficiency. The main disadvantage of the proposed approach is that it relies on knowing the injection time, i.e., the instant in time when the contaminant is injected into the wastewater. For this reason, the developed system also includes a finite state machine tool able to infer the exact time instant when the substance is injected. The entire system is presented and discussed in detail. Furthermore, several variants of the proposed processing technique are also presented to assess the sensitivity to the number of used samples and the corresponding promptness/computational burden of the system. The lowest accuracy obtained by our technique is 91.4%, which is significantly higher than the 81.0% accuracy reached by the best baseline method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AMIGO2023103115,

title = {A unifying and general account of fairness measurement in recommender systems},

author = {Enrique Amigó and Yashar Deldjoo and Stefano Mizzaro and Alejandro Bellogín},

url = {https://www.sciencedirect.com/science/article/pii/S0306457322002163},

doi = {https://doi.org/10.1016/j.ipm.2022.103115},

issn = {0306-4573},

year  = {2023},

date = {2023-01-01},

journal = {Information Processing & Management},

volume = {60},

number = {1},

pages = {103115},

abstract = {Fairness is fundamental to all information access systems, including recommender systems. However, the landscape of fairness definition and measurement is quite scattered with many competing definitions that are partial and often incompatible. There is much work focusing on specific – and different – notions of fairness and there exist dozens of metrics of fairness in the literature, many of them redundant and most of them incompatible. In contrast, to our knowledge, there is no formal framework that covers all possible variants of fairness and allows developers to choose the most appropriate variant depending on the particular scenario. In this paper, we aim to define a general, flexible, and parameterizable framework that covers a whole range of fairness evaluation possibilities. Instead of modeling the metrics based on an abstract definition of fairness, the distinctive feature of this study compared to the current state of the art is that we start from the metrics applied in the literature to obtain a unified model by generalization. The framework is grounded on a general work hypothesis: interpreting the space of users and items as a probabilistic sample space, two fundamental measures in information theory (Kullback–Leibler Divergence and Mutual Information) can capture the majority of possible scenarios for measuring fairness on recommender system outputs. In addition, earlier research on fairness in recommender systems could be viewed as single-sided, trying to optimize some form of equity across either user groups or provider/procurer groups, without considering the user/item space in conjunction, thereby overlooking/disregarding the interplay between user and item groups. Instead, our framework includes the notion of statistical independence between user and item groups. We finally validate our approach experimentally on both synthetic and real data according to a wide range of state-of-the-art recommendation algorithms and real-world data sets, showing that with our framework we can measure fairness in a general, uniform, and meaningful way.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9528028,

title = {What is My Problem? Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement Theory},

author = {Enrique Amigó and Julio Gonzalo and Stefano Mizzaro},

doi = {10.1109/TKDE.2021.3109823},

year  = {2023},

date = {2023-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {35},

number = {2},

pages = {2147–2157},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3546917,

title = {A Neural Model to Jointly Predict and Explain Truthfulness of Statements},

author = {Erik Brand and Kevin Roitero and Michael Soprano and Afshin Rahimi and Gianluca Demartini},

url = {https://doi.org/10.1145/3546917},

doi = {10.1145/3546917},

issn = {1936-1955},

year  = {2022},

date = {2022-05-01},

journal = {J. Data and Information Quality},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Automated fact-checking (AFC) systems exist to combat disinformation, however their complexity usually makes them opaque to the end user, making it difficult to foster trust in the system. In this paper, we introduce the E-BART model with the hope of making progress on this front. E-BART is able to provide a veracity prediction for a claim, and jointly generate a human-readable explanation for this decision. We show that E-BART is competitive with the state-of-the-art on the e-FEVER and e-SNLI tasks. In addition, we validate the joint-prediction architecture by showing 1) that generating explanations does not significantly impede the model from performing well in its main task of veracity prediction, and 2) that predicted veracity and explanations are more internally coherent when generated jointly than separately. We also calibrate the E-BART model, allowing the output of the final model be correctly interpreted as the confidence of correctness. Finally, we also conduct and extensive human evaluation on the impact of generated explanations and observe that: explanations increase human ability to spot misinformation and make people more skeptical about claims, and explanations generated by E-BART are competitive with ground truth explanations.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1145/3546916,

title = {Combining Human and Machine Confidence in Truthfulness Assessment},

author = {Yunke Qu and David La Barbera and Kevin Roitero and Stefano Mizzaro and Damiano Spina and Gianluca Demartini},

url = {https://doi.org/10.1145/3546916},

doi = {10.1145/3546916},

issn = {1936-1955},

year  = {2022},

date = {2022-05-01},

journal = {J. Data and Information Quality},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Automatically detecting online misinformation at scale is a challenging and interdisciplinary problem. Deciding what is to be considered truthful information is sometimes controversial and difficult also for educated experts. As the scale of the problem increases, human-in-the-loop approaches to truthfulness that combine both the scalability of machine learning (ML) and the accuracy of human contributions have been considered. In this work we look at the potential to automatically combine machine-based systems with human-based systems. The former exploit supervised ML approaches; the latter involve either crowd workers (i.e., human non-experts) or human experts. Since both ML and crowdsourcing approaches can produce a score indicating the level of confidence on their truthfulness judgments (either algorithmic or self-reported, respectively), we address the question of whether it is feasible to make use of such confidence scores to effectively and efficiently combine three approaches: (i) machine-based methods; (ii) crowd workers, and (iii) human experts. The three approaches differ significantly as they range from available, cheap, fast, scalable, but less accurate to scarce, expensive, slow, not scalable, but highly accurate.},

note = {Just Accepted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CESCHIA2022105995,

title = {Task design in complex crowdsourcing experiments: Item assignment optimization},

author = {Sara Ceschia and Kevin Roitero and Gianluca Demartini and Stefano Mizzaro and Luca Di Gaspero and Andrea Schaerf},

url = {https://www.sciencedirect.com/science/article/pii/S0305054822002295},

doi = {https://doi.org/10.1016/j.cor.2022.105995},

issn = {0305-0548},

year  = {2022},

date = {2022-01-01},

journal = {Computers & Operations Research},

pages = {105995},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{CEOLIN2022102107,

title = {Transparent Assessment of Information Quality of Online Reviews Using Formal Argumentation Theory},

author = {Davide Ceolin and Giuseppe Primiero and Michael Soprano and Jan Wielemaker},

doi = {10.1016/j.is.2022.102107},

issn = {0306-4379},

year  = {2022},

date = {2022-01-01},

journal = {Information Systems},

volume = {110},

pages = {102107},

abstract = {Review scores collect users’ opinions in a simple and intuitive manner. However, review scores are also easily manipulable, hence they are often accompanied by explanations. A substantial amount of research has been devoted to ascertaining the quality of reviews, to identify the most useful and authentic scores through explanation analysis. In this paper, we advance the state of the art in review quality analysis. We introduce a rating system to identify review arguments and to define an appropriate weighted semantics through formal argumentation theory. We introduce an algorithm to construct a corresponding graph, based on a selection of weighted arguments, their semantic distance, and the supported ratings. We also provide an algorithm to identify the model of such an argumentation graph, maximizing the overall weight of the admitted nodes and edges. We evaluate these contributions on the Amazon review dataset by McAuley et al. (2015), by comparing the results of our argumentation assessment with the upvotes received by the reviews. Also, we deepen the evaluation by crowdsourcing a multidimensional assessment of reviews and comparing it to the argumentation assessment. Lastly, we perform a user study to evaluate the explainability of our method, i.e., to test whether the automated method we use to assess reviews is understandable by humans. Our method achieves two goals: (1) it identifies reviews that are considered useful, comprehensible, and complete by online users, and does so in an unsupervised manner, and (2) it provides an explanation of quality assessments.},

note = {Journal Ranks: Journal Citation Reports (JCR) Q2 (2021), Scimago (SJR) Q1 (2021)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{journal-paper-puc-2021,

title = {Can The Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation About COVID-19},

author = {Kevin Roitero and Michael Soprano and Beatrice Portelli and Massimiliano De Luise and Damiano Spina and Vincenzo Della Mea and Giuseppe Serra and Stefano Mizzaro and Gianluca Demartini},

url = {https://doi.org/10.1007/s00779-021-01604-6},

doi = {10.1007/s00779-021-01604-6},

issn = {1617-4917},

year  = {2021},

date = {2021-01-01},

journal = {Personal and Ubiquitous Computing},

abstract = {Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of non-expert is exploited. We study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done. In our experiments, crowd workers are asked to assess the truthfulness of statements, and to provide evidence for the assessments. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we report results on workers' behavior, agreement among workers, effect of aggregation functions, of scales transformations, and of workers background and bias. We perform a longitudinal study by re-launching the task multiple times with both novice and experienced workers, deriving important insights on how the behavior and quality change over time. Our results show that workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e.g., source of information, behavior) impact the quality of the data. The longitudinal study demonstrates that the time-span has a major effect on the quality of the judgments, for both novice and experienced workers. Finally, we provide an extensive failure analysis of the statements misjudged by the crowd-workers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{journal-paper-ipm-2021,

title = {The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale},

author = {Michael Soprano and Kevin Roitero and David La Barbera and Davide Ceolin and Damiano Spina and Stefano Mizzaro and Gianluca Demartini},

url = {https://www.sciencedirect.com/science/article/pii/S0306457321001941},

doi = {https://doi.org/10.1016/j.ipm.2021.102710},

issn = {0306-4573},

year  = {2021},

date = {2021-01-01},

journal = {Information Processing & Management},

volume = {58},

number = {6},

pages = {102710},

abstract = {Recent work has demonstrated the viability of using crowdsourcing as a tool for evaluating the truthfulness of public statements. Under certain conditions such as: (1) having a balanced set of workers with different backgrounds and cognitive abilities; (2) using an adequate set of mechanisms to control the quality of the collected data; and (3) using a coarse grained assessment scale, the crowd can provide reliable identification of fake news. However, fake news are a subtle matter: statements can be just biased (“cherrypicked”), imprecise, wrong, etc. and the unidimensional truth scale used in existing work cannot account for such differences. In this paper we propose a multidimensional notion of truthfulness and we ask the crowd workers to assess seven different dimensions of truthfulness selected based on existing literature: Correctness, Neutrality, Comprehensibility, Precision, Completeness, Speaker’s Trustworthiness, and Informativeness. We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments. A comprehensive analysis of crowdsourced judgments shows that: (1) the crowdsourced assessments are reliable when compared to an expert-provided gold standard; (2) the proposed dimensions of truthfulness capture independent pieces of information; (3) the crowdsourcing task can be easily learned by the workers; and (4) the resulting assessments provide a useful basis for a more complete estimation of statement truthfulness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9628010,

title = {DiLBERT: Cheap Embeddings for Disease Related Medical NLP},

author = {Kevin Roitero and Beatrice Portelli and Mihai Horia Popescu and Vincenzo Della Mea},

doi = {10.1109/ACCESS.2021.3131386},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

volume = {9},

pages = {159714-159723},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2110-13504,

title = {Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal},

author = {Gianluca Demartini and Kevin Roitero and Stefano Mizzaro},

url = {https://arxiv.org/abs/2110.13504},

year  = {2021},

date = {2021-01-01},

journal = {CoRR},

volume = {abs/2110.13504},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{labarberaher2,

title = {Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning},

author = {David La Barbera and António Polónia and Kevin Roitero and Eduardo Conde-Sousa and Vincenzo Della Mea},

doi = {10.3390/jimaging6090082},

issn = {2313-433X},

year  = {2020},

date = {2020-08-23},

urldate = {2020-08-23},

journal = {Journal of Imaging},

volume = {6},

number = {9},

abstract = {Breast cancer is the most frequently diagnosed cancer in woman. The correct identification of the HER2 receptor is a matter of major importance when dealing with breast cancer: an over-expression of HER2 is associated with aggressive clinical behaviour; moreover, HER2 targeted therapy results in a significant improvement in the overall survival rate. In this work, we employ a pipeline based on a cascade of deep neural network classifiers and multi-instance learning to detect the presence of HER2 from Haematoxylin-Eosin slides, which partly mimics the pathologist’s behaviour by first recognizing cancer and then evaluating HER2. Our results show that the proposed system presents a good overall effectiveness. Furthermore, the system design is prone to further improvements that can be easily deployed in order to increase the effectiveness score.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ir/RoiteroCSSM20,

title = {Fewer topics? A million topics? Both?! On topics subsets in test 

 collections},

author = {Kevin Roitero and Shane J Culpepper and Mark Sanderson and Falk Scholer and Stefano Mizzaro},

url = {https://doi.org/10.1007/s10791-019-09357-w},

doi = {10.1007/s10791-019-09357-w},

year  = {2020},

date = {2020-01-01},

journal = {Inf. Retr. J.},

volume = {23},

number = {1},

pages = {49--85},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ROITERO2020102149,

title = {Effectiveness evaluation without human relevance judgments: A systematic analysis of existing methods and of their combinations},

author = {Kevin Roitero and Andrea Brunello and Giuseppe Serra and Stefano Mizzaro},

url = {http://www.sciencedirect.com/science/article/pii/S030645731930192X},

doi = {https://doi.org/10.1016/j.ipm.2019.102149},

issn = {0306-4573},

year  = {2020},

date = {2020-01-01},

journal = {Information Processing & Management},

volume = {57},

number = {2},

pages = {102149},

abstract = {In test collection based evaluation of retrieval effectiveness, it has been suggested to completely avoid using human relevance judgments. Although several methods have been proposed, their accuracy is still limited. In this paper we present two overall contributions. First, we provide a systematic comparison of all the most widely adopted previous approaches on a large set of 14 TREC collections. We aim at analyzing the methods in a homogeneous and complete way, in terms of the accuracy measures used as well as in terms of the datasets selected, showing that considerably different results may be achieved considering different methods, datasets, and measures. Second, we study the combination of such methods, which, to the best of our knowledge, has not been investigated so far. Our experimental results show that simple combination strategies based on data fusion techniques are usually not effective and even harmful. However, some more sophisticated solutions, based on machine learning, are indeed effective and often outperform all individual methods. Moreover, they are more stable, as they show a smaller variation across datasets. Our results have the practical implication that, when trying to automatically evaluate retrieval effectiveness, researchers should not use a single method, but a (machine-learning based) combination of them.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}