Publications

153 entries « ‹ 1 of 7 › »

2024
153.	Blazevic, Midhad; Sina, Lennart B.; Secco, Cristian A.; Siegel, Melanie; Nazemi, Kawa Real-Time Ideation Analyzer and Information Recommender Journal Article In: Electronics, vol. 13, no. 9, 2024, ISSN: 2079-9292. Abstract \| Links \| BibTeX \| Tags: @article{electronics13091761, title = {Real-Time Ideation Analyzer and Information Recommender}, author = {Blazevic, Midhad and Sina, Lennart B. and Secco, Cristian A. and Siegel, Melanie and Nazemi, Kawa}, url = {https://www.mdpi.com/2079-9292/13/9/1761}, doi = {10.3390/electronics13091761}, issn = {2079-9292}, year = {2024}, date = {2024-05-15}, urldate = {2024-05-15}, journal = {Electronics}, volume = {13}, number = {9}, abstract = {The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas. Close https://www.mdpi.com/2079-9292/13/9/1761 doi:10.3390/electronics13091761 Close
152.	Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Guided Visual Analytics-A Visual Analytics Guidance Approach for Systematic Reviews in Research Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery, pp. 319–343, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-46549-9. Abstract \| Links \| BibTeX \| Tags: @inbook{SSM*24, title = {Guided Visual Analytics-A Visual Analytics Guidance Approach for Systematic Reviews in Research}, author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, url = {https://doi.org/10.1007/978-3-031-46549-9_11}, doi = {10.1007/978-3-031-46549-9_11}, isbn = {978-3-031-46549-9}, year = {2024}, date = {2024-01-01}, urldate = {2024-01-01}, booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery}, pages = {319--343}, publisher = {Springer Nature Switzerland}, address = {Cham}, abstract = {Visual Analytics systems are often complex expert systems that require high expertise. The simplification of the interaction with such systems in order to make them usable for novices is one subject of current research works. One way to ease the user's interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a stepwise guidance approach for Visual Analytics. For that, we use the domain of literature search and exploration exemplary. The underlying system allows researchers to visually search and explore scientific publications and automatically generate systematic review protocols. To accomplish this, we present a stepwise visual guidance system approach that combines automatic steps and manual user validation to unify the systematic literature review (SLR) creation process. Based on a design study we conducted, we present our proposed AI-based assistant (MAIA) that assists users in the various steps required to create systematic literature reviews. According to the PRISMA statement, we describe the process of SLR creation exemplary and present the different screens that guide the user through SLR creation.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Close Visual Analytics systems are often complex expert systems that require high expertise. The simplification of the interaction with such systems in order to make them usable for novices is one subject of current research works. One way to ease the user's interaction with the systems is through guidance approaches. Guidance approaches aim to support the user while working with the system by providing targeted assistance. We present in this work a stepwise guidance approach for Visual Analytics. For that, we use the domain of literature search and exploration exemplary. The underlying system allows researchers to visually search and explore scientific publications and automatically generate systematic review protocols. To accomplish this, we present a stepwise visual guidance system approach that combines automatic steps and manual user validation to unify the systematic literature review (SLR) creation process. Based on a design study we conducted, we present our proposed AI-based assistant (MAIA) that assists users in the various steps required to create systematic literature reviews. According to the PRISMA statement, we describe the process of SLR creation exemplary and present the different screens that guide the user through SLR creation. Close https://doi.org/10.1007/978-3-031-46549-9_11 doi:10.1007/978-3-031-46549-9_11 Close
151.	Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Similarity in Visual Analytics-A Visual Analytics Approach for Finding Similar Publications Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery, pp. 443–468, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-46549-9. Abstract \| Links \| BibTeX \| Tags: @inbook{Blazevic2024, title = {Similarity in Visual Analytics-A Visual Analytics Approach for Finding Similar Publications}, author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, url = {https://doi.org/10.1007/978-3-031-46549-9_16}, doi = {10.1007/978-3-031-46549-9_16}, isbn = {978-3-031-46549-9}, year = {2024}, date = {2024-01-01}, urldate = {2024-01-01}, booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery}, pages = {443--468}, publisher = {Springer Nature Switzerland}, address = {Cham}, abstract = {Recent studies show that the search for relevant publications requires researchers to invest a significant amount of time and work; some studies even underline that this task requires the most time and work out of all the tasks in the entire research process. To reduce the search time in the research process, we propose a visual analytics approach that combines models and methods of natural language process, machine learning, similarity measures, and interactive visual representations. The proposed approach is based on our previous works and enhances those with additional automatic assistance during the entire search process. Our visual analytics approach facilitates the presentation of large amounts of relevant results similar to the identified topics of interest and tailored to the needs of researchers during their research process. The proposed method enables annotation in the context of exploration, allowing researchers to quickly find and bookmark relevant publications during exploration and, by doing so, improve the publication recommendations. We analyze the annotations researchers make throughout their research journey to identify the topics of interest and use them as input for our learning and measurement methods. By utilizing researchers' commonly observed annotation and exploration behavior, our approach counters information overload by generating labeled vectors of interest and providing similar publications.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Close Recent studies show that the search for relevant publications requires researchers to invest a significant amount of time and work; some studies even underline that this task requires the most time and work out of all the tasks in the entire research process. To reduce the search time in the research process, we propose a visual analytics approach that combines models and methods of natural language process, machine learning, similarity measures, and interactive visual representations. The proposed approach is based on our previous works and enhances those with additional automatic assistance during the entire search process. Our visual analytics approach facilitates the presentation of large amounts of relevant results similar to the identified topics of interest and tailored to the needs of researchers during their research process. The proposed method enables annotation in the context of exploration, allowing researchers to quickly find and bookmark relevant publications during exploration and, by doing so, improve the publication recommendations. We analyze the annotations researchers make throughout their research journey to identify the topics of interest and use them as input for our learning and measurement methods. By utilizing researchers' commonly observed annotation and exploration behavior, our approach counters information overload by generating labeled vectors of interest and providing similar publications. Close https://doi.org/10.1007/978-3-031-46549-9_16 doi:10.1007/978-3-031-46549-9_16 Close
150.	Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science Book Chapter In: Boris Kovalerchuk; Kawa Nazemi; Răzvan Andonie; Nuno Datia; Ebad Banissi (Ed.): Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery, pp. 345–373, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-46549-9. Abstract \| Links \| BibTeX \| Tags: @inbook{Blazevic2024b, title = {Integrating Machine Learning in Visual Analytics for Supporting Collaboration in Science}, author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi}, editor = {Boris Kovalerchuk and Kawa Nazemi and Răzvan Andonie and Nuno Datia and Ebad Banissi}, url = {https://doi.org/10.1007/978-3-031-46549-9_12}, doi = {10.1007/978-3-031-46549-9_12}, isbn = {978-3-031-46549-9}, year = {2024}, date = {2024-01-01}, booktitle = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery}, pages = {345--373}, publisher = {Springer Nature Switzerland}, address = {Cham}, abstract = {Studies have shown rising interest in scientific collaborations throughout the past decades. The challenges throughout various studies show an emerging need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors' publications and showcasing them with citation scores. They neglect the possibility of creating a holistic assistance and collaborative approach that covers the entire scientific research process using adequate intelligence methods. We introduce in this chapter a novel approach to visual collaboration. Our approach covers the entire process of scientific paper writing through real-time visual recommendations. It combines on-the-fly similarity measurements, ideation assistance based on group constellations, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the integration of visual analytics that combines artificial intelligence with interactive visualizations. Thereby the interaction design and the ease of use are essential. Our approach presents a single-source AI-driven visual collaborative research platform for the entire research community.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Close Studies have shown rising interest in scientific collaborations throughout the past decades. The challenges throughout various studies show an emerging need for research and development in methods and systems that utilize artificial intelligence to provide research communities with adequate tools that facilitate and encourage collaborative research. Many platforms focus on listing authors' publications and showcasing them with citation scores. They neglect the possibility of creating a holistic assistance and collaborative approach that covers the entire scientific research process using adequate intelligence methods. We introduce in this chapter a novel approach to visual collaboration. Our approach covers the entire process of scientific paper writing through real-time visual recommendations. It combines on-the-fly similarity measurements, ideation assistance based on group constellations, visual exploration, and stimuli promotion for the different stages of collaborative writing. Our research into collaborative research applications also led us to examine the adverse effects of multitasking and multi-application usage on researchers. These effects on human cognition require the integration of visual analytics that combines artificial intelligence with interactive visualizations. Thereby the interaction design and the ease of use are essential. Our approach presents a single-source AI-driven visual collaborative research platform for the entire research community. Close https://doi.org/10.1007/978-3-031-46549-9_12 doi:10.1007/978-3-031-46549-9_12 Close
149.	Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi (Ed.) Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery Book Springer Nature Switzerland, 2024, ISSN: 1860-9503. Links \| BibTeX \| Tags: @book{2024, title = {Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery}, editor = {Boris Kovalerchuk, Kawa Nazemi, Răzvan Andonie, Nuno Datia, Ebad Banissi}, url = {http://dx.doi.org/10.1007/978-3-031-46549-9}, doi = {10.1007/978-3-031-46549-9}, issn = {1860-9503}, year = {2024}, date = {2024-01-01}, urldate = {2024-01-01}, journal = {Studies in Computational Intelligence}, publisher = {Springer Nature Switzerland}, keywords = {}, pubstate = {published}, tppubtype = {book} } Close http://dx.doi.org/10.1007/978-3-031-46549-9 doi:10.1007/978-3-031-46549-9 Close
2023
148.	Cristian A. Secco; Lennart B. Sina; Midhad Blazevic; Kawa Nazemi Visual Analytics for Forecasting Technological Trends from Text Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV2023), pp. 251-258, IEEE CPS, 2023. Abstract \| Links \| BibTeX \| Tags: @inproceedings{Secco2023, title = {Visual Analytics for Forecasting Technological Trends from Text}, author = {Cristian A. Secco and Lennart B. Sina and Midhad Blazevic and Kawa Nazemi }, doi = {10.1109/IV60283.2023.00051}, year = {2023}, date = {2023-11-30}, urldate = {2023-11-30}, booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)}, pages = {251-258}, publisher = {IEEE CPS}, abstract = {Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel Visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on macro level, micro level, and monitoring topics of interest.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Knowledge of emerging and declining trends and their potential future course is highly relevant in many application domains, particularly in corporate strategy and foresight. The early awareness of trends allows reacting to market, political, and societal changes and challenges at an appropriate time. In our previous works, we presented approaches for the early identification and analysis of emerging trends. Although our previous approaches are detecting emerging trends appropriately, they lack the ability to predict the potential future course of a trend or technology. We present in this work a novel Visual Analytics approach for forecasting emerging trends that combines interactive visualizations with machine learning techniques and statistical approaches to detect, analyze, and predict trends from textual data. We extend our previous work on analyzing technological trends from text and propose an advanced approach that includes forecasting through hybrid techniques consisting of neural networks and established statistical methods. Our approach offers insights from enormous data sets and the potential future course of trends based on their occurrence in textual data. We contribute with a novel approach for identifying and forecasting trends, a hybrid forecasting method to predict trends from text, and interactive visualization techniques on macro level, micro level, and monitoring topics of interest. Close doi:10.1109/IV60283.2023.00051 Close
147.	Kawa Nazemi Artificial Intelligence in Visual Analytics Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award, pp. 230 - 237, IEEE CPS, 2023. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual Tasks @inproceedings{Nazemi2023, title = {Artificial Intelligence in Visual Analytics}, author = {Kawa Nazemi}, doi = {10.1109/IV60283.2023.00048}, year = {2023}, date = {2023-11-30}, urldate = {2023-11-30}, booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023), Best Paper Award}, journal = {Proceedings of the 27th International Conference Information Visualisation (IV2023) - Best Paper Award-}, pages = {230 - 237}, publisher = {IEEE CPS}, abstract = {Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics.}, keywords = {Artificial Intelligence, Visual Analytical Reasoning, Visual analytics, Visual Tasks}, pubstate = {published}, tppubtype = {inproceedings} } Close Visual Analytics that combines automated methods with information visualization has emerged as a powerful approach to analytical reasoning. The integration of artificial intelligence techniques into Visual Analytics has enhanced its capabilities but also presents challenges related to interpretability, explainability, and decision-making processes. Visual Analytics may use artificial intelligence methods to provide enhanced and more powerful analytical reasoning capabilities. Furthermore, Visual Analytics can be used to interpret black-box artificial intelligence models and provide a visual explanation of those models. In this paper, we provide an overview of the state-of-the-art of artificial intelligence techniques used in Visual Analytics, focusing on both explainable artificial intelligence in Visual Analytics and the human knowledge generation process through Visual Analytics. We review explainable artificial intelligence approaches in Visual Analytics and propose a revised Visual Analytics model for Explainable artificial intelligence based on an existing model. We then conduct a screening review of artificial intelligence methods in Visual Analytics from two time periods to highlight recently used artificial intelligence approaches in Visual Analytics. Based on this review, we propose a revised task model for tasks in Visual Analytics. Our contributions include a state-of-the-art review of explainable artificial intelligence in Visual Analytics, a revised model for creating explainable artificial intelligence through Visual Analytics, a screening review of recent artificial intelligence methods in Visual Analytics, and a revised task model for generic tasks in Visual Analytics. Close doi:10.1109/IV60283.2023.00048 Close
146.	Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Visual Analytics for Corporate Foresight - A Conceptual Approach Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV2023), pp. 244-250, IEEE CPS, 2023. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Visual analytics @inproceedings{SinaIV2023, title = {Visual Analytics for Corporate Foresight - A Conceptual Approach}, author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi}, doi = {10.1109/IV60283.2023.00050}, year = {2023}, date = {2023-11-29}, urldate = {2023-11-29}, booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV2023)}, pages = {244-250}, publisher = {IEEE CPS}, abstract = {Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.}, keywords = {Artificial Intelligence, Visual analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data. Close doi:10.1109/IV60283.2023.00050 Close
145.	Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science Inproceedings In: Proceedings of the 27th International Conference Information Visualisation (IV 2023), pp. 259- 267, IEEE CPS, 2023. Abstract \| Links \| BibTeX \| Tags: Artifical Intelligence @inproceedings{blaz2023, title = {Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science}, author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi}, doi = {10.1109/IV60283.2023.00052}, year = {2023}, date = {2023-11-24}, urldate = {2023-11-24}, booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV 2023)}, pages = {259- 267}, publisher = {IEEE CPS}, abstract = {The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.}, keywords = {Artifical Intelligence}, pubstate = {published}, tppubtype = {inproceedings} } Close The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model. Close doi:10.1109/IV60283.2023.00052 Close
144.	Ebad Banissi; Harri Siirtola; Anna Ursyn; João Moura Pires; Nuno Datia; Kawa Nazemi; Boris Kovalerchuk; Razvan Andonie; Minoru Nakayama; Marco Temperini; Filippo Sciarrone; Quang Vinh Nguyen; Mabule Samuel Mabakane; Adrian Rusu; Urska Cvek; Marjan Trutschl; Heimo Mueller; Rita Francese; Fatma Boua-li; Gilles Venturini (Ed.) Proceedings of 2023 27th International Conference Information Visualisation Proceeding 2023, ISBN: 979-8-3503-4161-4. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery @proceedings{Banissi2023, title = {Proceedings of 2023 27th International Conference Information Visualisation}, editor = {Ebad Banissi and Harri Siirtola and Anna Ursyn and João Moura Pires and Nuno Datia and Kawa Nazemi and Boris Kovalerchuk and Razvan Andonie and Minoru Nakayama and Marco Temperini and Filippo Sciarrone and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Urska Cvek and Marjan Trutschl and Heimo Mueller and Rita Francese and Fatma Boua-li and Gilles Venturini }, doi = {10.1109/IV60283.2023.00001}, isbn = {979-8-3503-4161-4}, year = {2023}, date = {2023-11-01}, urldate = {2023-11-01}, issue = {IV2023}, abstract = {Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt.}, keywords = {Artificial Intelligence, Data Analytics, Data Science, Visual analytics, Visual Knowledge Discovery}, pubstate = {published}, tppubtype = {proceedings} } Close Do aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era? Does the infrastructure of any information-dependent society rely on the quality of data, information, and analysis of such entities from past and present and projected future activities and, most importantly, how it is intended to be applied? Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are state-of-the-art developments that effectively enhance understanding of these well-established drivers. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From raw data to knowledge, processing the relationship between these phases has added new impetus to understanding and communicating these. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to knowledge discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains to simply storytelling through data. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the processes, from raw data to the knowledge acquisition stage. But there is a new twist: fast-developing generative AI with ever-increasing access to data outsmarting humans in decision-making. A new evolutionary step in the human journey, no doubt. Close doi:10.1109/IV60283.2023.00001 Close
143.	Lennart B. Sina; Cristian A. Secco; Midhad Blazevic; Kawa Nazemi Hybrid Forecasting Methods - A Systematic Review Journal Article In: Electronics, vol. 12, no. 7, 2023. Abstract \| Links \| BibTeX \| Tags: hybrid forecsting, PRISMA study @article{SSB*23, title = {Hybrid Forecasting Methods - A Systematic Review}, author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi}, url = {https://www.mdpi.com/2079-9292/12/9/2019}, doi = {10.3390/electronics12092019}, year = {2023}, date = {2023-04-27}, urldate = {2023-04-27}, journal = {Electronics}, volume = {12}, number = {7}, abstract = {Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.}, keywords = {hybrid forecsting, PRISMA study}, pubstate = {published}, tppubtype = {article} } Close Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization. Close https://www.mdpi.com/2079-9292/12/9/2019 doi:10.3390/electronics12092019 Close
142.	Midhad Blazevic; Lennart B. Sina; Cristian A. Secco; Kawa Nazemi Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System Journal Article In: Electronics, vol. 12, no. 7, 2023, ISSN: 2079-9292. Abstract \| Links \| BibTeX \| Tags: latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling @article{electronics12071699, title = {Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System}, author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi}, url = {https://www.mdpi.com/2079-9292/12/7/1699}, doi = {10.3390/electronics12071699}, issn = {2079-9292}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, journal = {Electronics}, volume = {12}, number = {7}, abstract = {Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.}, keywords = {latex editor, publication recommendations, recommendation systems, similarity algorithms, topics modeling}, pubstate = {published}, tppubtype = {article} } Close Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities. Close https://www.mdpi.com/2079-9292/12/7/1699 doi:10.3390/electronics12071699 Close
141.	Mina Schütz Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations Inproceedings In: Jaap Kamps; Lorraine Goeuriot; Fabio Crestani; Maria Maistro; Hideo Joho; Brian Davis; Cathal Gurrin; Udo Kruschwitz; Annalina Caputo (Ed.): Advances in Information Retrieval, pp. 468–475, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-28241-6. Abstract \| Links \| BibTeX \| Tags: @inproceedings{Sc23, title = {Disinformation Detection: Knowledge Infusion with Transfer Learning and Visualizations}, author = {Mina Schütz}, editor = {Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Udo Kruschwitz and Annalina Caputo}, doi = {10.1007/978-3-031-28241-6_54}, isbn = {978-3-031-28241-6}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, booktitle = {Advances in Information Retrieval}, pages = {468--475}, publisher = {Springer Nature Switzerland}, address = {Cham}, abstract = {The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close The automatic detection of disinformation has gained an increased focus by the research community during the last years. The spread of false information can be an issue for political processes, opinion mining and journalism in general. In this dissertation, I propose a novel approach to gain new insights on the automatic detection of disinformation in textual content. Additionally, I will combine multiple research domains, such as fake news, hate speech, propaganda, and extremism. For this purpose, I will create two novel and annotated datasets in German - a large multi-label dataset for disinformation detection in news articles and a second dataset for hate speech detection in social media posts, which both can be used for training the models in the listed domains via transfer learning. With the usage of transfer learning, an extensive data analysis and classification of the presented domains will be conducted. The classification models will be enhanced during and after training using a knowledge graph, containing additional information (i.e. named entities, relationships, topics), to find explicit insights about the common traits or lines of disinformative arguments in an article. Lastly, methods of explainable artificial intelligence will be combined with visualization techniques to understand the models predictions and present the results in a user-friendly and interactive way. Close doi:10.1007/978-3-031-28241-6_54 Close
140.	Christoph Demus; Mina Schütz; Nadine Probol; Jonas Pitz; Melanie Siegel; Dirk Labudde Hass im Netz -- Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und Analyse Book Chapter In: Thomas-Gabriel Rüdiger; P. Saskia Bayerl (Ed.): Handbuch Cyberkriminologie, pp. 1–32, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-35450-3. Abstract \| Links \| BibTeX \| Tags: @inbook{DSPP23, title = {Hass im Netz -- Aggressivität und Toxizität von Hasskommentaren und Postings, Detektion und Analyse}, author = {Christoph Demus and Mina Schütz and Nadine Probol and Jonas Pitz and Melanie Siegel and Dirk Labudde}, editor = {Thomas-Gabriel Rüdiger and P. Saskia Bayerl}, url = {https://doi.org/10.1007/978-3-658-35450-3_13-1}, doi = {10.1007/978-3-658-35450-3_13-1}, isbn = {978-3-658-35450-3}, year = {2023}, date = {2023-01-01}, booktitle = {Handbuch Cyberkriminologie}, pages = {1--32}, publisher = {Springer Fachmedien Wiesbaden}, address = {Wiesbaden}, abstract = {Hass und aggressives Verhalten im Netz werden immer größere Probleme. Der bisher etablierte Versuch zur Lösung des Problems ist das Löschen von Kommentaren, doch um dem grundlegenden Problem entgegenzuwirken, müssen Ursachen für die Entstehung von Hass im Netz bekämpft werden. In diesem Kapitel wird daher neben Grundlagen der Hatespeechanalyse insbesondere auf Gruppierungen, Informationsfluss und die Ausbreitung von Hass in sozialen Netzwerken eingegangen. Daraus werden dann Maßnahmen zur Bekämpfung der Ursachen von Hass abgeleitet und diskutiert.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Close Hass und aggressives Verhalten im Netz werden immer größere Probleme. Der bisher etablierte Versuch zur Lösung des Problems ist das Löschen von Kommentaren, doch um dem grundlegenden Problem entgegenzuwirken, müssen Ursachen für die Entstehung von Hass im Netz bekämpft werden. In diesem Kapitel wird daher neben Grundlagen der Hatespeechanalyse insbesondere auf Gruppierungen, Informationsfluss und die Ausbreitung von Hass in sozialen Netzwerken eingegangen. Daraus werden dann Maßnahmen zur Bekämpfung der Ursachen von Hass abgeleitet und diskutiert. Close https://doi.org/10.1007/978-3-658-35450-3_13-1 doi:10.1007/978-3-658-35450-3_13-1 Close
2022
139.	Lukas Kaupp; Kawa Nazemi; Bernhard Humm Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories Journal Article In: Electronics, vol. 11, no. 23, 2022, ISSN: 2079-9292. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Case Study, Data Analytics, Data Science, Data Visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics @article{electronics11233942, title = {Evaluation of the Flourish Dashboard for Context-Aware Fault Diagnosis in Industry 4.0 Smart Factories}, author = { Lukas Kaupp and Kawa Nazemi and Bernhard Humm}, editor = {Kawa Nazemi and Egils Ginters and Michael Bazant}, doi = {10.3390/electronics11233942}, issn = {2079-9292}, year = {2022}, date = {2022-11-01}, urldate = {2022-11-01}, journal = {Electronics}, volume = {11}, number = {23}, abstract = {Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions.}, keywords = {Artificial Intelligence, Case Study, Data Analytics, Data Science, Data Visualization, Decision Making, Decision Support Systems, Evaluation, smart factory, Smart manufacturing, Visual analytics}, pubstate = {published}, tppubtype = {article} } Close Cyber-physical systems become more complex, therewith production lines become more complex in the smart factory. Every employed system produces high amounts of data with unknown dependencies and relationships, making incident reasoning difficult. Context-aware fault diagnosis can unveil such relationships on different levels. A fault diagnosis application becomes context-aware when the current production situation is used in the reasoning process. We have already published TAOISM, a visual analytics model defining the context-aware fault diagnosis process for the Industry 4.0 domain. In this article, we propose the Flourish dashboard for context-aware fault diagnosis. The eponymous visualization Flourish is a first implementation of a context-displaying visualization for context-aware fault diagnosis in an Industry 4.0 setting. We conducted a questionnaire and interview-based bilingual evaluation with two user groups based on contextual faults recorded in a production-equal smart factory. Both groups provided qualitative feedback after using the Flourish dashboard. We positively evaluate the Flourish dashboard as an essential part of the context-aware fault diagnosis and discuss our findings, open gaps, and future research directions. Close doi:10.3390/electronics11233942 Close
138.	Lukas Kaupp; Bernhard Humm; Kawa Nazemi; Stephan Simons Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis Journal Article In: Sensors, vol. 22, no. 21, 2022, ISSN: 1424-8220. Abstract \| Links \| BibTeX \| Tags: Artificial Intelligence, Machine Leanring, Machine Learning, smart factory, Smart manufacturing @article{s22218259, title = {Autoencoder-Ensemble-Based Unsupervised Selection of Production-Relevant Variables for Context-Aware Fault Diagnosis}, author = {Lukas Kaupp and Bernhard Humm and Kawa Nazemi and Stephan Simons}, doi = {10.3390/s22218259}, issn = {1424-8220}, year = {2022}, date = {2022-10-01}, urldate = {2022-10-01}, journal = {Sensors}, volume = {22}, number = {21}, abstract = {Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert.}, keywords = {Artificial Intelligence, Machine Leanring, Machine Learning, smart factory, Smart manufacturing}, pubstate = {published}, tppubtype = {article} } Close Smart factories are complex; with the increased complexity of employed cyber-physical systems, the complexity evolves further. Cyber-physical systems produce high amounts of data that are hard to capture and challenging to analyze. Real-time recording of all data is not possible due to limited network capabilities. Limited network capabilities are the reason for a chain of faults introduced via active surveillance during fault diagnosis. These introduced faults may slow down production or lead to an outage of the production line. Here, we present a novel approach to automatically select production-relevant shop floor parameters to decrease the number of surveyed variables and, at the same time, maintain quality in fault diagnosis without overloading the network. We were able to achieve higher throughput, mitigate communication losses and prevent the disruption of factory instructions. Our approach uses an autoencoder ensemble via minority voting to differentiate between normal—always on—variables and production variables that may yield a higher entropy. Our approach has been tested in a production-equal smart factory and was cross-validated by a domain expert. Close doi:10.3390/s22218259 Close
137.	Ebad Banissi, Anna Ursyn, Mark W. McK. Bannatyne, João Moura Pires, Nuno Datia, Kawa Nazemi, Boris Kovalerchuk, Razvan Andonie, Minoru Nakayama, Filippo Sciarrone, Weidong Huang, Quang Vinh Nguyen, Mabule Samuel Mabakane, Adrian Rusu, Marco Temperini, Urska Cvek, Marjan Trutschl, Heimo Mueller, Harri Siirtola, Wai Lok Woo, Rita Francese, Veronica Rossano, Tania Di Mascio, Fatma Bouali, Gilles Venturini, Sebastian Kernbach, Delfina Malandrino, Rocco Zaccagnin, Jian J Zhang, Xiaosong Yang,; Vladimir Geroimenko (Ed.) Proceedings of 2022 26th International Conference Information Visualisation (IV) Proceeding 2022, ISBN: 978-1-6654-9007-8. Abstract \| Links \| BibTeX \| Tags: @proceedings{nokey, title = {Proceedings of 2022 26th International Conference Information Visualisation (IV)}, editor = {Ebad Banissi, Anna Ursyn, Mark W. McK. Bannatyne, João Moura Pires, Nuno Datia, Kawa Nazemi, Boris Kovalerchuk, Razvan Andonie, Minoru Nakayama, Filippo Sciarrone, Weidong Huang, Quang Vinh Nguyen, Mabule Samuel Mabakane, Adrian Rusu, Marco Temperini, Urska Cvek, Marjan Trutschl, Heimo Mueller, Harri Siirtola, Wai Lok Woo, Rita Francese, Veronica Rossano, Tania Di Mascio, Fatma Bouali, Gilles Venturini, Sebastian Kernbach, Delfina Malandrino, Rocco Zaccagnin, Jian J Zhang, Xiaosong Yang, and Vladimir Geroimenko}, doi = {10.1109/IV56949.2022.00001}, isbn = {978-1-6654-9007-8}, year = {2022}, date = {2022-08-08}, urldate = {2022-08-08}, abstract = {Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any information-dependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it is intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are just a few state-of-the-art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the process, from raw data to the knowledge acquisition stage. This collection of papers on this year's information visualization forum, compiled for the 26th conference on the Information Visualization incorporating the following: Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2022 provides the opportunity to resonate with many international and collaborative research projects, lectures, and panel discussions from distinguished speakers that channel the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social Networking impact on the social, cultural, and heritage aspects of life, and learning analysis of today's multifaceted and data-rich environment. Joining us in this search are some 75-plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts that challenge our beliefs and further encourage our adventure of innovation. }, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } Close Most aspects of our lives depend on and are driven by data, information, knowledge, user experience, and cultural influences in the current information era. The infrastructure of any information-dependent society relies on the quality of data, information, and analysis of such entities from past and present and projected future activities in addition and possibly most importantly, how it is intended to be applied. Information Visualization, Analytics, Machine Learning, Artificial Intelligence, and Application domains are just a few state-of-the-art developments that effectively enhance understanding of these driving forces. Several key interdependent variables are emerging that are becoming the focus of scientific activities, such as Information and Data Science. Aspects tightly tie raw data (origin, autonomous capture, classification, incompleteness, impurity, filtering) and data scale to knowledge acquisition. Its dependencies on the application domain and its evolution steer the next generation of research activities. From the raw data to knowledge, processing the relationship between these phases has added new impetus to how these are understood and communicated. The tradition of use and communication by visualization is deep-rooted. It helps us investigate new meanings for the humanities, history of art, design, human factors, and user experience, leading to discoveries and hypothesis analysis. Modern-day computer-aided analytics and visualization have added momentum in developing tools that exploit metaphor-driven techniques within many applied domains. The methods are developed beyond visualization to simplify the complexities, reveal ambiguity, and work with incompleteness. The next phase of this evolving field is to understand uncertainty, risk analysis, and tapping into unknowns; this uncertainty is built into all stages of the process, from raw data to the knowledge acquisition stage. This collection of papers on this year's information visualization forum, compiled for the 26th conference on the Information Visualization incorporating the following: Artificial Intelligence – analytics, machine-, deep-learning, and Learning Analytics - IV2021, advocates that a new conceptual framework will emerge from information-rich disciplines like the Humanities, Psychology, Sociology, Business of everyday activities as well as the science-rich disciplines. To facilitate this, IV2022 provides the opportunity to resonate with many international and collaborative research projects, lectures, and panel discussions from distinguished speakers that channel the way this new framework conceptually and practically has been realized. This year's theme is enhanced further by AI, Social Networking impact on the social, cultural, and heritage aspects of life, and learning analysis of today's multifaceted and data-rich environment. Joining us in this search are some 75-plus researchers who reflect and share a chapter of their thoughts with fellow researchers. The papers collected, peer-reviewed by the international reviewing committee, reflect the vibrant state of information visualization, analytics, applications, and results of researchers, artists, and professionals from more than 25 countries. It has allowed us to address the scope of visualization from a much broader perspective. Each contributor to this conference has added fresh views and thoughts that challenge our beliefs and further encourage our adventure of innovation. Close doi:10.1109/IV56949.2022.00001 Close
136.	Christoph Demus; Jonas Pitz; Mina Schütz; Nadine Probol; Melanie Siegel; Dirk Labudde DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis Inproceedings In: Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pp. 143–153, Association for Computational Linguistics, Seattle, Washington (Hybrid), 2022. Abstract \| Links \| BibTeX \| Tags: @inproceedings{DPSP22, title = {DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis}, author = {Christoph Demus and Jonas Pitz and Mina Schütz and Nadine Probol and Melanie Siegel and Dirk Labudde}, url = {https://aclanthology.org/2022.woah-1.14}, doi = {10.18653/v1/2022.woah-1.14}, year = {2022}, date = {2022-07-01}, booktitle = {Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)}, pages = {143--153}, publisher = {Association for Computational Linguistics}, address = {Seattle, Washington (Hybrid)}, abstract = {In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments' contexts and do conversation analyses in order to research the contagion of offensive language in conversations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments' contexts and do conversation analyses in order to research the contagion of offensive language in conversations. Close https://aclanthology.org/2022.woah-1.14 doi:10.18653/v1/2022.woah-1.14 Close
135.	Lennart B. Sina; Kawa Nazemi Visual Analytics for Systematic Reviews According to PRISMA Inproceedings In: 2022 26th International Conference Information Visualisation (IV), pp. 307 - 313, IEEE, 2022. Abstract \| Links \| BibTeX \| Tags: @inproceedings{SiNa22, title = {Visual Analytics for Systematic Reviews According to PRISMA}, author = {Lennart B. Sina and Kawa Nazemi}, doi = {10.1109/IV56949.2022.00059}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {2022 26th International Conference Information Visualisation (IV)}, pages = {307 - 313}, publisher = {IEEE}, abstract = {Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Systematic reviews play an essential role in various disciplines. Particularly, in biomedical sciences, systematic reviews according to a predefined schema and protocol are how related literature is analyzed. Although a protocol-based systematic review is replicable and provides the required information to reproduce each step and refine them, such a systematic review is time-consuming and may get complex. To face this challenge, automatic methods can be applied that support researchers in their systematic analysis process. The combination of artificial intelligence for automatic information extraction from scientific literature with interactive visualizations as a Visual Analytics system can lead to sophisticated analysis and protocoling of the review process. We introduce in this paper a novel Visual Analytics approach and system that enables researchers to visually search and explore scientific publications and generate a protocol based on the PRISMA protocol and the PRISMA statement. Close doi:10.1109/IV56949.2022.00059 Close
134.	Midhad Blazevic; Lennart B. Sina; Kawa Nazemi Visual Collaboration - An Approach for Visual Analytical Collaborative Research Inproceedings In: 2022 26th International Conference Information Visualisation (IV), pp. 293 - 299, IEEE, 2022. Abstract \| Links \| BibTeX \| Tags: @inproceedings{BSN22, title = { Visual Collaboration - An Approach for Visual Analytical Collaborative Research}, author = {Midhad Blazevic and Lennart B. Sina and Kawa Nazemi}, doi = {10.1109/IV56949.2022.00057}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {2022 26th International Conference Information Visualisation (IV)}, pages = {293 - 299}, publisher = {IEEE}, abstract = {Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Studies have shown that collaboration in scientific fields is rising and considered enormously important. However, collaboration has proved to be challenging for various reasons, among others, the requirements for human-machine workflows. The importance of scientific collaboration lies in the complexity of the challenges that are faced today. The more complex the challenge, the more scientists should work together. The current form of collaboration in the scientific community is not as intelligent as it should be. Scientists have to multitask with various applications, often losing cognitive focus. Collaboration itself is very nearsighted as it is usually conducted not solely based on expertise but instead on social or local networks. We introduce a single-source visual collaboration approach based on learning methods in this work. We use machine learning and natural language processing approaches to improve the traditional research and development process and create a system that facilitates and encourages collaboration based on expertise, enhancing the research collaboration process in many ways. Our approach combines collaborative Visual Analytics with enhanced collaboration techniques to support researchers from different disciplines. Close doi:10.1109/IV56949.2022.00057 Close
133.	Medina Andresel; Sergiu Gordea; Srdjan Stevanetic; Mina Schütz An Approach for Curating Collections of Historical Documents with the Use of Topic Detection Technologies Journal Article In: Int. J. Digit. Curation, vol. 17, no. 1, pp. 12, 2022. Abstract \| Links \| BibTeX \| Tags: @article{AGSS22, title = {An Approach for Curating Collections of Historical Documents with the Use of Topic Detection Technologies}, author = {Medina Andresel and Sergiu Gordea and Srdjan Stevanetic and Mina Schütz}, url = {http://www.ijdc.net/article/view/819}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, journal = {Int. J. Digit. Curation}, volume = {17}, number = {1}, pages = {12}, abstract = {Digital curation of materials available in large online repositories is required to enable the reuse of Cultural Heritage resources in specific activities like education or scientific research. The digitization of such valuable objects is an important task for making them accessible through digital platforms such as Europeana, therefore ensuring the success of transcription campaigns via the Transcribathon platform is highly important for this goal. Based on impact assessment results, people are more engaged in the transcription process if the content is more oriented to specific themes, such as First World War. Currently, efforts to group related documents into thematic collections are in general hand-crafted and due to the large ingestion of new material they are difficult to maintain and update. The current solutions based on text retrieval are not able to support the discovery of related content since the existing collections are multi-lingual and contain heterogeneous items like postcards, letters, journals, photographs etc. Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. To use existing topic detection technologies on Europeana collections there are several challenges to be addressed: (1) ensure representative and qualitative training data, (2) ensure the quality of the learned topics, and (3) efficient and scalable solutions for searching related content based on the automatically detected topics, and for suggesting the most relevant topics on new items. This paper describes in more details each such challenge and the proposed solutions thus offering a novel perspective on how digital curation practices can be enhanced with the help of machine learning technologies.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Digital curation of materials available in large online repositories is required to enable the reuse of Cultural Heritage resources in specific activities like education or scientific research. The digitization of such valuable objects is an important task for making them accessible through digital platforms such as Europeana, therefore ensuring the success of transcription campaigns via the Transcribathon platform is highly important for this goal. Based on impact assessment results, people are more engaged in the transcription process if the content is more oriented to specific themes, such as First World War. Currently, efforts to group related documents into thematic collections are in general hand-crafted and due to the large ingestion of new material they are difficult to maintain and update. The current solutions based on text retrieval are not able to support the discovery of related content since the existing collections are multi-lingual and contain heterogeneous items like postcards, letters, journals, photographs etc. Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. To use existing topic detection technologies on Europeana collections there are several challenges to be addressed: (1) ensure representative and qualitative training data, (2) ensure the quality of the learned topics, and (3) efficient and scalable solutions for searching related content based on the automatically detected topics, and for suggesting the most relevant topics on new items. This paper describes in more details each such challenge and the proposed solutions thus offering a novel perspective on how digital curation practices can be enhanced with the help of machine learning technologies. Close http://www.ijdc.net/article/view/819 Close
132.	Juliane Köhler; Gautam Kishore Shahi; Julia Maria Struss; Michael Wiegand; Melanie Siegel; Mina Schütz Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection Inproceedings In: Guglielmo Faggioli; Nicola Ferro; Allan Hanbury; Martin Potthast (Ed.): Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, pp. 404-421, Bologna, Italy, 2022. Links \| BibTeX \| Tags: @inproceedings{KSSW22, title = {Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection}, author = {Juliane Köhler and Gautam Kishore Shahi and Julia Maria Struss and Michael Wiegand and Melanie Siegel and Mina Schütz}, editor = {Guglielmo Faggioli and Nicola Ferro and Allan Hanbury and Martin Potthast}, url = {https://ceur-ws.org/Vol-3180/paper-30.pdf}, year = {2022}, date = {2022-01-01}, booktitle = {Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum}, pages = {404-421}, address = {Bologna, Italy}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close https://ceur-ws.org/Vol-3180/paper-30.pdf Close
131.	Mina Schütz; Jaqueline Böck; Medina Andresel; Armin Kirchknopf; Daria Liakhovets; Djordje Slijepčević; Alexander Schindler AIT_FHSTP at CheckThat! 2022: Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer Inproceedings In: Guglielmo Faggioli; Nicola Ferro; Allan Hanbury; Martin Potthast (Ed.): Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, pp. 660-670, Bologna, Italy, 2022. Abstract \| Links \| BibTeX \| Tags: @inproceedings{SBAK22, title = {AIT_FHSTP at CheckThat! 2022: Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer}, author = {Mina Schütz and Jaqueline Böck and Medina Andresel and Armin Kirchknopf and Daria Liakhovets and Djordje Slijepčević and Alexander Schindler}, editor = {Guglielmo Faggioli and Nicola Ferro and Allan Hanbury and Martin Potthast}, url = {https://ceur-ws.org/Vol-3180/paper-53.pdf}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum}, pages = {660-670}, address = {Bologna, Italy}, abstract = {The increase of fake news in today’s society, partially due to the accelerating digital transformation, is a major problem in today’s world. This year’s CheckThat! Lab 2022 challenge addresses this problem as a Natural Language Processing (NLP) task aiming to detect fake news in English and German texts. Within this paper, we present our methodology and results for both, the monolingual (English) and cross-lingual (German) tasks of the CheckThat! challenge in 2022. We applied the multilingual transformer model XLM-RoBERTa to solve these tasks by pre-training the models on additional datasets and fine-tuning them on the original data as well as its translations for the cross-lingual task. Our final model achieves a macro F1-score of 15,48% and scores the 22𝑡ℎ rank in the benchmark. Regarding the second task, i.e., the cross-lingual German classification, our final model achieves an F1-score of 19.46% and reaches the 4𝑡ℎ rank in the benchmark.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close The increase of fake news in today’s society, partially due to the accelerating digital transformation, is a major problem in today’s world. This year’s CheckThat! Lab 2022 challenge addresses this problem as a Natural Language Processing (NLP) task aiming to detect fake news in English and German texts. Within this paper, we present our methodology and results for both, the monolingual (English) and cross-lingual (German) tasks of the CheckThat! challenge in 2022. We applied the multilingual transformer model XLM-RoBERTa to solve these tasks by pre-training the models on additional datasets and fine-tuning them on the original data as well as its translations for the cross-lingual task. Our final model achieves a macro F1-score of 15,48% and scores the 22𝑡ℎ rank in the benchmark. Regarding the second task, i.e., the cross-lingual German classification, our final model achieves an F1-score of 19.46% and reaches the 4𝑡ℎ rank in the benchmark. Close https://ceur-ws.org/Vol-3180/paper-53.pdf Close
130.	Daria Liakhovets; Mina Schütz; Jaqueline Böck; Medina Andresel; Armin Kirchknopf; Andreas Babic; Djordje Slijepčević; Jasmin Lampert; Alexander Schindler; Matthias Zeppelzauer Transfer Learning for Automatic Sexism Detection with Multilingual Transformer Models Inproceedings In: Manuel Montes-y-Gómez; Julio Gonzalo; Francisco Rangel; Marco Casavantes; Miguel Ángel Álvarez-Carmona; Gemma Bel-Enguix; Hugo Jair Escalante; Larissa Freitas; Antonio Miranda-Escalada; Francisco Rodríguez-Sánchez; Aiala Rosá; Marco Antonio Sobrevilla-Cabezudo; Mariona Taulé; Rafael Valencia-García (Ed.): Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022), CEUR-WS.org, A Coruna, Spain, 2022. Abstract \| Links \| BibTeX \| Tags: @inproceedings{LSBA22, title = {Transfer Learning for Automatic Sexism Detection with Multilingual Transformer Models}, author = {Daria Liakhovets and Mina Schütz and Jaqueline Böck and Medina Andresel and Armin Kirchknopf and Andreas Babic and Djordje Slijepčević and Jasmin Lampert and Alexander Schindler and Matthias Zeppelzauer}, editor = {Manuel Montes-y-Gómez and Julio Gonzalo and Francisco Rangel and Marco Casavantes and Miguel Ángel Álvarez-Carmona and Gemma Bel-Enguix and Hugo Jair Escalante and Larissa Freitas and Antonio Miranda-Escalada and Francisco Rodríguez-Sánchez and Aiala Rosá and Marco Antonio Sobrevilla-Cabezudo and Mariona Taulé and Rafael Valencia-García}, url = {https://ceur-ws.org/Vol-3202/exist-paper1.pdf}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2022)}, publisher = {CEUR-WS.org}, address = {A Coruna, Spain}, abstract = {In recent years sexism has become an increasingly significant problem on social networks. In order to address this problem, the sEXism Identification in Social neTworks (EXIST) challenge has been launched at IberLEF in 2021. In this international benchmark, sexism detection is formulated as a Natural Language Processing (NLP) task with the aim to automatically identify sexism in social media content (binary classification) and to classify statements into different categories such as dominance, stereotyping or objectification. In this paper we present the contribution of team AIT_FHSTP for the EXIST challenge at IberLEF in 2022. To solve the two related tasks we applied two multilingual transformer models, one based on a multilingual BERT and one based on an XLM-RoBERTa architecture, and a monolingual (English) T5 model. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional as well as augmented data. For both tasks the XLM-RoBERTa model, which applies a combination of the two strategies, outperforms the other two models. The best run for the binary classification (task 1) achieves a macro F1-score of 74.96% and scores the 26𝑡ℎ rank in the benchmark; for the multi-class classification (task 2) our best submission scores the 13𝑡ℎ rank with a macro F1-score of 46.75%.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close In recent years sexism has become an increasingly significant problem on social networks. In order to address this problem, the sEXism Identification in Social neTworks (EXIST) challenge has been launched at IberLEF in 2021. In this international benchmark, sexism detection is formulated as a Natural Language Processing (NLP) task with the aim to automatically identify sexism in social media content (binary classification) and to classify statements into different categories such as dominance, stereotyping or objectification. In this paper we present the contribution of team AIT_FHSTP for the EXIST challenge at IberLEF in 2022. To solve the two related tasks we applied two multilingual transformer models, one based on a multilingual BERT and one based on an XLM-RoBERTa architecture, and a monolingual (English) T5 model. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional as well as augmented data. For both tasks the XLM-RoBERTa model, which applies a combination of the two strategies, outperforms the other two models. The best run for the binary classification (task 1) achieves a macro F1-score of 74.96% and scores the 26𝑡ℎ rank in the benchmark; for the multi-class classification (task 2) our best submission scores the 13𝑡ℎ rank with a macro F1-score of 46.75%. Close https://ceur-ws.org/Vol-3202/exist-paper1.pdf Close
129.	Christoph Demus; Mina Schütz; Jonas Pitz; Nadine Probol; Melanie Siegel; Dirk Labudde Automatische Klassifikation offensiver deutscher Sprache in sozialen Netzwerken Book Chapter In: Sylvia Jaki; Stefan Steiger (Ed.): Digitale Hate Speech, 2022. BibTeX \| Tags: @inbook{DSPP22, title = {Automatische Klassifikation offensiver deutscher Sprache in sozialen Netzwerken}, author = {Christoph Demus and Mina Schütz and Jonas Pitz and Nadine Probol and Melanie Siegel and Dirk Labudde}, editor = {Sylvia Jaki and Stefan Steiger}, year = {2022}, date = {2022-01-01}, booktitle = {Digitale Hate Speech}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } Close

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