Oda Elise Nordberg
PhD student
University of Bergen
Oda Elise Nordberg began her PhD position focusing on technology and innovation within the field of journalism in January 2020. Currently, she is researching how people interact with the news through voice-activated interfaces and exploring ways to improve this interaction. Her research is primarily qualitative, with interest areas revolving around how people interact with technology (Human-Computer Interaction, HCI), interaction design, innovation in journalism, privacy, and ethics.
Nordberg has a background that includes a bachelor's degree in new media, a master's degree in information science, and a one-year program in psychology, all from the University of Bergen.
2024
Andrews, Peter; Nordberg, Oda Elise; Guribye, Frode; Fjeld, Morten; Borch, Njål
Designing for Automated Sports Commentary Systems Conference
IMX'24, 2024.
@conference{designing_for_automated24,
title = {Designing for Automated Sports Commentary Systems},
author = {Peter Andrews and Oda Elise Nordberg and Frode Guribye and Morten Fjeld and Njål Borch },
url = {https://mediafutures.no/designing_for_automated_sports_commentary_systems-2/},
year = {2024},
date = {2024-06-12},
booktitle = {IMX'24},
abstract = {Advancements in Natural Language Processing (NLP) and Computer Vision (CV) are revolutionizing how we experience sports broadcasting. Traditionally, sports commentary has played a crucial role in enhancing viewer understanding and engagement with live games. Yet, the prospects of automated commentary, especially in light of these technological advancements and their impact on viewers’ experience, remain largely unexplored. This paper elaborates upon an innovative automated commentary system that integrates NLP and CV to provide a multimodal experience, combining auditory feedback through text-to-speech and visual cues, known as italicizing, for real-time in-game commentary. The system supports color commentary, which aims to inform the viewer of information surrounding the game by pulling additional content from a database. Moreover, it also supports play-by-play commentary covering in-game developments derived from an event system based on CV. As the system reinvents the role of commentary in sports video, we must consider the design and implications of multimodal artificial commentators. A focused user study with eight participants aimed at understanding the design implications of such multimodal artificial commentators reveals critical insights. Key findings emphasize the importance of language precision, content relevance, and delivery style in automated commentary, underscoring the necessity for personalization to meet diverse viewer preferences. Our results validate the potential value and effectiveness of multimodal feedback and derive design considerations, particularly in personalizing content to revolutionize the role of commentary in sports broadcasts.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Advancements in Natural Language Processing (NLP) and Computer Vision (CV) are revolutionizing how we experience sports broadcasting. Traditionally, sports commentary has played a crucial role in enhancing viewer understanding and engagement with live games. Yet, the prospects of automated commentary, especially in light of these technological advancements and their impact on viewers’ experience, remain largely unexplored. This paper elaborates upon an innovative automated commentary system that integrates NLP and CV to provide a multimodal experience, combining auditory feedback through text-to-speech and visual cues, known as italicizing, for real-time in-game commentary. The system supports color commentary, which aims to inform the viewer of information surrounding the game by pulling additional content from a database. Moreover, it also supports play-by-play commentary covering in-game developments derived from an event system based on CV. As the system reinvents the role of commentary in sports video, we must consider the design and implications of multimodal artificial commentators. A focused user study with eight participants aimed at understanding the design implications of such multimodal artificial commentators reveals critical insights. Key findings emphasize the importance of language precision, content relevance, and delivery style in automated commentary, underscoring the necessity for personalization to meet diverse viewer preferences. Our results validate the potential value and effectiveness of multimodal feedback and derive design considerations, particularly in personalizing content to revolutionize the role of commentary in sports broadcasts.
Andrews, Peter; Nordberg, Oda Elise; Guribye, Frode; Fujita, Kazuyuki; Fjeld, Morten; Borch, Njål
AiCommentator: A Multimodal Conversational Agent for Embedded Visualization in Football Viewing Conference
Intelligent User Interfaces (IUI), 2024.
@conference{AIComment,
title = {AiCommentator: A Multimodal Conversational Agent for Embedded Visualization in Football Viewing},
author = {Peter Andrews and Oda Elise Nordberg and Frode Guribye and Kazuyuki Fujita and Morten Fjeld and Njål Borch},
url = {https://mediafutures.no/acm_iui_24_aicommentator_peterandrews-1/},
year = {2024},
date = {2024-03-18},
urldate = {2024-03-18},
booktitle = {Intelligent User Interfaces (IUI)},
journal = {Intelligent User Interfaces (IUI)},
abstract = {Traditionally, sports commentators provide viewers with diverse information, encompassing in-game developments and player performances. Yet young adult football viewers increasingly use mobile devices for deeper insights during football matches. Such insights into players on the pitch and performance statistics support viewers’ understanding of game stakes, creating a more engaging viewing experience. Inspired by commentators’ traditional roles and to incorporate information into a single platform, we developed AiCommentator, a Multimodal Conversational Agent (MCA) for embedded visualization and conversational interactions in football broadcast video. AiCommentator integrates embedded visualization, either with an automated non-interactive or with a responsive interactive commentary mode. Our system builds upon multimodal techniques, integrating computer vision and large language models, to demonstrate ways for designing tailored, interactive sports-viewing content. AiCommentator’s event system infers game states based on a multi-object tracking algorithm and computer vision backend, facilitating automated responsive commentary. We address three key topics: evaluating young adults’ satisfaction and immersion across the two viewing modes, enhancing viewer understanding of in-game events and players on the pitch, and devising methods to present this information in a usable manner. In a mixed-method evaluation (n=16) of AiCommentator, we found that the participants appreciated aspects of both system modes but preferred the interactive mode, expressing a higher degree of engagement and satisfaction. Our paper reports on our development of AiCommentator and presents the results from our user study, demonstrating the promise of interactive MCA for a more engaging sports viewing experience. Systems like AiCommentator could be pivotal in transforming the interactivity and accessibility of sports content, revolutionizing how sports viewers engage with video content.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Traditionally, sports commentators provide viewers with diverse information, encompassing in-game developments and player performances. Yet young adult football viewers increasingly use mobile devices for deeper insights during football matches. Such insights into players on the pitch and performance statistics support viewers’ understanding of game stakes, creating a more engaging viewing experience. Inspired by commentators’ traditional roles and to incorporate information into a single platform, we developed AiCommentator, a Multimodal Conversational Agent (MCA) for embedded visualization and conversational interactions in football broadcast video. AiCommentator integrates embedded visualization, either with an automated non-interactive or with a responsive interactive commentary mode. Our system builds upon multimodal techniques, integrating computer vision and large language models, to demonstrate ways for designing tailored, interactive sports-viewing content. AiCommentator’s event system infers game states based on a multi-object tracking algorithm and computer vision backend, facilitating automated responsive commentary. We address three key topics: evaluating young adults’ satisfaction and immersion across the two viewing modes, enhancing viewer understanding of in-game events and players on the pitch, and devising methods to present this information in a usable manner. In a mixed-method evaluation (n=16) of AiCommentator, we found that the participants appreciated aspects of both system modes but preferred the interactive mode, expressing a higher degree of engagement and satisfaction. Our paper reports on our development of AiCommentator and presents the results from our user study, demonstrating the promise of interactive MCA for a more engaging sports viewing experience. Systems like AiCommentator could be pivotal in transforming the interactivity and accessibility of sports content, revolutionizing how sports viewers engage with video content.
2020
Soe, Than Htut; Nordberg, Oda Elise; Guribye, Frode; Slavkovik, Marija
Circumvention by design - dark patterns in cookie consents for online news outlets Conference
Proceedings of the 11th Nordic Conference on Human-Computer Interaction, 2020, (Pre SFI).
@conference{Soe2020,
title = {Circumvention by design - dark patterns in cookie consents for online news outlets},
author = {Than Htut Soe and Oda Elise Nordberg and Frode Guribye and Marija Slavkovik},
url = {https://dl.acm.org/doi/pdf/10.1145/3419249.3420132},
doi = {10.1145/3419249.3420132},
year = {2020},
date = {2020-06-24},
booktitle = {Proceedings of the 11th Nordic Conference on Human-Computer Interaction},
abstract = {To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union's General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up. We have manually analyzed 300 data collection consent notices from news outlets that are built to ensure compliance with GDPR. The analysis uncovered a variety of strategies or dark patterns that circumvent the intent of GDPR by design. We further study the presence and variety of these dark patterns in these "cookie consents" and use our observations to specify the concept of dark pattern in the context of consent elicitation.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union's General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up. We have manually analyzed 300 data collection consent notices from news outlets that are built to ensure compliance with GDPR. The analysis uncovered a variety of strategies or dark patterns that circumvent the intent of GDPR by design. We further study the presence and variety of these dark patterns in these "cookie consents" and use our observations to specify the concept of dark pattern in the context of consent elicitation.