2,566 research outputs found
A psychology literature study on modality related issues for multimodal presentation in crisis management
The motivation of this psychology literature study is to obtain modality related guidelines for real-time information presentation in crisis management environment. The crisis management task is usually companied by time urgency, risk, uncertainty, and high information density. Decision makers (crisis managers) might undergo cognitive overload and tend to show biases in their performances. Therefore, the on-going crisis event needs to be presented in a manner that enhances perception, assists diagnosis, and prevents cognitive overload. To this end, this study looked into the modality effects on perception, cognitive load, working memory, learning, and attention. Selected topics include working memory, dual-coding theory, cognitive load theory, multimedia learning, and attention. The findings are several modality usage guidelines which may lead to more efficient use of the user’s cognitive capacity and enhance the information perception
Navigating Complex Search Tasks with AI Copilots
As many of us in the information retrieval (IR) research community know and
appreciate, search is far from being a solved problem. Millions of people
struggle with tasks on search engines every day. Often, their struggles relate
to the intrinsic complexity of their task and the failure of search systems to
fully understand the task and serve relevant results. The task motivates the
search, creating the gap/problematic situation that searchers attempt to
bridge/resolve and drives search behavior as they work through different task
facets. Complex search tasks require more than support for rudimentary fact
finding or re-finding. Research on methods to support complex tasks includes
work on generating query and website suggestions, personalizing and
contextualizing search, and developing new search experiences, including those
that span time and space. The recent emergence of generative artificial
intelligence (AI) and the arrival of assistive agents, or copilots, based on
this technology, has the potential to offer further assistance to searchers,
especially those engaged in complex tasks. There are profound implications from
these advances for the design of intelligent systems and for the future of
search itself. This article, based on a keynote by the author at the 2023 ACM
SIGIR Conference, explores these issues and charts a course toward new horizons
in information access guided by AI copilots.Comment: 10 pages, 6 figure
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Understanding image-text relations and news values for multimodal news analysis
The analysis of news dissemination is of utmost importance since the credibility of information and the identification of disinformation and misinformation affect society as a whole. Given the large amounts of news data published daily on the Web, the empirical analysis of news with regard to research questions and the detection of problematic news content on the Web require computational methods that work at scale. Today's online news are typically disseminated in a multimodal form, including various presentation modalities such as text, image, audio, and video. Recent developments in multimodal machine learning now make it possible to capture basic “descriptive” relations between modalities–such as correspondences between words and phrases, on the one hand, and corresponding visual depictions of the verbally expressed information on the other. Although such advances have enabled tremendous progress in tasks like image captioning, text-to-image generation and visual question answering, in domains such as news dissemination, there is a need to go further. In this paper, we introduce a novel framework for the computational analysis of multimodal news. We motivate a set of more complex image-text relations as well as multimodal news values based on real examples of news reports and consider their realization by computational approaches. To this end, we provide (a) an overview of existing literature from semiotics where detailed proposals have been made for taxonomies covering diverse image-text relations generalisable to any domain; (b) an overview of computational work that derives models of image-text relations from data; and (c) an overview of a particular class of news-centric attributes developed in journalism studies called news values. The result is a novel framework for multimodal news analysis that closes existing gaps in previous work while maintaining and combining the strengths of those accounts. We assess and discuss the elements of the framework with real-world examples and use cases, setting out research directions at the intersection of multimodal learning, multimodal analytics and computational social sciences that can benefit from our approach
Understanding image-text relations and news values for multimodal news analysis
The analysis of news dissemination is of utmost importance since the credibility of information and the identification of disinformation and misinformation affect society as a whole. Given the large amounts of news data published daily on the Web, the empirical analysis of news with regard to research questions and the detection of problematic news content on the Web require computational methods that work at scale. Today's online news are typically disseminated in a multimodal form, including various presentation modalities such as text, image, audio, and video. Recent developments in multimodal machine learning now make it possible to capture basic “descriptive” relations between modalities–such as correspondences between words and phrases, on the one hand, and corresponding visual depictions of the verbally expressed information on the other. Although such advances have enabled tremendous progress in tasks like image captioning, text-to-image generation and visual question answering, in domains such as news dissemination, there is a need to go further. In this paper, we introduce a novel framework for the computational analysis of multimodal news. We motivate a set of more complex image-text relations as well as multimodal news values based on real examples of news reports and consider their realization by computational approaches. To this end, we provide (a) an overview of existing literature from semiotics where detailed proposals have been made for taxonomies covering diverse image-text relations generalisable to any domain; (b) an overview of computational work that derives models of image-text relations from data; and (c) an overview of a particular class of news-centric attributes developed in journalism studies called news values. The result is a novel framework for multimodal news analysis that closes existing gaps in previous work while maintaining and combining the strengths of those accounts. We assess and discuss the elements of the framework with real-world examples and use cases, setting out research directions at the intersection of multimodal learning, multimodal analytics and computational social sciences that can benefit from our approach
Design Principals of Social Navigation
8th Delos Workshop on "User Interfaces for Digital Libraries" (on 21 October it will be held in conjuction with the 4th ERCIM Workshop on "User Interfaces for All"), SICS, Kista, Sweden, 21-23 October 1998PERSON
Tidying Up the Conversational Recommender Systems' Biases
The growing popularity of language models has sparked interest in
conversational recommender systems (CRS) within both industry and research
circles. However, concerns regarding biases in these systems have emerged.
While individual components of CRS have been subject to bias studies, a
literature gap remains in understanding specific biases unique to CRS and how
these biases may be amplified or reduced when integrated into complex CRS
models. In this paper, we provide a concise review of biases in CRS by
surveying recent literature. We examine the presence of biases throughout the
system's pipeline and consider the challenges that arise from combining
multiple models. Our study investigates biases in classic recommender systems
and their relevance to CRS. Moreover, we address specific biases in CRS,
considering variations with and without natural language understanding
capabilities, along with biases related to dialogue systems and language
models. Through our findings, we highlight the necessity of adopting a holistic
perspective when dealing with biases in complex CRS models
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