Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

Portenoy, Jason; Radensky, Marissa; West, Jevin D.; Horvitz, Eric; Weld, Daniel S.; Hope, Tom

doi:10.1145/3491102.3501905

Cited by 15 publications

(18 citation statements)

References 39 publications

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“…We present key concepts of task-guided scientific knowledge retrieval, including work on prototypes that highlight the promise of the direction and bring into focus concrete steps forward for novel representations, tools, and services. We review systems that help researchers discover novel perspectives and inspirations [16,17,19,42], help guide the attention of researchers toward opportunity areas rife with uncertainties and unknowns [26,46], and models that leverage retrieval and synthesis of scientific knowledge as part of machine learning and prediction [12,37]. We conclude with a discussion of opportunities ahead with computational approaches that have the potential to revolutionize science.…”

Section: Widening Gapmentioning

confidence: 99%

“…These limitations are all tightly interrelated. The ability to generate ideas, for instance, directly relies on prior knowledge and understandings; but, when a large volume of information from the outer world of science is met by insufficient cognitive capacity for processing and assimilating it, the result is information overload-a ubiquitous hindrance for researchers [28,42]. Information overload in science strains the attentional resources of researchers, and forces researchers to allocate attention to increasingly narrow areas.…”

Section: Inner World: Human Cognition In Sciencementioning

confidence: 99%

“…This effect, in turn, amplifies a host of biases which researchers, just like all humans, suffer from [40,46]. For example, scientists can be limited by confirmation bias [8], aversion to information from novel domains [24,42], homophily [32], and fixation on specific directions and perspectives without consideration of alternative views [19,40]. More broadly, selection of directions and areas to work on is a case of decision-making, and as such personal preference and subjective utility play fundamental roles.…”

Section: Inner World: Human Cognition In Sciencementioning

confidence: 99%

“…Grounded in this cognitive understanding, we have pursued several approaches for stimulating creativity powered by retrieving outer knowledge. We developed and studied a system named Bridger that connects researchers to peers who inspire novel directions for research [42]. Bridger identifies matches among authors based on commonalities and contrasts, identifying peers who are both relevant and novel-working on similar problems but using very different methods, potentially inspiring new solutions.…”

Section: Prototypes Of Task-guided Retrievalmentioning

confidence: 99%

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A Computational Inflection for Scientific Discovery

Hope¹,

Downey²,

Etzioni³

et al. 2022

Preprint

Self Cite

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We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's collective scientific knowledge and discourse. We now read and write papers in digitized form, and a great deal of the formal and informal processes of science are captured digitally-including papers, preprints and books, code and datasets, conference presentations, and interactions in social networks and collaboration and communication platforms. The transition has led to the creation and growth of a tremendous amount of information-much of which is available for public access-opening exciting opportunities for computational models and systems that analyze and harness it. In parallel, exponential growth in data processing power has fueled remarkable advances in artificial intelligence, including self-supervised neural models capable of learning powerful representations from large-scale unstructured text without costly human supervision. Dramatic changes in scientific communication channels-such as the advent of the first scientific journal in the 17 th century-have historically catalyzed revolutions in scientific thought and behavior. The confluence of societal and computational trends suggests that computer science is poised to ignite a revolution in the scientific process itself.

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Section: Widening Gapmentioning

confidence: 99%

Section: Inner World: Human Cognition In Sciencementioning

confidence: 99%

Section: Inner World: Human Cognition In Sciencementioning

confidence: 99%

Section: Prototypes Of Task-guided Retrievalmentioning

confidence: 99%

See 2 more Smart Citations

A Computational Inflection for Scientific Discovery

Hope¹,

Downey²,

Etzioni³

et al. 2022

Preprint

Self Cite

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show abstract

“…Readers of scientific papers often encounter unfamiliar concepts, which impedes their understanding (Portenoy et al, 2022). This is because papers assume a priori knowledge, and often lack definitions for the scientific terms that they use.…”

Section: Introductionmentioning

confidence: 99%

ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts

Murthy¹,

Lo²,

King³

et al. 2022

Preprint

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Systems that can automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge. However, current systems assume a single "best" description per concept, which fails to account for the many potentially useful ways a concept can be described. We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts. Our system takes advantage of the myriad ways a concept is mentioned across the scientific literature to produce distinct, diverse descriptions of target scientific concepts in terms of different reference concepts. To support research on the task, we release an expert-annotated resource, the ACCoRD corpus, which includes 1,275 labeled contexts and 1,787 hand-authored concept descriptions. We conduct a user study demonstrating that (1) users prefer descriptions produced by our end-to-end system, and (2) users prefer multiple descriptions to a single "best" description.

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Turning Filter Bubbles into Bubblesphere with Multi‐Viewpoint KOS and Diverse Similarity

Zhitomirsky‐Geffet

2022

Proceedings of the Association for Information Science and Tech

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The filter bubble phenomenon and its negative societal effects have been extensively explored in the literature in the past decade. However, the ability of modern AI‐based systems to create personalized information bubbles, that is, to classify similar contents and users into clusters according to their interests and behavior, can actually be quite beneficial if utilized and managed properly and ethically. In this article we present ongoing research that aims to refine such bubble‐building smart systems by adopting an ethical, multi‐perspective approach that allows for linking isolated bubbles into a consolidated bubblesphere and offering users a choice to explore diverse bubbles related to their topics of interest. To implement the proposed approach, content matching should be based on diverse similarity, which can be derived from a multi‐viewpoint KOS. In addition, the study explores how such a multi‐viewpoint KOS and bubblesphere can be constructed using Wikidata's ranks and qualifiers.

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Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

Cited by 15 publications

References 39 publications

A Computational Inflection for Scientific Discovery

A Computational Inflection for Scientific Discovery

ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts

Turning Filter Bubbles into Bubblesphere with Multi‐Viewpoint KOS and Diverse Similarity

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