Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery

@article{Portenoy2022BurstingSF,
  title={Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery},
  author={Jason Portenoy and Marissa Radensky and Jevin D. West and Eric Horvitz and Daniel S. Weld and Tom Hope},
  journal={CHI Conference on Human Factors in Computing Systems},
  year={2022}
}
Isolated silos of scientific research and the growing challenge of information overload limit awareness across the literature and hinder innovation. Algorithmic curation and recommendation, which often prioritize relevance, can further reinforce these informational “filter bubbles.” In response, we describe Bridger, a system for facilitating discovery of scholars and their work. We construct a faceted representation of authors with information gleaned from their papers and inferred author… 

Figures and Tables from this paper

Augmenting Scientific Creativity with Retrieval across Knowledge Domains
TLDR
An exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains, which is aimed at augmenting the end-user ability in cross-domain exploration.
Specialized document embeddings for aspect-based similarity of research papers
TLDR
The approach of aspect-based document embeddings mitigates potential risks arising from implicit biases by making them explicit and can, for example, be used for more diverse and explainable recommendations.
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
TLDR
ACCoRD, an end-to-end system tack-ling the novel task of generating sets of descriptions of scientific concepts, is presented and a user study is conducted demonstrating that users prefer descriptions produced by the system, and users prefer multiple descriptions to a single “best” description.

References

SHOWING 1-10 OF 81 REFERENCES
Echo Chambers in Science?
TLDR
There is some evidence that the digitization of science has democratized the exposure of prior research and weakened journals’ role as gatekeepers, and the increasing importance of prior citations suggests a competing trend is also occurring that may create an echo chamber centered on small numbers of highly cited papers.
VITALITY: Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics
TLDR
Qualitative findings from an evaluation of VITALITY are presented, suggesting it can be a promising complementary technique for conducting academic literature reviews.
Leveraging Citation Networks to Visualize Scholarly Influence Over Time
TLDR
This approach uses an animated node-link diagram showing the citation network accumulated around the researcher over the course of the career in concert with key indicators, highlighting influence both within and across fields.
AMiner: Search and Mining of Academic Social Networks
AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and
CiteRivers: Visual Analytics of Citation Patterns
TLDR
This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach and enable users to analyze citation patterns, spot trends, and track long-term developments.
SOLVENT: A Mixed Initiative System for Finding Analogies between Research Papers
TLDR
SOLVENT is introduced, a mixed-initiative system where humans annotate aspects of research papers that denote their background, purpose, mechanism, and findings, and a computational model constructs a semantic representation from these annotations that can be used to find analogies among the research papers.
Diversity Exposure in Social Recommender Systems: A Social Capital Theory Perspective
TLDR
The extant theory on social capital and diversity exposure in recommendation systems is drawn to discuss the importance of social diversity exposure and design directions for social recommender systems for building social capital are presented.
Accelerating Innovation Through Analogy Mining
TLDR
This paper explores the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose, and combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions.
Finding Cultural Holes: How Structure and Culture Diverge in Networks of Scholarly Communication
Divergent interests, expertise, and language form cultural barriers to communication. No formalism has been available to characterize these “cultural holes.” Here we use information theory to measure
Exploring the filter bubble: the effect of using recommender systems on content diversity
TLDR
This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users and contributes a novel metric to measure content diversity based on information encoded in user-generated tags, and presents a new set of methods to examine the temporal effect of recommender systems on the user experience.
...
...