Research on Information Resilience

Paper 1: "Information Resilience: The Nexus of Responsible and Agile Approaches to Information Use"

Authors: Sadiq, S., Aryani, A., Demartini, G., Hua, W., Indulska, M., Burton-Jones, A., Khosravi, H., et al.

Published in: The VLDB Journal

Publication Date: 2022

This paper explores the concept of information resilience, which is the intersection of responsible and agile approaches to information use. The authors argue that organizations need to balance the need for responsible information practices with the need for agility in order to effectively respond to disruptions and uncertainties. They propose a framework for information resilience and discuss its implications for organizations.

Paper 2: "Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model Approach"

Authors: Samtani, S., Chai, Y., and Chen, H.

Published in: MIS Quarterly

Publication Date: In press

This upcoming paper focuses on proactive cyber threat intelligence and proposes a novel approach to linking exploits from the dark web to known vulnerabilities. The authors develop an attention-based deep structured semantic model that can effectively identify and connect relevant information from the dark web to known vulnerabilities. This approach aims to enhance organizations' ability to proactively detect and mitigate cyber threats.

Paper 3: "The Halo Effect in Multicomponent Ratings and its Implications for Recommender Systems: The Case Of Yahoo! Movies"

Authors: Sahoo, N., Krishnan, R., Duncan, G., and Callan, J.

Published in: Information Systems Research

Publication Date: 2012

This research note investigates the halo effect in multicomponent ratings and its implications for recommender systems, using the case of Yahoo! Movies. The authors find that users' overall ratings of movies are influenced by their ratings of specific components, such as plot, acting, and special effects. This halo effect can impact the accuracy of recommender systems that rely on overall ratings, and the authors suggest potential strategies to mitigate this effect.

Paper 4: "Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach"

Authors: Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., and Lee, K. C.

Published in: MIS Quarterly

Publication Date: 2020

This paper presents a deep learning approach to enhance social media analysis with visual data analytics. The authors propose a framework that combines deep learning techniques with visual data analytics to extract meaningful insights from social media data. They demonstrate the effectiveness of their approach through a case study on Twitter data. This research has implications for organizations seeking to leverage social media data for decision-making and strategic planning.

Overall, these four research papers contribute to the understanding of information resilience, proactive cyber threat intelligence, the halo effect in multicomponent ratings, and enhancing social media analysis with visual data analytics. Each paper offers unique insights and methodologies that can inform future research and practice in these areas.

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