A framework for understanding online group behaviors during a catastrophic event

2020 | International Journal of Information Management | Citations: 3

Authors: Kim, Jooho; Park, Hogun

Abstract: This study investigated the underlying mechanisms of online social media group b ... Expand

Abstract: This study investigated the underlying mechanisms of online social media group behaviors in an emergency. The proposed framework was designed to analyze group behaviors/interactions and examine the main topics of interest among numerous tweets generated in an emergency. We collected tweets sent during Hurricane Harvey in 2017 and applied the framework to demonstrate its effectiveness. The proposed framework enables us to understand the unique characteristics of group interactions and develop operational strategies to effectively communicate with the public, as well as other groups, as critical emergency information appears in an online social network. Collapse

Topics: Twitter hurricane social media social network emergency management

Methods: social network analysis topic model term frequency–inverse document frequency data transformation latent dirichlet allocation

Theories: social exchange theory communication theory

Excavating the Treasure of IoT Data: An Architecture to Empower Rapid Data Analytics for Predictive Maintenance of Connected Vehicles

2017 | International Conference on Information Systems | Citations: 4

Authors: Gerloff, Christian; Cleophas, Catherine

Abstract: The Internet of Things (IoT) challenges companies to reinforce their digital str ... Expand

Abstract: The Internet of Things (IoT) challenges companies to reinforce their digital strategy. For example, connected vehicles shape the future of the automotive industry by enabling new services, which will become integral to vehicles’ value. By applying predictive maintenance, after-sales services can prevent forced outages. This requires a digital service platform that enables predictive maintenance. For such a platform, we deduce requirements to overcome the inherent challenges of IoT data. Based on these, we propose a suitable architecture, particularly focusing on data quality criteria by developing several pipelines for data cleaning. We exemplify the platform's value creation via its data analytics functions. Thus, this paper demonstrates excavating the treasure of IoT data and outlines a transferable architecture to empower rapid data analytics for better business decision making and enabling business innovations. Collapse

Topics: predictive maintenance digital service analytical information system data quality big data

Methods: machine learning design science computational algorithm machine learning method unsupervised learning