Abstract Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, that is, comments. A news outlet may allow users to contribute comments on all their articles or a selected number of them. The topic of an article may lead to an apathetic user commenting activity (several tens of comments) or to a spontaneous fervent one (several thousands of comments). This environment creates a social dynamic that is little studied. The social dynamics around articles have the potential to reveal interesting facets of the user population at a news outlet. In this paper, we report the salient findings about these social media from 15 months worth of data collected from 17 news outlets comprising of over 38,000 news articles and about 21 million user comments. Analysis of the data reveals interesting insights such as there is an uneven relationship between news outlets and their user populations across outlets. Such observations and others have not been revealed, to our knowledge. We believe our analysis in this paper can contribute to news predictive analytics (e.g., user reaction to a news article or predicting the volume of comments posted to an article). This article is categorized under:Internet > Society and CultureEnsemble Methods > Web MiningFundamental Concepts of Data and Knowledge > Human Centricity and User Interaction 
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                            A biomedical open knowledge network harnesses the power of AI to understand deep human biology
                        
                    
    
            Abstract Knowledge representation and reasoning (KR&R) has been successfully implemented in many fields to enable computers to solve complex problems with AI methods. However, its application to biomedicine has been lagging in part due to the daunting complexity of molecular and cellular pathways that govern human physiology and pathology. In this article, we describe concrete uses of Scalable PrecisiOn Medicine Knowledge Engine (SPOKE), an open knowledge network that connects curated information from thirty‐seven specialized and human‐curated databases into a single property graph, with 3 million nodes and 15 million edges to date. Applications discussed in this article include drug discovery, COVID‐19 research and chronic disease diagnosis, and management. 
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                            - Award ID(s):
- 2033569
- PAR ID:
- 10366726
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AI Magazine
- Volume:
- 43
- Issue:
- 1
- ISSN:
- 0738-4602
- Page Range / eLocation ID:
- p. 46-58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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