Duh, Kevin (NTT Communication Science Labs / NAIST) | Hirao, Tsutomu (NTT Communication Science Labs / NAIST) | Kimura, Akisato (NTT Communication Science Labs / NAIST) | Ishiguro, Katsuhiko (NTT Communication Science Labs / NAIST) | Iwata, Tomoharu (NTT Communication Science Labs / NAIST) | Yeung, Ching-Man Au (NTT Communication Science Labs / NAIST)
Social media has become ubiquitous. Tweets and other user-generated content have become so abundant that better tools for information organization are needed in order to fully exploit their potential richness. ”Social cu- ration” has recently emerged as a promising new frame- work for organizing and adding value to social media, complementing the traditional methods of algorithmic search and aggregation. For example, web services like Togetter and Storify empower users to collect and or- ganize tweets to form stories that are pertinent, mem- orable, and easy to read. While social curation services are gaining popularity, little academic research has stud- ied the phenomenon. In this work, we perform one of the first analysis of a large corpus of social curation data. We seek to understand why and how people cu- rate tweets. We also propose an machine learning sys- tem that suggests new tweets, increasing the curator’s productivity and breadth of perspective.
This paper looks at how and why users categorise and curate content into collections online, using datasets containing nearly all the relevant activities from Pinterest.com during January 2013, and Last.fm in December 2012. In addition, a user survey of over 25 Pinterest and 250 Last.fm users is used to obtain insights into the motivations for content curation and corroborate results. The data reveal that curation tends to focus on items that may not rank highly in popularity and search rankings. Yet, curated items exhibit their own skewed popularity, with the top few items receiving most of the attention; indicative of a synchronised community. We distinguish structured curation by active categorisation from a more passive bookmarking by `liking' an item, and find the former more prevalent for popularly curated items. Likes, however, are initially accumulated at a faster pace. Finally, we study the social value of content curation and show that curators attract more followers with consistent activity, and diversity of interests. Interestingly, our user study indicates a divided opinion on the relevance of the social network.
In this paper first I introduce curation and affordance in chance discovery. According to Matsumura's definition, a shikake is a trigger to start a certain action or to change person's mind and behaviour. As a result of the action, all or part of problem will be solved. A chance and shikake are in a certain sense similar. In addition, affrodance seems to play a significant role in shikakeology. From the point I will discuss the relationships between chance discovery and Shikakeology.
Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date. There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity. Purpose NLM wishes to accelerate the availability of and access to secure, complete data sets and computational models that can serve as the basis of transformative biomedical discoveries by improving the speed and scope of the curation processes. This Funding Opportunity Announcement is focused on automating curation of biomedical digital assets in support of Goal 1. Objective 1.1 of the NLM Strategic Plan 2017-2027: "An important research direction will develop strategies for curation at scale…".