154,686 research outputs found
Content Reuse and Interest Sharing in Tagging Communities
Tagging communities represent a subclass of a broader class of user-generated
content-sharing online communities. In such communities users introduce and tag
content for later use. Although recent studies advocate and attempt to harness
social knowledge in this context by exploiting collaboration among users,
little research has been done to quantify the current level of user
collaboration in these communities. This paper introduces two metrics to
quantify the level of collaboration: content reuse and shared interest. Using
these two metrics, this paper shows that the current level of collaboration in
CiteULike and Connotea is consistently low, which significantly limits the
potential of harnessing the social knowledge in communities. This study also
discusses implications of these findings in the context of recommendation and
reputation systems.Comment: 6 pages, 6 figures, AAAI Spring Symposium on Social Information
Processin
Providing behaviour awareness in collaborative project courses
Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version
Providing behaviour awareness in collaborative project courses
Several studies show that awareness mechanisms can contribute to enhance the collaboration process among students and the learning experiences during collaborative project courses. However, it is not clear what awareness information should be provided to whom, when it should be provided, and how to obtain and represent such information in an accurate and understandable way. Regardless the research efforts done in this area, the problem remains open. By recognizing the diversity of work scenarios (contexts) where the collaboration may occur, this research proposes a behaviour awareness mechanism to support collaborative work in undergraduate project courses. Based on the authors previous experiences and the literature in the area, the proposed mechanism considers personal and social awareness components, which represent metrics in a visual way, helping students realize their performance, and lecturers intervene when needed. The trustworthiness of the mechanisms for determining the metrics was verified using empirical data, and the usability and usefulness of these metrics were evaluated with undergraduate students. Experimental results show that this awareness mechanism is useful, understandable and representative of the observed scenarios.Peer ReviewedPostprint (published version
Metrics for Analyzing Social Documents to Understand Joint Work
Social Collaboration Analytics (SCA) aims at measuring and interpreting communication and joint work on collaboration platforms and is a relatively new topic in the discipline of Information Systems. Previous applications of SCA are largely based on transactional data (event logs). In this paper, we propose a novel approach for the examination of collaboration based on the structure of social documents. Guided by the ontology for social business documents (SocDOnt) we develop metrics to measure collaboration around documents that provide traces of collaborative activity. For the evaluation, we apply these metrics to a large-scale collaboration platform. The findings show that group workspaces that support the same use case are characterized by a similar richness of their social documents (i.e. the number of components and contributing authors). We also show typical differences in the âcollaborativityâ of functional modules (containers)
OpenDigger: Data Mining and Information Service System for Open Collaboration Digital Ecosystem
The widespread development and adoption of open-source software have built an
ecosystem for open development and collaboration. In this ecosystem,
individuals and organizations collaborate to create high-quality software that
can be used by everyone. Social collaboration platforms like GitHub have
further facilitated large-scale, distributed, and fine-grained code
collaboration and technical interactions. Countless developers contribute code,
review code, report bugs, and propose new features on these platforms every
day, generating a massive amount of valuable behavioral data from the open
collaboration process. This paper presents the design and implementation of
OpenDigger, a comprehensive data mining and information service system for open
collaboration in the digital ecosystem. The goal is to build a data
infrastructure for the open-source domain and promote the continuous
development of the open-source ecosystem. The metrics and analysis models in
the OpenDigger system can mine various knowledge from the macro to micro levels
in the open-source digital ecosystem. Through a unified information service
interface, OpenDigger provides various open-source information services to
different user groups, including governments, enterprises, foundations, and
individuals. As a novel information service system in the open-source
ecosystem, this paper demonstrates the effectiveness of the metrics and models
in OpenDigger through several real-world scenarios, including products, tools,
applications, and courses. It showcases the significant and diverse practical
applications of the metrics and models in both algorithmic and business
aspects.Comment: in Chinese languag
Australian innovation system report 2011
Key points
Metrics and baseline indicators which track progress against the Governmentâs innovation priorities and targets â these metrics are presented under four themes: skills and research capacity, business innovation, links and collaboration and public sector and social innovation
Features and trends of the Australian innovation system and performance as a whole by comparing Australiaâs innovation performance to other OECD countries in areas such as framework conditions for entrepreneurship and innovation, the ways Australian firms innovate, investment in intangible capital, collaboration and skills used and shortages
Actions, achievements, and forward plans of various participants in the national innovation system, including governments, public sector researchers, and industry
On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts
Data-driven analysis of large social networks has attracted a great deal of
research interest. In this paper, we investigate 120 real social networks and
their measurement-calibrated synthetic counterparts generated by four
well-known network models. We investigate the structural properties of the
networks revealing the correlation profiles of graph metrics across various
social domains (friendship networks, communication networks, and collaboration
networks). We find that the correlation patterns differ across domains. We
identify a non-redundant set of metrics to describe social networks. We study
which topological characteristics of real networks the models can or cannot
capture. We find that the goodness-of-fit of the network models depends on the
domains. Furthermore, while 2K and stochastic block models lack the capability
of generating graphs with large diameter and high clustering coefficient at the
same time, they can still be used to mimic social networks relatively
efficiently.Comment: To appear in International Conference on Advances in Social Networks
Analysis and Mining (ASONAM '19), Vancouver, BC, Canad
Characterizing social media metrics of scholarly papers : the effect of document properties and collaboration patterns
A number of new metrics based on social media platformsâgrouped under the term âaltmetricsââhave recently been introduced as potential indicators of research impact. Despite
their current popularity, there is a lack of information regarding the determinants of these
metrics. Using publication and citation data from 1.3 million papers published in 2012 and
covered in Thomson Reutersâ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function
of document characteristics (i.e., discipline, document type, title length, number of pages
and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on
blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast,
66.8% of papers have received at least one citation. Our findings show that both citations
and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these
types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally,
contrary to what is observed for citations, it is papers in the Social Sciences and humanities
that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media
metrics cannot actually be seen as alternatives to citations; at most, they may function as
complements to other type of indicators
- âŠ