1,201 research outputs found
Community-Oriented Models and Applications for the Social Web
The past few years have seen the rapid rise of all things "social" on the web from the growth of online social networks like Facebook, to user-contributed content sites
like Flickr and YouTube, to social bookmarking services like Delicious, among many others. Whereas traditional approaches to organizing and accessing the web’s massive
amount of information have focused on content-based and link-based approaches, these social systems offer rich opportunities for user-based and community-based exploration and analysis of the web by building on the unprecedented access to the interests and perspectives of millions of users.
We focus here on the challenge of modeling and mining social bookmarking systems, in which resources are enriched by large-scale socially generated metadata
(“tags”) and contextualized by the user communities that are associated with the resources. Our hypothesis is that an underlying social collective intelligence is embedded
in the uncoordinated actions of users on social bookmarking services, and that this social collective intelligence can be leveraged for enhanced web-based information discovery and knowledge sharing. Concretely, we posit the existence of underlying implicit communities in these social bookmarking systems that drive the social bookmarking process which can provide a foundation for community-based organization of web resources.
To that end, we make three contributions:
• First, we propose a pair of novel probabilistic generative models for describing and modeling community-oriented social bookmarking. We show how these
models enable effective extraction of meaningful communities over large real world social bookmarking services.
• Second, we develop two frameworks for community-based web information browsing and search that are based on these community-oriented social bookmarking models. We show how both achieve improved discovery and exploration of the social web.
• Third, we introduce a community evolution framework for studying and analyzing social bookmarking communities over time. We explore the temporal dimension of social bookmarking and explore the dynamics of community formation, evolution, and dissolution. By uncovering implicit communities, putting them to use in an application scenario (search and browsing), and analyzing them over time, this dissertation provides a foundation for the study of how social knowledge networks are self-organized, a deeper understanding and appreciation of the factors impacting collective intelligence, and the creation of new information access algorithms for leveraging these communities
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Link creation and profile alignment in the aNobii social network
The present work investigates the structural and dynamical properties of
aNobii\footnote{http://www.anobii.com/}, a social bookmarking system designed
for readers and book lovers. Users of aNobii provide information about their
library, reading interests and geographical location, and they can establish
typed social links to other users. Here, we perform an in-depth analysis of the
system's social network and its interplay with users' profiles. We describe the
relation of geographic and interest-based factors to social linking.
Furthermore, we perform a longitudinal analysis to investigate the interplay of
profile similarity and link creation in the social network, with a focus on
triangle closure. We report a reciprocal causal connection: profile similarity
of users drives the subsequent closure in the social network and, reciprocally,
closure in the social network induces subsequent profile alignment. Access to
the dynamics of the social network also allows us to measure quantitative
indicators of preferential linking.Comment: http://www.iisocialcom.org/conference/socialcom2010
Competition and Success in the Meme Pool: a Case Study on Quickmeme.com
The advent of social media has provided data and insights about how people
relate to information and culture. While information is composed by bits and
its fundamental building bricks are relatively well understood, the same cannot
be said for culture. The fundamental cultural unit has been defined as a
"meme". Memes are defined in literature as specific fundamental cultural
traits, that are floating in their environment together. Just like genes
carried by bodies, memes are carried by cultural manifestations like songs,
buildings or pictures. Memes are studied in their competition for being
successfully passed from one generation of minds to another, in different ways.
In this paper we choose an empirical approach to the study of memes. We
downloaded data about memes from a well-known website hosting hundreds of
different memes and thousands of their implementations. From this data, we
empirically describe the behavior of these memes. We statistically describe
meme occurrences in our dataset and we delineate their fundamental traits,
along with those traits that make them more or less apt to be successful
The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics
The presence of web-based communities is a distinctive signature of Web 2.0. The web-based feature means that information propagation within each community is highly facilitated, promoting complex collective dynamics in view of information exchange. In this work, we focus on a community of scientists and study, in particular, how the awareness of a scientific paper is spread. Our work is based on the web usage statistics obtained from the PLoS Article Level Metrics dataset compiled by PLoS. The cumulative number of HTML views was found to follow a long tail distribution which is reasonably well-fitted by a lognormal one. We modeled the diffusion of information by a random multiplicative process, and thus extracted the rates of information spread at different stages after the publication of a paper. We found that the spread of information displays two distinct decay regimes: a rapid downfall in the first month after publication, and a gradual power law decay afterwards. We identified these two regimes with two distinct driving processes: a short-term behavior driven by the fame of a paper, and a long-term behavior consistent with citation statistics. The patterns of information spread were found to be remarkably similar in data from different journals, but there are intrinsic differences for different types of web usage (HTML views and PDF downloads versus XML). These similarities and differences shed light on the theoretical understanding of different complex systems, as well as a better design of the corresponding web applications that is of high potential marketing impact
Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices
Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook
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