18,761 research outputs found
Of course we share! Testing Assumptions about Social Tagging Systems
Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
'Girlfriends and Strawberry Jamâ: Tagging Memories, Experiences, and Events for Future Retrieval
In this short paper we have some preliminary thoughts about tagging everyday life events in order to allow future retrieval of events or experiences related to events. Elaboration of these thoughts will be done in the context of the recently started Network of Excellence PetaMedia (Peer-to-Peer Tagged Media) and the Network of Excellence SSPNet (Social Signal Processing), to start in 2009, both funded by the European Commission's Seventh Framework Programme. Descriptions of these networks will be given later in this paper
The role of social networks in studentsâ learning experiences
The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in studentsâ learning experiences. The construction of studentsâ social networks, the evolution of these networks, and their effects on the studentsâ learning experience in a university environment are examined
Semantic Stability in Social Tagging Streams
One potential disadvantage of social tagging systems is that due to the lack
of a centralized vocabulary, a crowd of users may never manage to reach a
consensus on the description of resources (e.g., books, users or songs) on the
Web. Yet, previous research has provided interesting evidence that the tag
distributions of resources may become semantically stable over time as more and
more users tag them. At the same time, previous work has raised an array of new
questions such as: (i) How can we assess the semantic stability of social
tagging systems in a robust and methodical way? (ii) Does semantic
stabilization of tags vary across different social tagging systems and
ultimately, (iii) what are the factors that can explain semantic stabilization
in such systems? In this work we tackle these questions by (i) presenting a
novel and robust method which overcomes a number of limitations in existing
methods, (ii) empirically investigating semantic stabilization processes in a
wide range of social tagging systems with distinct domains and properties and
(iii) detecting potential causes for semantic stabilization, specifically
imitation behavior, shared background knowledge and intrinsic properties of
natural language. Our results show that tagging streams which are generated by
a combination of imitation dynamics and shared background knowledge exhibit
faster and higher semantic stability than tagging streams which are generated
via imitation dynamics or natural language streams alone
What to do about non-standard (or non-canonical) language in NLP
Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered standard, most prominently English newswire. However, there are
many dimensions, e.g., socio-demographics, language, genre, sentence type, etc.
on which texts can differ from the standard. The solution is not obvious: we
cannot control for all factors, and it is not clear how to best go beyond the
current practice of training on homogeneous data from a single domain and
language.
In this paper, I review the notion of canonicity, and how it shapes our
community's approach to language. I argue for leveraging what I call fortuitous
data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight,
or raw data that needs to be refined. If we embrace the variety of this
heterogeneous data by combining it with proper algorithms, we will not only
produce more robust models, but will also enable adaptive language technology
capable of addressing natural language variation.Comment: KONVENS 201
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