9,094 research outputs found
Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions: supplementary materials
These supplementary materials accompany the report ‘Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions’, which is the first report from research commissioned by Becta into Web 2.0 technologies for learning at Key Stages 3 and 4. This report describes findings from the commissioned literature review of the then current landscape concerning learner use of Web 2.0 technologies and the implications for teachers, schools, local authorities and policy makers
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Mathematical practice, crowdsourcing, and social machines
The highest level of mathematics has traditionally been seen as a solitary
endeavour, to produce a proof for review and acceptance by research peers.
Mathematics is now at a remarkable inflexion point, with new technology
radically extending the power and limits of individuals. Crowdsourcing pulls
together diverse experts to solve problems; symbolic computation tackles huge
routine calculations; and computers check proofs too long and complicated for
humans to comprehend.
Mathematical practice is an emerging interdisciplinary field which draws on
philosophy and social science to understand how mathematics is produced. Online
mathematical activity provides a novel and rich source of data for empirical
investigation of mathematical practice - for example the community question
answering system {\it mathoverflow} contains around 40,000 mathematical
conversations, and {\it polymath} collaborations provide transcripts of the
process of discovering proofs. Our preliminary investigations have demonstrated
the importance of "soft" aspects such as analogy and creativity, alongside
deduction and proof, in the production of mathematics, and have given us new
ways to think about the roles of people and machines in creating new
mathematical knowledge. We discuss further investigation of these resources and
what it might reveal.
Crowdsourced mathematical activity is an example of a "social machine", a new
paradigm, identified by Berners-Lee, for viewing a combination of people and
computers as a single problem-solving entity, and the subject of major
international research endeavours. We outline a future research agenda for
mathematics social machines, a combination of people, computers, and
mathematical archives to create and apply mathematics, with the potential to
change the way people do mathematics, and to transform the reach, pace, and
impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent
Computer Mathematics, CICM 2013, July 2013 Bath, U
GOING GAGA: POP FANDOM AS ONLINE COMMUNITY OF PRACTICE
Among various fan sites dedicated to pop stars, GagaDaily is one prominent online collective that centers around Lady Gaga. This study is a piece of ethnographic research focused on two claims – GagaDaily constitutes a Community of Practice (Eckert, 2006) in an online setting, and the regular use of humor by users fulfills social and pragmatic roles in the discourse. Communicative phenomena (both textual and graphic) that characterize the linguistic repertoire of GagaDaily members were catalogued from the first 100 pages of one thread within the forums. These data were grouped into categories corresponding to different dimensions of language use as well as media/literary devices. Alongside a quantitative analysis of various tokens and types of data, a qualitative examination of selected excerpts from the sample confirm the veracity of the two main claims. When analyzed with regard to Wenger’s definition of a Community of Practice (Wenger, 2009), GagaDaily meets all three of his requirements. Likewise, the analysis of humor reveal that GagaDaily users regularly engage in the first dichotomy of the tactics of intersubjectivity, adequation and distinction (Bucholtz & Hall, 2004) and incorporate GIF images in their humor to express their alignment with stance objects (DuBois, 2007) and other members
Modeling Human Group Behavior In Virtual Worlds
Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether iii people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics
Prediction of user behaviour on the web
The Web has become an ubiquitous environment for human interaction, communication,
and data sharing. As a result, large amounts of data are produced. This
data can be utilised by building predictive models of user behaviour in order to support
business decisions. However, the fast pace of modern businesses is creating the
pressure on industry to provide faster and better decisions. This thesis addresses
this challenge by proposing a novel methodology for an effcient prediction of user
behaviour. The problems concerned are: (i) modelling user behaviour on the Web,
(ii) choosing and extracting features from data generated by user behaviour, and
(iii) choosing a Machine Learning (ML) set-up for an effcient prediction.
First, a novel Time-Varying Attributed Graph (TVAG) is introduced and
then a TVAG-based model for modelling user behaviour on the Web is proposed.
TVAGs capture temporal properties of user behaviour by their time varying component
of features of the graph nodes and edges. Second, the proposed model allows
to extract features for further ML predictions. However, extracting the features and
building the model may be unacceptably hard and long process. Thus, a guideline
for an effcient feature extraction from the TVAG-based model is proposed. Third,
a method for choosing a ML set-up to build an accurate and fast predictive model
is proposed and evaluated. Finally, a deep learning architecture for predicting user
behaviour on the Web is proposed and evaluated.
To sum up, the main contribution to knowledge of this work is in developing
the methodology for fast and effcient predictions of user behaviour on the Web.
The methodology is evaluated on datasets from a few Web platforms, namely Stack
Exchange, Twitter, and Facebook
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