118,571 research outputs found
Traveling Trends: Social Butterflies or Frequent Fliers?
Trending topics are the online conversations that grab collective attention
on social media. They are continually changing and often reflect exogenous
events that happen in the real world. Trends are localized in space and time as
they are driven by activity in specific geographic areas that act as sources of
traffic and information flow. Taken independently, trends and geography have
been discussed in recent literature on online social media; although, so far,
little has been done to characterize the relation between trends and geography.
Here we investigate more than eleven thousand topics that trended on Twitter in
63 main US locations during a period of 50 days in 2013. This data allows us to
study the origins and pathways of trends, how they compete for popularity at
the local level to emerge as winners at the country level, and what dynamics
underlie their production and consumption in different geographic areas. We
identify two main classes of trending topics: those that surface locally,
coinciding with three different geographic clusters (East coast, Midwest and
Southwest); and those that emerge globally from several metropolitan areas,
coinciding with the major air traffic hubs of the country. These hubs act as
trendsetters, generating topics that eventually trend at the country level, and
driving the conversation across the country. This poses an intriguing
conjecture, drawing a parallel between the spread of information and diseases:
Do trends travel faster by airplane than over the Internet?Comment: Proceedings of the first ACM conference on Online social networks,
pp. 213-222, 201
Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
How are topics born? Understanding the research dynamics preceding the emergence of new areas
The ability to promptly recognise new research trends is strategic for many stake- holders, including universities, institutional funding bodies, academic publishers and companies. While the literature describes several approaches which aim to identify the emergence of new research topics early in their lifecycle, these rely on the assumption that the topic in question is already associated with a number of publications and consistently referred to by a community of researchers. Hence, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. In this paper, we begin to address this challenge by performing a study of the dynamics preceding the creation of new topics. This study indicates that the emergence of a new topic is anticipated by a significant increase in the pace of collaboration between relevant research areas, which can be seen as the ‘parents’ of the new topic. These initial findings (i) confirm our hypothesis that it is possible in principle to detect the emergence of a new topic at the embryonic stage, (ii) provide new empirical evidence supporting relevant theories in Philosophy of Science, and also (iii) suggest that new topics tend to emerge in an environment in which weakly interconnected research areas begin to cross-fertilise
Understanding research dynamics
Rexplore leverages novel solutions in data mining, semantic technologies and visual analytics, and provides an innovative environment for exploring and making sense of scholarly data. Rexplore allows users: 1) to detect and make sense of important trends in research; 2) to identify a variety of interesting relations between researchers, beyond the standard co-authorship relations provided by most other systems; 3) to perform fine-grained expert search with respect to detailed multi-dimensional parameters; 4) to detect and characterize the dynamics of interesting communities of researchers, identified on the basis of shared research interests and scientific trajectories; 5) to analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities
Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network
We explore the hypothesis that it is possible to obtain information about the
dynamics of a blog network by analysing the temporal relationships between
blogs at a semantic level, and that this type of analysis adds to the knowledge
that can be extracted by studying the network only at the structural level of
URL links. We present an algorithm to automatically detect fine-grained
discussion topics, characterized by n-grams and time intervals. We then propose
a probabilistic model to estimate the temporal relationships that blogs have
with one another. We define the precursor score of blog A in relation to blog B
as the probability that A enters a new topic before B, discounting the effect
created by asymmetric posting rates. Network-level metrics of precursor and
laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs. The scores
are compared to traditional link degree metrics. We obtain insights into the
dynamics of topic participation on this network, as well as the relationship
between precursor/laggard and linking behaviors. We validate and analyze
results with the help of an expert on the French blogosphere. Finally, we
propose possible applications to the improvement of search engine ranking
algorithms
Interests Diffusion in Social Networks
Understanding cultural phenomena on Social Networks (SNs) and exploiting the
implicit knowledge about their members is attracting the interest of different
research communities both from the academic and the business side. The
community of complexity science is devoting significant efforts to define laws,
models, and theories, which, based on acquired knowledge, are able to predict
future observations (e.g. success of a product). In the mean time, the semantic
web community aims at engineering a new generation of advanced services by
defining constructs, models and methods, adding a semantic layer to SNs. In
this context, a leapfrog is expected to come from a hybrid approach merging the
disciplines above. Along this line, this work focuses on the propagation of
individual interests in social networks. The proposed framework consists of the
following main components: a method to gather information about the members of
the social networks; methods to perform some semantic analysis of the Domain of
Interest; a procedure to infer members' interests; and an interests evolution
theory to predict how the interests propagate in the network. As a result, one
achieves an analytic tool to measure individual features, such as members'
susceptibilities and authorities. Although the approach applies to any type of
social network, here it is has been tested against the computer science
research community.
The DBLP (Digital Bibliography and Library Project) database has been elected
as test-case since it provides the most comprehensive list of scientific
production in this field.Comment: 30 pages 13 figs 4 table
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