227,803 research outputs found
Social Network Analysis on Food Web and Dispute Data
Several social science disciplines, especially anthropology and sociology, have long engaged in social network analyses. Social Network Analysis (SNA) uses network theory to analyse social networks â a network that often involves individual social actors (people) and relations between them. Social network analysis aims at understanding the network structure by description, visualization, and statistical modeling. In this research, the illustration of the use of SNA is done on two different datasets: food web data and militarized interstate dispute data
Exploiting Text and Network Context for Geolocation of Social Media Users
Research on automatically geolocating social media users has conventionally
been based on the text content of posts from a given user or the social network
of the user, with very little crossover between the two, and no bench-marking
of the two approaches over compara- ble datasets. We bring the two threads of
research together in first proposing a text-based method based on adaptive
grids, followed by a hybrid network- and text-based method. Evaluating over
three Twitter datasets, we show that the empirical difference between text- and
network-based methods is not great, and that hybridisation of the two is
superior to the component methods, especially in contexts where the user graph
is not well connected. We achieve state-of-the-art results on all three
datasets
Multi-dimensional Conversation Analysis across Online Social Networks
With the advance of the Internet, ordinary users have created multiple
personal accounts on online social networks, and interactions among these
social network users have recently been tagged with location information. In
this work, we observe user interactions across two popular online social
networks, Facebook and Twitter, and analyze which factors lead to retweet/like
interactions for tweets/posts. In addition to the named entities, lexical
errors and expressed sentiments in these data items, we also consider the
impact of shared user locations on user interactions. In particular, we show
that geolocations of users can greatly affect which social network post/tweet
will be liked/ retweeted. We believe that the results of our analysis can help
researchers to understand which social network content will have better
visibility.Comment: Datasets will be anonymized and published at:
http://akcora.wordpress.com/2013/12/24/pointer-for-datasets
Bootstrapping opportunistic networks using social roles
Opportunistic routing protocols can enable message delivery in disconnected networks of mobile devices. To conserve energy in mobile environments, such routing protocols must minimise unnecessary message-forwarding. This paper presents an opportunistic routing protocol that leverages social role information. We compute node roles from a social network graph to identify nodes with similar contact relationships, and use these roles to determine routing decisions. By using pre-existing social network information, such as online social network friends, to determine roles, we show that our protocol can bootstrap a new opportunistic network without the delay incurred by encounter-history-based routing protocols such as SimbetTS. Simulations with four real-world datasets show improved performance over SimbetTS, with performance approaching Epidemic routing in some scenarios.Postprin
The Social Network of Contemporary Popular Musicians
In this paper we analyze two social network datasets of contemporary
musicians constructed from allmusic.com (AMG), a music and artists' information
database: one is the collaboration network in which two musicians are connected
if they have performed in or produced an album together, and the other is the
similarity network in which they are connected if they where musically similar
according to music experts. We find that, while both networks exhibit typical
features of social networks such as high transitivity, several key network
features, such as degree as well as betweenness distributions suggest
fundamental differences in music collaborations and music similarity networks
are created.Comment: 7 pages, 2 figure
Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Enormous online textual information provides intriguing opportunities for
understandings of social and economic semantics. In this paper, we propose a
novel text regression model based on a conditional generative adversarial
network (GAN), with an attempt to associate textual data and social outcomes in
a semi-supervised manner. Besides promising potential of predicting
capabilities, our superiorities are twofold: (i) the model works with
unbalanced datasets of limited labelled data, which align with real-world
scenarios; and (ii) predictions are obtained by an end-to-end framework,
without explicitly selecting high-level representations. Finally we point out
related datasets for experiments and future research directions
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