3,842 research outputs found
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
A New Analysis Method for Simulations Using Node Categorizations
Most research concerning the influence of network structure on phenomena
taking place on the network focus on relationships between global statistics of
the network structure and characteristic properties of those phenomena, even
though local structure has a significant effect on the dynamics of some
phenomena. In the present paper, we propose a new analysis method for phenomena
on networks based on a categorization of nodes. First, local statistics such as
the average path length and the clustering coefficient for a node are
calculated and assigned to the respective node. Then, the nodes are categorized
using the self-organizing map (SOM) algorithm. Characteristic properties of the
phenomena of interest are visualized for each category of nodes. The validity
of our method is demonstrated using the results of two simulation models. The
proposed method is useful as a research tool to understand the behavior of
networks, in particular, for the large-scale networks that existing
visualization techniques cannot work well.Comment: 9 pages, 8 figures. This paper will be published in Social Network
Analysis and Mining(www.springerlink.com
Using Twitter to learn about the autism community
Considering the raising socio-economic burden of autism spectrum disorder
(ASD), timely and evidence-driven public policy decision making and
communication of the latest guidelines pertaining to the treatment and
management of the disorder is crucial. Yet evidence suggests that policy makers
and medical practitioners do not always have a good understanding of the
practices and relevant beliefs of ASD-afflicted individuals' carers who often
follow questionable recommendations and adopt advice poorly supported by
scientific data. The key goal of the present work is to explore the idea that
Twitter, as a highly popular platform for information exchange, could be used
as a data-mining source to learn about the population affected by ASD -- their
behaviour, concerns, needs etc. To this end, using a large data set of over 11
million harvested tweets as the basis for our investigation, we describe a
series of experiments which examine a range of linguistic and semantic aspects
of messages posted by individuals interested in ASD. Our findings, the first of
their nature in the published scientific literature, strongly motivate
additional research on this topic and present a methodological basis for
further work.Comment: Social Network Analysis and Mining, 201
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
201
Tweet, but Verify: Epistemic Study of Information Verification on Twitter
While Twitter provides an unprecedented opportunity to learn about breaking
news and current events as they happen, it often produces skepticism among
users as not all the information is accurate but also hoaxes are sometimes
spread. While avoiding the diffusion of hoaxes is a major concern during
fast-paced events such as natural disasters, the study of how users trust and
verify information from tweets in these contexts has received little attention
so far. We survey users on credibility perceptions regarding witness pictures
posted on Twitter related to Hurricane Sandy. By examining credibility
perceptions on features suggested for information verification in the field of
Epistemology, we evaluate their accuracy in determining whether pictures were
real or fake compared to professional evaluations performed by experts. Our
study unveils insight about tweet presentation, as well as features that users
should look at when assessing the veracity of tweets in the context of
fast-paced events. Some of our main findings include that while author details
not readily available on Twitter feeds should be emphasized in order to
facilitate verification of tweets, showing multiple tweets corroborating a fact
misleads users to trusting what actually is a hoax. We contrast some of the
behavioral patterns found on tweets with literature in Psychology research.Comment: Pre-print of paper accepted to Social Network Analysis and Mining
(Springer
- …