16,209 research outputs found
Tracing Networks of Knowledge in the Digital Age
The emergence of new digital technologies has allowed the study of human
behaviour at a scale and at level of granularity that were unthinkable just a
decade ago. In particular, by analysing the digital traces left by people
interacting in the online and offline worlds, we are able to trace the
spreading of knowledge and ideas at both local and global scales. In this
article we will discuss how these digital traces can be used to map knowledge
across the world, outlining both the limitations and the challenges in
performing this type of analysis. We will focus on data collected from social
media platforms, large-scale digital repositories and mobile data. Finally, we
will provide an overview of the tools that are available to scholars and
practitioners for understanding these processes using these emerging forms of
data.Comment: 8 pages. In Proceedings of the British Academy. Accepted for
Publication. To Appear. 201
Literature Survey on Interplay of Topics, Information Diffusion and Connections on Social Networks
Researchers have attempted to model information diffusion and topic trends
and lifecycle on online social networks. They have investigated the role of
content, social connections and communities, familiarity and behavioral
similarity in this context. The current article presents a survey of
representative models that perform topic analysis, capture information
diffusion, and explore the properties of social connections in the context of
online social networks. The article concludes with a set of outlines of open
problems and possible directions of future research interest. This article is
intended for researchers to identify the current literature, and explore
possibilities to improve the art
Modeling Information Diffusion in Online Social Networks with Partial Differential Equations
Online social networks such as Twitter and Facebook have gained tremendous
popularity for information exchange. The availability of unprecedented amounts
of digital data has accelerated research on information diffusion in online
social networks. However, the mechanism of information spreading in online
social networks remains elusive due to the complexity of social interactions
and rapid change of online social networks. Much of prior work on information
diffusion over online social networks has based on empirical and statistical
approaches. The majority of dynamical models arising from information diffusion
over online social networks involve ordinary differential equations which only
depend on time. In a number of recent papers, the authors propose to use
partial differential equations(PDEs) to characterize temporal and spatial
patterns of information diffusion over online social networks. Built on
intuitive cyber-distances such as friendship hops in online social networks,
the reaction-diffusion equations take into account influences from various
external out-of-network sources, such as the mainstream media, and provide a
new analytic framework to study the interplay of structural and topical
influences on information diffusion over online social networks. In this
survey, we discuss a number of PDE-based models that are validated with real
datasets collected from popular online social networks such as Digg and
Twitter. Some new developments including the conservation law of information
flow in online social networks and information propagation speeds based on
traveling wave solutions are presented to solidify the foundation of the PDE
models and highlight the new opportunities and challenges for mathematicians as
well as computer scientists and researchers in online social networks
Who creates trends in online social media: The crowd or opinion leaders?
Trends in online social media always reflect the collective attention of a
vast number of individuals across the network. For example, Internet slang
words can be ubiquitous because of social memes and online contagions in an
extremely short period. From Weibo, a Twitter-like service in China, we find
that the adoption of popular Internet slang words experiences two peaks in its
temporal evolution, in which the former is relatively much lower than the
latter. This interesting phenomenon in fact provides a decent window to
disclose essential factors that drive the massive diffusion underlying trends
in online social media. Specifically, the in-depth comparison between
diffusions represented by different peaks suggests that more attention from the
crowd at early stage of the propagation produces large-scale coverage, while
the dominant participation of opinion leaders at the early stage just leads to
popularity of small scope. Our results quantificationally challenge the
conventional hypothesis of influentials. And the implications of these novel
findings for marketing practice and influence maximization in social networks
are also discussed
Semantic Place Descriptors for Classification and Map Discovery
Urban environments develop complex, non-obvious structures that are often
hard to represent in the form of maps or guides. Finding the right place to go
often requires intimate familiarity with the location in question and cannot
easily be deduced by visitors. In this work, we exploit large-scale samples of
usage information, in the form of mobile phone traces and geo-tagged Twitter
messages in order to automatically explore and annotate city maps via kernel
density estimation. Our experiments are based on one year's worth of mobile
phone activity collected by Nokia's Mobile Data Challenge (MDC). We show that
usage information can be a strong predictor of semantic place categories,
allowing us to automatically annotate maps based on the behavior of the local
user base.Comment: 13 pages, 1 figure, 1 tabl
Measuring user influence on Twitter: A survey
Centrality is one of the most studied concepts in social network analysis.
There is a huge literature regarding centrality measures, as ways to identify
the most relevant users in a social network. The challenge is to find measures
that can be computed efficiently, and that can be able to classify the users
according to relevance criteria as close as possible to reality. We address
this problem in the context of the Twitter network, an online social networking
service with millions of users and an impressive flow of messages that are
published and spread daily by interactions between users. Twitter has different
types of users, but the greatest utility lies in finding the most influential
ones. The purpose of this article is to collect and classify the different
Twitter influence measures that exist so far in literature. These measures are
very diverse. Some are based on simple metrics provided by the Twitter API,
while others are based on complex mathematical models. Several measures are
based on the PageRank algorithm, traditionally used to rank the websites on the
Internet. Some others consider the timeline of publication, others the content
of the messages, some are focused on specific topics, and others try to make
predictions. We consider all these aspects, and some additional ones.
Furthermore, we include measures of activity and popularity, the traditional
mechanisms to correlate measures, and some important aspects of computational
complexity for this particular context.Comment: 33 pages, 3 tables, 3 figures, Information Processing & Management
(2016
Sample NLPDE and NLODE Social-Media Modeling of Information Transmission for Infectious Diseases:Case Study Ebola
We investigate the spreading of information through Twitter messaging related
to the spread of Ebola in western Africa using epidemic based dynamic models.
Diffusive spreading leads to NLPDE models and fixed point analysis yields
systems of NLODE models. When tweets are mapped as connected nodes in a graph
and are treated as a time sequenced Markov chain, TSMC, then by the Kurtz
theorem these specific paths can be identified as being near solutions to
systems of ordinary differential equations that in the large N limit retain
many of the features of the original Tweet dynamics. Constraints on the model
related to Tweet and re-Tweet rates lead to different versions of the system of
equations. We use Ebola Twitter meme based data to investigate a modified four
parameter model and apply the resulting fit to an accuracy metric for a set of
Ebola memes. In principle the temporal and spatial evolution equations
describing the propagation of the Twitter based memes can help ascertain and
inform decision makers on the nature of the spreading and containment of an
epidemic of this type.Comment: 14 pages, 4 tables, 2 figure
Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities
Nearly all previous work on geo-locating latent states and activities from
social media confounds general discussions about activities, self-reports of
users participating in those activities at times in the past or future, and
self-reports made at the immediate time and place the activity occurs.
Activities, such as alcohol consumption, may occur at different places and
types of places, and it is important not only to detect the local regions where
these activities occur, but also to analyze the degree of participation in them
by local residents. In this paper, we develop new machine learning based
methods for fine-grained localization of activities and home locations from
Twitter data. We apply these methods to discover and compare alcohol
consumption patterns in a large urban area, New York City, and a more suburban
and rural area, Monroe County. We find positive correlations between the rate
of alcohol consumption reported among a community's Twitter users and the
density of alcohol outlets, demonstrating that the degree of correlation varies
significantly between urban and suburban areas. While our experiments are
focused on alcohol use, our methods for locating homes and distinguishing
temporally-specific self-reports are applicable to a broad range of behaviors
and latent states.Comment: 12 pages, 7 figures, 4-page poster version accepted at ICWSM 2016,
alcohol dataset and keywords available in:
cs.rochester.edu/u/nhossain/icwsm-16-data.zi
Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams
The local event detection is to use posting messages with geotags on social
networks to reveal the related ongoing events and their locations. Recent
studies have demonstrated that the geo-tagged tweet stream serves as an
unprecedentedly valuable source for local event detection. Nevertheless, how to
effectively extract local events from large geo-tagged tweet streams in real
time remains challenging. A robust and efficient cloud-based real-time local
event detection software system would benefit various aspects in the real-life
society, from shopping recommendation for customer service providers to
disaster alarming for emergency departments. We use the preliminary research
GeoBurst as a starting point, which proposed a novel method to detect local
events. GeoBurst+ leverages a novel cross-modal authority measure to identify
several pivots in the query window. Such pivots reveal different geo-topical
activities and naturally attract related tweets to form candidate events. It
further summarises the continuous stream and compares the candidates against
the historical summaries to pinpoint truly interesting local events. We mainly
implement a website demonstration system Event-Radar with an improved algorithm
to show the real-time local events online for public interests. Better still,
as the query window shifts, our method can update the event list with little
time cost, thus achieving continuous monitoring of the stream.Comment: 10 page
Angry Birds Flock Together: Aggression Propagation on Social Media
Cyberaggression has been found in various contexts and online social
platforms, and modeled on different data using state-of-the-art machine and
deep learning algorithms to enable automatic detection and blocking of this
behavior. Users can be influenced to act aggressively or even bully others
because of elevated toxicity and aggression in their own (online) social
circle. In effect, this behavior can propagate from one user and neighborhood
to another, and therefore, spread in the network. Interestingly, to our
knowledge, no work has modeled the network dynamics of aggressive behavior. In
this paper, we take a first step towards this direction, by studying
propagation of aggression on social media. We look into various opinion
dynamics models widely used to model how opinions propagate through a network.
We propose ways to enhance these classical models to accommodate how aggression
may propagate from one user to another, depending on how each user is connected
to other aggressive or regular users. Through extensive simulations on Twitter
data, we study how aggressive behavior could propagate in the network. We
validate the models with ground truth crawled and annotated data, reaching up
to 80% AUC. We discuss the results and implications of our work.Comment: 11 pages, 4 figures, 3 table
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