359 research outputs found
Adaptive visualization of research communities
Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects
Exploratory Analysis of Pairwise Interactions in Online Social Networks
In the last few decades sociologists were trying to explain human behaviour
by analysing social networks, which requires access to data about interpersonal
relationships. This represented a big obstacle in this research field until the
emergence of online social networks (OSNs), which vastly facilitated the
process of collecting such data. Nowadays, by crawling public profiles on OSNs,
it is possible to build a social graph where "friends" on OSN become
represented as connected nodes. OSN connection does not necessarily indicate a
close real-life relationship, but using OSN interaction records may reveal
real-life relationship intensities, a topic which inspired a number of recent
researches. Still, published research currently lacks an extensive exploratory
analysis of OSN interaction records, i.e. a comprehensive overview of users'
interaction via different ways of OSN interaction. In this paper we provide
such an overview by leveraging results of conducted extensive social experiment
which managed to collect records for over 3,200 Facebook users interacting with
over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
population distributions and correlation parameters for 13 interaction
parameters, providing valuable insight in online social network interaction for
future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4
on pages 422 to 42
A semi-supervised approach to visualizing and manipulating overlapping communities
When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. © 2013 IEEE
On Facebook, most ties are weak
Pervasive socio-technical networks bring new conceptual and technological
challenges to developers and users alike. A central research theme is
evaluation of the intensity of relations linking users and how they facilitate
communication and the spread of information. These aspects of human
relationships have been studied extensively in the social sciences under the
framework of the "strength of weak ties" theory proposed by Mark Granovetter.13
Some research has considered whether that theory can be extended to online
social networks like Facebook, suggesting interaction data can be used to
predict the strength of ties. The approaches being used require handling
user-generated data that is often not publicly available due to privacy
concerns. Here, we propose an alternative definition of weak and strong ties
that requires knowledge of only the topology of the social network (such as who
is a friend of whom on Facebook), relying on the fact that online social
networks, or OSNs, tend to fragment into communities. We thus suggest
classifying as weak ties those edges linking individuals belonging to different
communities and strong ties as those connecting users in the same community. We
tested this definition on a large network representing part of the Facebook
social graph and studied how weak and strong ties affect the
information-diffusion process. Our findings suggest individuals in OSNs
self-organize to create well-connected communities, while weak ties yield
cohesion and optimize the coverage of information spread.Comment: Accepted version of the manuscript before ACM editorial work. Check
http://cacm.acm.org/magazines/2014/11/179820-on-facebook-most-ties-are-weak/
for the final versio
Quantifying the Effect of Sentiment on Information Diffusion in Social Media
Social media have become the main vehicle of information production and
consumption online. Millions of users every day log on their Facebook or
Twitter accounts to get updates and news, read about their topics of interest,
and become exposed to new opportunities and interactions. Although recent
studies suggest that the contents users produce will affect the emotions of
their readers, we still lack a rigorous understanding of the role and effects
of contents sentiment on the dynamics of information diffusion. This work aims
at quantifying the effect of sentiment on information diffusion, to understand:
(i) whether positive conversations spread faster and/or broader than negative
ones (or vice-versa); (ii) what kind of emotions are more typical of popular
conversations on social media; and, (iii) what type of sentiment is expressed
in conversations characterized by different temporal dynamics. Our findings
show that, at the level of contents, negative messages spread faster than
positive ones, but positive ones reach larger audiences, suggesting that people
are more inclined to share and favorite positive contents, the so-called
positive bias. As for the entire conversations, we highlight how different
temporal dynamics exhibit different sentiment patterns: for example, positive
sentiment builds up for highly-anticipated events, while unexpected events are
mainly characterized by negative sentiment. Our contribution is a milestone to
understand how the emotions expressed in short texts affect their spreading in
online social ecosystems, and may help to craft effective policies and
strategies for content generation and diffusion.Comment: 10 pages, 5 figure
Measuring Emotional Contagion in Social Media
Social media are used as main discussion channels by millions of individuals
every day. The content individuals produce in daily social-media-based
micro-communications, and the emotions therein expressed, may impact the
emotional states of others. A recent experiment performed on Facebook
hypothesized that emotions spread online, even in absence of non-verbal cues
typical of in-person interactions, and that individuals are more likely to
adopt positive or negative emotions if these are over-expressed in their social
network. Experiments of this type, however, raise ethical concerns, as they
require massive-scale content manipulation with unknown consequences for the
individuals therein involved. Here, we study the dynamics of emotional
contagion using Twitter. Rather than manipulating content, we devise a null
model that discounts some confounding factors (including the effect of
emotional contagion). We measure the emotional valence of content the users are
exposed to before posting their own tweets. We determine that on average a
negative post follows an over-exposure to 4.34% more negative content than
baseline, while positive posts occur after an average over-exposure to 4.50%
more positive contents. We highlight the presence of a linear relationship
between the average emotional valence of the stimuli users are exposed to, and
that of the responses they produce. We also identify two different classes of
individuals: highly and scarcely susceptible to emotional contagion. Highly
susceptible users are significantly less inclined to adopt negative emotions
than the scarcely susceptible ones, but equally likely to adopt positive
emotions. In general, the likelihood of adopting positive emotions is much
greater than that of negative emotions.Comment: 10 pages, 5 figure
Animating and sustaining niche social networks
Within the communicative space online Social Network Sites (SNS) afford, Niche Social Networks Sites (NSNS) have emerged around particular geographic, demographic or topic-based communities to provide what broader SNS do not: specified and targeted content for an engaged and interested community. Drawing on a research project developed at the Queensland University of Technology in conjunction with the Australian Smart Services Cooperative Research Centre that produced an NSNS based around Adventure Travel, this paper outlines the main drivers for community creation and sustainability within NSNS. The paper asks what factors motivate users to join and stay with these sites and what, if any, common patterns can be noted in their formation. It also outlines the main barriers to online participation and content creation in NSNS, and the similarities and differences in SNS and NSNS business models. Having built a community of 100 registered members, the staywild.com.au project was a living laboratory, enabling us to document the steps taken in producing a NSNS and cultivating and retaining active contributors. The paper incorporates observational analysis of user-generated content (UGC) and user profile submissions, statistical analysis of site usage, and findings from a survey of our membership pool in noting areas of success and of failure. In drawing on our project in this way we provide a template for future iterations of NSNS initiation and development across various other social settings: not only niche communities, but also the media and advertising with which they engage and interact. Positioned within the context of online user participation and UGC research, our paper concludes with a discussion of the ways in which the tools afforded by NSNS extend earlier understandings of online âcommunities of interestâ. It also outlines the relevance of our research to larger questions about the diversity of the social media ecology
Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction
Online social networks (OSN) are one of the most popular forms of modern
communication and among the best known is Facebook. Information about the
connection between users on the OSN is often very scarce. It's only known if
users are connected, while the intensity of the connection is unknown. The aim
of the research described was to determine and quantify friendship intensity
between OSN users based on analysis of their interaction. We built a
mathematical model, which uses: supervised machine learning algorithm Random
Forest, experimentally determined importance of communication parameters and
coefficients for every interaction parameter based on answers of research
conducted through a survey. Taking user opinion into consideration while
designing a model for calculation of friendship intensity is a novel approach
in opposition to previous researches from literature. Accuracy of the proposed
model was verified on the example of determining a better friend in the offered
pair
- âŠ