871 research outputs found
Moral Duties and Juridical Duties: The Ambiguity of Legal Ethics Considered Through the Prism of Kant's Metaphysics of Morals
AbstractMy article aims to analyze the conflict between the duties deriving from the supranational deontological codification under a philosophical point of view. Specifically, I will discuss both rules implying the independence of a lawyer and the obligation to ensure that the legitimate interests of the client and the proper administration of justice are protected. I will conduct an analysis about the relationship between moral and juridical duties through a few parts from The Metaphysics of Morals, published by Immanuel Kant in 1797. Afterwards, I will try to verify if Kant's conception of moral and juridical duties can provide a lawyer with the guidelines for the conflict resolution. I will suggest a meeting point capable of satisfying at the same time those interests whose protection is conflictingly established by the ethical code, and which is represented by the Alternative Dispute Resolution, precisely by the Assisted Negotiation procedure. Ultimately, I will propose an approach based on the identification of an agreement as a way to solve the conflict between the lawyer's duties
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
Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships
Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated\ud
by Collaborative Filtering Systems (CFSs). Traditional CFSs\ud
based on Matrix Factorization operate on the ratings provided\ud
by users and have been recently extended to incorporate\ud
demographic aspects such as age and gender. In this paper we\ud
propose to merge CF techniques based on Matrix Factorization\ud
and information regarding social friendships in order to\ud
provide users with more accurate suggestions and rankings\ud
on items of their interest. The proposed approach has been\ud
evaluated on a real-life online social network; the experimental\ud
results show an improvement against existing CF approaches.\ud
A detailed comparison with related literature is also presen
Enhancing community detection using a network weighting strategy
A community within a network is a group of vertices densely connected to each
other but less connected to the vertices outside. The problem of detecting
communities in large networks plays a key role in a wide range of research
areas, e.g. Computer Science, Biology and Sociology. Most of the existing
algorithms to find communities count on the topological features of the network
and often do not scale well on large, real-life instances.
In this article we propose a strategy to enhance existing community detection
algorithms by adding a pre-processing step in which edges are weighted
according to their centrality w.r.t. the network topology. In our approach, the
centrality of an edge reflects its contribute to making arbitrary graph
tranversals, i.e., spreading messages over the network, as short as possible.
Our strategy is able to effectively complements information about network
topology and it can be used as an additional tool to enhance community
detection. The computation of edge centralities is carried out by performing
multiple random walks of bounded length on the network. Our method makes the
computation of edge centralities feasible also on large-scale networks. It has
been tested in conjunction with three state-of-the-art community detection
algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results
show that our method raises the accuracy of existing algorithms both on
synthetic and real-life datasets.Comment: 28 pages, 2 figure
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
Extraction and Analysis of Facebook Friendship Relations
Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud
However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
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