24,687 research outputs found
GeoNotes: A Location-based Information System for Public Spaces
The basic idea behind location-based information systems is to connect information pieces to positions in outdoor or indoor space. Through position technologies such as Global Positioning System (GPS), GSM positioning, Wireless LAN positioning o
How Far Removed Are You? Scalable Privacy-Preserving Estimation of Social Path Length with Social PaL
Social relationships are a natural basis on which humans make trust
decisions. Online Social Networks (OSNs) are increasingly often used to let
users base trust decisions on the existence and the strength of social
relationships. While most OSNs allow users to discover the length of the social
path to other users, they do so in a centralized way, thus requiring them to
rely on the service provider and reveal their interest in each other. This
paper presents Social PaL, a system supporting the privacy-preserving discovery
of arbitrary-length social paths between any two social network users. We
overcome the bootstrapping problem encountered in all related prior work,
demonstrating that Social PaL allows its users to find all paths of length two
and to discover a significant fraction of longer paths, even when only a small
fraction of OSN users is in the Social PaL system - e.g., discovering 70% of
all paths with only 40% of the users. We implement Social PaL using a scalable
server-side architecture and a modular Android client library, allowing
developers to seamlessly integrate it into their apps.Comment: A preliminary version of this paper appears in ACM WiSec 2015. This
is the full versio
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
Social Bots: Human-Like by Means of Human Control?
Social bots are currently regarded an influential but also somewhat
mysterious factor in public discourse and opinion making. They are considered
to be capable of massively distributing propaganda in social and online media
and their application is even suspected to be partly responsible for recent
election results. Astonishingly, the term `Social Bot' is not well defined and
different scientific disciplines use divergent definitions. This work starts
with a balanced definition attempt, before providing an overview of how social
bots actually work (taking the example of Twitter) and what their current
technical limitations are. Despite recent research progress in Deep Learning
and Big Data, there are many activities bots cannot handle well. We then
discuss how bot capabilities can be extended and controlled by integrating
humans into the process and reason that this is currently the most promising
way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure
The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election
The present study argues that political communication on social media is
mediated by a platform's digital architecture, defined as the technical
protocols that enable, constrain, and shape user behavior in a virtual space. A
framework for understanding digital architectures is introduced, and four
platforms (Facebook, Twitter, Instagram, and Snapchat) are compared along the
typology. Using the 2016 US election as a case, interviews with three
Republican digital strategists are combined with social media data to qualify
the studyies theoretical claim that a platform's network structure,
functionality, algorithmic filtering, and datafication model affect political
campaign strategy on social media
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