12,420 research outputs found
The Lifecycles of Apps in a Social Ecosystem
Apps are emerging as an important form of on-line content, and they combine
aspects of Web usage in interesting ways --- they exhibit a rich temporal
structure of user adoption and long-term engagement, and they exist in a
broader social ecosystem that helps drive these patterns of adoption and
engagement. It has been difficult, however, to study apps in their natural
setting since this requires a simultaneous analysis of a large set of popular
apps and the underlying social network they inhabit.
In this work we address this challenge through an analysis of the collection
of apps on Facebook Login, developing a novel framework for analyzing both
temporal and social properties. At the temporal level, we develop a retention
model that represents a user's tendency to return to an app using a very small
parameter set. At the social level, we organize the space of apps along two
fundamental axes --- popularity and sociality --- and we show how a user's
probability of adopting an app depends both on properties of the local network
structure and on the match between the user's attributes, his or her friends'
attributes, and the dominant attributes within the app's user population. We
also develop models that show the importance of different feature sets with
strong performance in predicting app success.Comment: 11 pages, 10 figures, 3 tables, International World Wide Web
Conferenc
Cultures in Community Question Answering
CQA services are collaborative platforms where users ask and answer
questions. We investigate the influence of national culture on people's online
questioning and answering behavior. For this, we analyzed a sample of 200
thousand users in Yahoo Answers from 67 countries. We measure empirically a set
of cultural metrics defined in Geert Hofstede's cultural dimensions and Robert
Levine's Pace of Life and show that behavioral cultural differences exist in
community question answering platforms. We find that national cultures differ
in Yahoo Answers along a number of dimensions such as temporal predictability
of activities, contribution-related behavioral patterns, privacy concerns, and
power inequality.Comment: Published in the proceedings of the 26th ACM Conference on Hypertext
and Social Media (HT'15
Network entity characterization and attack prediction
The devastating effects of cyber-attacks, highlight the need for novel attack
detection and prevention techniques. Over the last years, considerable work has
been done in the areas of attack detection as well as in collaborative defense.
However, an analysis of the state of the art suggests that many challenges
exist in prioritizing alert data and in studying the relation between a
recently discovered attack and the probability of it occurring again. In this
article, we propose a system that is intended for characterizing network
entities and the likelihood that they will behave maliciously in the future.
Our system, namely Network Entity Reputation Database System (NERDS), takes
into account all the available information regarding a network entity (e. g. IP
address) to calculate the probability that it will act maliciously. The latter
part is achieved via the utilization of machine learning. Our experimental
results show that it is indeed possible to precisely estimate the probability
of future attacks from each entity using information about its previous
malicious behavior and other characteristics. Ranking the entities by this
probability has practical applications in alert prioritization, assembly of
highly effective blacklists of a limited length and other use cases.Comment: 30 pages, 8 figure
Communication Channels, Spatial Stereotyping, and Urban Conflict: A Cross-Scale and Spatio-Temporal Perspective
Our research addresses how individuals exposed to various types of communication situations-from face-to-face to Internet environments-are more or less likely to react to urban locations with fear or to find them desirable. The present article summarizes what we have learned from a number of research projects about the effects of communication practices on spatial and ethnic stereotyping in conditions of violent urban conflict and will offer a number of recommendations for mitigating the negative effects of these processes
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
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