37,239 research outputs found
Modeling Evolutionary Dynamics of Lurking in Social Networks
Lurking is a complex user-behavioral phenomenon that occurs in all
large-scale online communities and social networks. It generally refers to the
behavior characterizing users that benefit from the information produced by
others in the community without actively contributing back to the production of
social content. The amount and evolution of lurkers may strongly affect an
online social environment, therefore understanding the lurking dynamics and
identifying strategies to curb this trend are relevant problems. In this
regard, we introduce the Lurker Game, i.e., a model for analyzing the
transitions from a lurking to a non-lurking (i.e., active) user role, and vice
versa, in terms of evolutionary game theory. We evaluate the proposed Lurker
Game by arranging agents on complex networks and analyzing the system
evolution, seeking relations between the network topology and the final
equilibrium of the game. Results suggest that the Lurker Game is suitable to
model the lurking dynamics, showing how the adoption of rewarding mechanisms
combined with the modeling of hypothetical heterogeneity of users' interests
may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201
Characterizing user behavior in online social networks: Analysis of the regular use of Facebook
The analysis of user behaviour in online social networks (OSNs) is one of the important research interests related to human-computer interactions. OSNs gives a large space to share news with no limits around the world and allows user to benefit from properties of this interactive and dynamic system. The study of user behaviour on a social and popular platform characterized by the use of new technologies requires to understand and the analysis of collective behaviour on Facebook. This paper aims to analyse the usage patterns in OSNs using the visible interactions of Facebook, by studying the time of activity and the evolution of human behaviour through a process of detection of visible and non-volatile interactions. In the first step, we perform a data collection process based on breadth first search algorithm (BFS) and semi-supervised crawler agent. In the second step, we build an interaction quantification process to measure users’ activities and analysis related time series. The study of the frequency of periodic use has shown that the communities monitored follow a weekly rhythm that decreases over time to reach a frequency of daily use, which reflects a stability of activities and a case of dependency of use
Measuring, Characterizing, and Detecting Facebook Like Farms
Social networks offer convenient ways to seamlessly reach out to large
audiences. In particular, Facebook pages are increasingly used by businesses,
brands, and organizations to connect with multitudes of users worldwide. As the
number of likes of a page has become a de-facto measure of its popularity and
profitability, an underground market of services artificially inflating page
likes, aka like farms, has emerged alongside Facebook's official targeted
advertising platform. Nonetheless, there is little work that systematically
analyzes Facebook pages' promotion methods. Aiming to fill this gap, we present
a honeypot-based comparative measurement study of page likes garnered via
Facebook advertising and from popular like farms. First, we analyze likes based
on demographic, temporal, and social characteristics, and find that some farms
seem to be operated by bots and do not really try to hide the nature of their
operations, while others follow a stealthier approach, mimicking regular users'
behavior. Next, we look at fraud detection algorithms currently deployed by
Facebook and show that they do not work well to detect stealthy farms which
spread likes over longer timespans and like popular pages to mimic regular
users. To overcome their limitations, we investigate the feasibility of
timeline-based detection of like farm accounts, focusing on characterizing
content generated by Facebook accounts on their timelines as an indicator of
genuine versus fake social activity. We analyze a range of features, grouped
into two main categories: lexical and non-lexical. We find that like farm
accounts tend to re-share content, use fewer words and poorer vocabulary, and
more often generate duplicate comments and likes compared to normal users.
Using relevant lexical and non-lexical features, we build a classifier to
detect like farms accounts that achieves precision higher than 99% and 93%
recall.Comment: To appear in ACM Transactions on Privacy and Security (TOPS
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportunities to
study the mechanisms that explain group success. While a growing body of
research investigates community success through a single measure -- typically,
the number of members -- we argue that there are multiple ways of measuring
success. Here, we present a systematic study to understand the relations
between these success definitions and test how well they can be predicted based
on community properties and behaviors from the earliest period of a community's
lifetime. We identify four success measures that are desirable for most
communities: (i) growth in the number of members; (ii) retention of members;
(iii) long term survival of the community; and (iv) volume of activities within
the community. Surprisingly, we find that our measures do not exhibit very high
correlations, suggesting that they capture different types of success.
Additionally, we find that different success measures are predicted by
different attributes of online communities, suggesting that success can be
achieved through different behaviors. Our work sheds light on the basic
understanding of what success represents in online communities and what
predicts it. Our results suggest that success is multi-faceted and cannot be
measured nor predicted by a single measurement. This insight has practical
implications for the creation of new online communities and the design of
platforms that facilitate such communities.Comment: To appear at The Web Conference 201
Characterizing web pornography consumption from passive measurements
Web pornography represents a large fraction of the Internet traffic, with
thousands of websites and millions of users. Studying web pornography
consumption allows understanding human behaviors and it is crucial for medical
and psychological research. However, given the lack of public data, these works
typically build on surveys, limited by different factors, e.g. unreliable
answers that volunteers may (involuntarily) provide.
In this work, we collect anonymized accesses to pornography websites using
HTTP-level passive traces. Our dataset includes about broadband
subscribers over a period of 3 years. We use it to provide quantitative
information about the interactions of users with pornographic websites,
focusing on time and frequency of use, habits, and trends. We distribute our
anonymized dataset to the community to ease reproducibility and allow further
studies.Comment: Passive and Active Measurements Conference 2019 (PAM 2019). 14 pages,
7 figure
Characterizing interactions in online social networks during exceptional events
Nowadays, millions of people interact on a daily basis on online social media
like Facebook and Twitter, where they share and discuss information about a
wide variety of topics. In this paper, we focus on a specific online social
network, Twitter, and we analyze multiple datasets each one consisting of
individuals' online activity before, during and after an exceptional event in
terms of volume of the communications registered. We consider important events
that occurred in different arenas that range from policy to culture or science.
For each dataset, the users' online activities are modeled by a multilayer
network in which each layer conveys a different kind of interaction,
specifically: retweeting, mentioning and replying. This representation allows
us to unveil that these distinct types of interaction produce networks with
different statistical properties, in particular concerning the degree
distribution and the clustering structure. These results suggests that models
of online activity cannot discard the information carried by this multilayer
representation of the system, and should account for the different processes
generated by the different kinds of interactions. Secondly, our analysis
unveils the presence of statistical regularities among the different events,
suggesting that the non-trivial topological patterns that we observe may
represent universal features of the social dynamics on online social networks
during exceptional events
- …