2,125 research outputs found
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Data extracted from social networks like Twitter are increasingly being used
to build applications and services that mine and summarize public reactions to
events, such as traffic monitoring platforms, identification of epidemic
outbreaks, and public perception about people and brands. However, such
services are vulnerable to attacks from socialbots automated accounts that
mimic real users seeking to tamper statistics by posting messages generated
automatically and interacting with legitimate users. Potentially, if created in
large scale, socialbots could be used to bias or even invalidate many existing
services, by infiltrating the social networks and acquiring trust of other
users with time. This study aims at understanding infiltration strategies of
socialbots in the Twitter microblogging platform. To this end, we create 120
socialbot accounts with different characteristics and strategies (e.g., gender
specified in the profile, how active they are, the method used to generate
their tweets, and the group of users they interact with), and investigate the
extent to which these bots are able to infiltrate the Twitter social network.
Our results show that even socialbots employing simple automated mechanisms are
able to successfully infiltrate the network. Additionally, using a
factorial design, we quantify infiltration effectiveness of different bot
strategies. Our analysis unveils findings that are key for the design of
detection and counter measurements approaches
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
The arm race between spambots and spambot-detectors is made of several cycles
(or generations): a new wave of spambots is created (and new spam is spread),
new spambot filters are derived and old spambots mutate (or evolve) to new
species. Recently, with the diffusion of the adversarial learning approach, a
new practice is emerging: to manipulate on purpose target samples in order to
make stronger detection models. Here, we manipulate generations of Twitter
social bots, to obtain - and study - their possible future evolutions, with the
aim of eventually deriving more effective detection techniques. In detail, we
propose and experiment with a novel genetic algorithm for the synthesis of
online accounts. The algorithm allows to create synthetic evolved versions of
current state-of-the-art social bots. Results demonstrate that synthetic bots
really escape current detection techniques. However, they give all the needed
elements to improve such techniques, making possible a proactive approach for
the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM
Conference on Web Science, June 30-July 3, 2019, Boston, U
Report on the Information Retrieval Festival (IRFest2017)
The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
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