190,592 research outputs found
Discovery and classification of Twitter bots
A very large number of people use Online Social Networks daily. Such
platforms thus become attractive targets for agents that seek to gain access to
the attention of large audiences, and influence perceptions or opinions.
Botnets, collections of automated accounts controlled by a single agent, are a
common mechanism for exerting maximum influence. Botnets may be used to better
infiltrate the social graph over time and to create an illusion of community
behavior, amplifying their message and increasing persuasion.
This paper investigates Twitter botnets, their behavior, their interaction
with user communities and their evolution over time. We analyzed a dense crawl
of a subset of Twitter traffic, amounting to nearly all interactions by
Greek-speaking Twitter users for a period of 36 months. We detected over a
million events where seemingly unrelated accounts tweeted nearly identical
content at nearly the same time. We filtered these concurrent content injection
events and detected a set of 1,850 accounts that repeatedly exhibit this
pattern of behavior, suggesting that they are fully or in part controlled and
orchestrated by the same software. We found botnets that appear for brief
intervals and disappear, as well as botnets that evolve and grow, spanning the
duration of our dataset. We analyze statistical differences between bot
accounts and human users, as well as botnet interaction with user communities
and Twitter trending topics
Citizen Social Lab: A digital platform for human behaviour experimentation within a citizen science framework
Cooperation is one of the behavioral traits that define human beings, however
we are still trying to understand why humans cooperate. Behavioral experiments
have been largely conducted to shed light into the mechanisms behind
cooperation and other behavioral traits. However, most of these experiments
have been conducted in laboratories with highly controlled experimental
protocols but with varied limitations which limits the reproducibility and the
generalization of the results obtained. In an attempt to overcome these
limitations, some experimental approaches have moved human behavior
experimentation from laboratories to public spaces, where behaviors occur
naturally, and have opened the participation to the general public within the
citizen science framework. Given the open nature of these environments, it is
critical to establish the appropriate protocols to maintain the same data
quality that one can obtain in the laboratories. Here, we introduce Citizen
Social Lab, a software platform designed to be used in the wild using citizen
science practices. The platform allows researchers to collect data in a more
realistic context while maintaining the scientific rigour, and it is structured
in a modular and scalable way so it can also be easily adapted for online or
brick-and-mortar experimental laboratories. Following citizen science
guidelines, the platform is designed to motivate a more general population into
participation, but also to promote engaging and learning of the scientific
research process. We also review the main results of the experiments performed
using the platform up to now, and the set of games that each experiment
includes. Finally, we evaluate some properties of the platform, such as the
heterogeneity of the samples of the experiments and their satisfaction level,
and the parameters that demonstrate the robustness of the platform and the
quality of the data collected.Comment: 17 pages, 11 figures and 4 table
SUPER: Towards the Use of Social Sensors for Security Assessments and Proactive Management of Emergencies
Social media statistics during recent disasters (e.g. the 20 million tweets relating to 'Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
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