15,130 research outputs found
Trust Based Participant Driven Privacy Control in Participatory Sensing
Widespread use of sensors and multisensory personal devices generate a lot of
personal information. Sharing this information with others could help in
various ways. However, this information may be misused when shared with all.
Sharing of information between trusted parties overcomes this problem. This
paper describes a model to share information based on interactions and opinions
to build trust among peers. It also considers institutional and other controls,
which influence the behaviour of the peers. The trust and control build
confidence. The computed confidence bespeaks whether to reveal information or
not thereby increasing trusted cooperation among peers.Comment: 14 page
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
Detecting and Monitoring Hate Speech in Twitter
Social Media are sensors in the real world that can be used to measure the pulse of societies.
However, the massive and unfiltered feed of messages posted in social media is a phenomenon that
nowadays raises social alarms, especially when these messages contain hate speech targeted to a
specific individual or group. In this context, governments and non-governmental organizations
(NGOs) are concerned about the possible negative impact that these messages can have on individuals
or on the society. In this paper, we present HaterNet, an intelligent system currently being used by
the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that
identifies and monitors the evolution of hate speech in Twitter. The contributions of this research
are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social
network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on
hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification
approaches based on different document representation strategies and text classification models. (4)
The best approach consists of a combination of a LTSM+MLP neural network that takes as input the
tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area
under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the
literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation
grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge
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