34,131 research outputs found
Reasoning about Complex Networks: A Logic Programming Approach
Reasoning about complex networks has in recent years become an important
topic of study due to its many applications: the adoption of commercial
products, spread of disease, the diffusion of an idea, etc. In this paper, we
present the MANCaLog language, a formalism based on logic programming that
satisfies a set of desiderata proposed in previous work as recommendations for
the development of approaches to reasoning in complex networks. To the best of
our knowledge, this is the first formalism that satisfies all such criteria. We
first focus on algorithms for finding minimal models (on which multi-attribute
analysis can be done), and then on how this formalism can be applied in certain
real world scenarios. Towards this end, we study the problem of deciding group
membership in social networks: given a social network and a set of groups where
group membership of only some of the individuals in the network is known, we
wish to determine a degree of membership for the remaining group-individual
pairs. We develop a prototype implementation that we use to obtain experimental
results on two real world datasets, including a current social network of
criminal gangs in a major U.S.\ city. We then show how the assignment of degree
of membership to nodes in this case allows for a better understanding of the
criminal gang problem when combined with other social network mining techniques
-- including detection of sub-groups and identification of core group members
-- which would not be possible without further identification of additional
group members.Comment: arXiv admin note: substantial text overlap with arXiv:1301.030
Complex Network Tools to Understand the Behavior of Criminality in Urban Areas
Complex networks are nowadays employed in several applications. Modeling
urban street networks is one of them, and in particular to analyze criminal
aspects of a city. Several research groups have focused on such application,
but until now, there is a lack of a well-defined methodology for employing
complex networks in a whole crime analysis process, i.e. from data preparation
to a deep analysis of criminal communities. Furthermore, the "toolset"
available for those works is not complete enough, also lacking techniques to
maintain up-to-date, complete crime datasets and proper assessment measures. In
this sense, we propose a threefold methodology for employing complex networks
in the detection of highly criminal areas within a city. Our methodology
comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community
Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of
assessment measures for analyzing intrinsic criminality of communities,
especially when considering different crime types. We show our methodology by
applying it to a real crime dataset from the city of San Francisco - CA, USA.
The results confirm its effectiveness to identify and analyze high criminality
areas within a city. Hence, our contributions provide a basis for further
developments on complex networks applied to crime analysis.Comment: 7 pages, 2 figures, 14th International Conference on Information
Technology : New Generation
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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