Analysis of Measures of Quantitative Association Rules

This paper presents the analysis of relationships among different interestingness measures of quality of association rules as first step to select the best objectives in order to develop a multi-objective algorithm. For this purpose, the discovering of association rules is based on evolutionary techniques. Specifically, a genetic algorithm has been used in order to mine quantitative association rules and determine the intervals on the attributes without discretizing the data before. The algorithm has been applied in real-word climatological datasets based on Ozone and Earthquake data.Ministerio de Ciencia y Tecnología TIN2007-68084-C-00Junta de Andalucía P07-TIC-0261

Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings

Unstructured data in social media is as part of the “big data” spectrum. Unstructured data in Social media can provide useful insights into social phenomena and citizen opinions, both of which are critical to government policy and businesses decisions. Teachers of business intelligence and analytics commonly use quantitative data from sales, marketing, finance and manufacturing to demonstrate various analytics concepts in a business context. However, researchers have seldom used social media data to analyze social behavior and communication. In this study we aim to demonstrate an assessment structure for teaching social media analytics concepts with the goal of analyzing and interpreting social media content. We base this assessment on forum postings regarding two recent events: the Christchurch earthquake in New Zealand, and the Japanese earthquake and tsunami. The aim of the assessment is to discover social insights. We base the assessment structure on Cooper’s Analytics Framework to cover such concepts as term frequency (TF), term frequency–inverse document frequency (TFIDF), data visualization, sentiments and opinions analysis, the Nearest Neighbor K-NN classification algorithm, and Information Diffusion theory. We review how the students performed on the assignment that used this assessment, and we make recommendations for future studies

Local conflict in Indonesia : Measuring incidence and identifying patterns

The widespread presence of local conflict characterizes many developing countries such as Indonesia. Outbreaks of violent conflict not only have direct costs for lives, livelihoods, and material property, but may also have the potential to escalate further. Recent studies on large-scale"headline"conflicts have tended to exclude the systematic consideration of local conflict, in large part due to the absence of representative data at low levels of geographic specification. This paper is a first attempt to correct for that. We evaluate a unique dataset compiled by the Indonesian government, the periodic Village Potential Statistics (PODES), which seeks to map conflict across all of Indonesia's 69,000 villages/neighborhoods. The data confirm that conflict is prevalent beyond well publicized"conflict regions,"and that it can be observed across the archipelago. The data report largely violent conflict in 7.1 percent of Indonesia's lowest administrative tier (rural desa and urban kelurahan). Integrating examples from qualitative fieldwork, we assess issues in the measurement of local conflict for quantitative analysis, and adopt an empirical framework to examine potential associations with poverty, inequality, shocks, ethnic and religious diversity/inequality, and community-level associational and security arrangements. The quantitative analysis shows positive correlations between local conflict and unemployment, inequality, natural disasters, changes in sources of incomes, and clustering of ethnic groups within villages. The institutional variables indicate that the presence of places of worship is associated with less conflict, while the presence of religious groups and traditional culture (adat) institutions are associated with conflict. We conclude by suggesting future areas of research, notably on the role of group inequality and inference, and suggest ways to improve the measurement of conflict in the village census.Services&Transfers to Poor,Post Conflict Reconstruction,Public Health Promotion,Education and Society,Peace&Peacekeeping,Post Conflict Reconstruction,Education and Society,Social Conflict and Violence,Rural Poverty Reduction,Services&Transfers to Poor

Uncertainty Analysis for the Keyword System of Web Events

© 2015 IEEE. Webpage recommendations for hot Web events can assist people to easily follow the evolution of these Web events. At the same time, there are different levels of semantic uncertainty underlying the amount of Webpages for a Web event, such as recapitulative information and detailed information. Apparently, the grasp of the semantic uncertainty of Web events could improve the satisfactoriness of Webpage recommendations. However, traditional hit-rate-based or clustering-based Webpage recommendation methods have overlooked these different levels of semantic uncertainty. In this paper, we propose a framework to identify the different underlying levels of semantic uncertainty in terms of Web events, and then utilize these for Webpage recommendations. Our idea is to consider a Web event as a system composed of different keywords, and the uncertainty of this keyword system is related to the uncertainty of the particular Web event. Based on keyword association linked network Web event representation and Shannon entropy, we identify the different levels of semantic uncertainty, and construct a semantic pyramid (SP) to express the uncertainty hierarchy of a Web event. Finally, an SP-based Webpage recommendation system is developed. Experiments show that the proposed algorithm can significantly capture the different levels of the semantic uncertainties of Web events and it can be applied to Webpage recommendations

Macro-scale vulnerability assessment of cities using Association Rule Learning

International audienceIn this paper, a datamining method based on Association Rule Learning (ARL) is applied to define a vulnerability proxy between the elementary characteristics of buildings and the vulnerability classes of the European Macroseismic Scale EMS98 (Grunthal, 1998). The method was applied to the Grenoble city test-bed described in the first part of this paper. The ARL method is then presented and a vulnerability proxy was derived for a Grenoble city-like environment. The vulnerability proxy is tested in Nice in the third part, a city that has been the subject of a vulnerability study (Spence and Lebrun, 2006). Finally, the damage produced by historic earthquakes was computed, considering the (equivalent) earthquake-era and the present-day urbanization for simulating seismic damage

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