Definición y análisis de indicadores estratégicos para redes sociales : un caso de estudio en el sector automovilístico

Treball Final de Màster Universitari en Sistemes Intel·ligents. Codi: SIU043. Curs: 2015/2016La disciplina Inteligencia de Negocios se dedica a definir indicadores estratégicos a partir de medidas de interés definidas sobre un conjunto de datos temporales recolectados desde diferentes fuentes, e integrados bajo un mismo esquema multidimensional. Tradicionalmente, los datos recolectados tienen un carácter corporativo (ventas, promociones, etc.) y son generados dentro de la misma empresa. Sin embargo, buena parte de la información estratégica relevante que puede afectar a una organización reside actualmente en fuentes externas, principalmente las redes sociales. Desafortunadamente existen pocos trabajos que establezcan los indicadores externos más adecuados para cada dominio, y la forma de calcularlos a partir de las mismas redes sociales. En este trabajo se hace un estudio tanto de los trabajos propuestos en la literatura, como de los sistemas que actualmente ofrecen algún tipo de informes y análisis sobre redes sociales. Una vez realizado este estudio se propondrá un método para definir indicadores sociales, siguiendo la metodología tradicional utilizada en BI para definir indicadores estratégicos. Por último, se desarrollará un caso de estudio sobre la infraestructura SLOD-BI para demostrar la utilidad del método propuesto

Defining Dynamic Indicators for Social Network Analysis: A Case Study in the Automotive Domain using Twiter

Comunicación pesentada en 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2018) (18-20 septiembre Sevilla, España)In this paper we present a framework based on Linked Open Data Infrastructures to perform analysis tasks in social networks based on dynamically defined indicators. Based on the typical stages of business intelligence models, which starts from the definition of strategic goals to define relevant indicators (Key Performance Indicators), we propose a new scenario where the sources of information are the social networks. The fundamental contribution of this work is to provide a framework for easily specifying and monitoring social indicators based on the measures offered by the APIs of the most important social networks. The main novelty of this method is that all the involved data and information is represented and stored as Linked Data. In this work we demonstrate the benefits of using linked open data, especially for processing and publishing company-specific social metrics and indicators

Modeling Analytical Streams for Social Business Intelligence

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector

Quality Indicators for Social Business Intelligence

Comunicació presentada a 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (Granada, Spain, 22-25 Oct. 2019)The main purpose of Social Business Intelligence is to help companies in making decisions by performing multidimensional analysis of the relevant information disseminated on social networks. Although data quality is a general issue in SBI, few approaches have aimed at assessing it for any data collection, being this a context dependent task. In this paper, we define a quality indicator as a metric that serves to assess the overall quality of a collection, and that integrates the measures obtained by several quality criteria applied to filter the posts relevant for a SBI project. The selection of the best quality criteria to include in each quality indicator is a complex task that requires a deep understanding of both the context and objectives of analysis. In this paper, we propose a new methodology to design quality indicators for SBI projects whose quality criteria consider contents coherence and data provenance. Thus, for the context defined by an objective of analysis, this methodology helps users to find the quality criteria that best suit both the users and the available data, and then integrate them into a valid quality indicator

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems

Dynamic SLOD-BI: Infraestructura Dinamica de Inteligencia de Negocio Social

Comunicación presentada a las XXIII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2018) (Sevilla, 17-19 septiembre de 2018).Este proyecto plantea nuevas perspectivas de analisis y nuevasextensiones en la funcionalidad de la infraestructura de datos desarrolladaen el proyecto SLOD-BI (Social Linked Open Data for BusinessIntelligence). SLOD-BI plantea una infraestructura de datos enlazados yabiertos (Linked Open Data -LOD-) orientada a capturar y publicar hechosextrados de las redes sociales que son relevantes para los objetivosestrategicos de empresas PYME. La principal limitacion de estas infraestructurases su naturaleza estatica, ya que los datos son generados ypublicados cada cierto tiempo como conjuntos de datos RDF. Sin embargo,los hechos generados en las redes sociales son altamente dinamicos,y muchas veces requieren ser analizados casi en tiempo real (right time).En el proyecto Dynamic SLOD-BI se aborda principalmente la generacion dinamica de hechos para el calculo a demanda de indicadores deredes sociales. El sistema planteado en el proyecto descansa en el modeloconceptual de SLOD-BI, y plantea nuevos desafos de investigacion talescomo la generacion dinamica de conocimiento y el mantenimiento de sucoherencia

Multidimensional Author Profling for Social Business Intelligence

This paper presents a novel author profling method specially aimed at classifying social network users into the multidimensional perspectives for social business intelligence (SBI) applications. In this scenario, being the user profles defned on demand for each particular SBI application, we cannot assume the existence of labelled datasets for training purposes. Thus, we propose an unsupervised method to obtain the required labelled datasets for training the profle classifers. Contrary to other author profling approaches in the literature, we only make use of the users’ descriptions, which are usually part of the metadata posts. We exhaustively evaluated the proposed method under four diferent tasks for multidimensional author profling along with state-of-the-art text classifers. We achieved performances around 88% and 98% of F1 score for a gold standard and a silver standard datasets respectively. Additionally, we compare our results to other supervised approaches previously proposed for two of our tasks, getting very close performances despite using an unsupervised method. To the best of our knowledge, this is the frst method designed to label user profles in an unsupervised way for training profle classifers with a similar performance to fully supervised ones.Funding for open access charge: CRUE-Universitat Jaume