Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Visual analysis of social networks in space and time using smartphone logs

We designed and applied novel interactive visualisation to investigate how social networks - derived from smartphone logs - are embedded in time and space. Social networks were identified through direct calls between participants and calls to mutual contacts of participants. Direct contact between participants was sparse and deriving networks through mutual contacts helped enrich the social networks. Our resulting interactive visualisation tool offers four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data. Brushing and altering techniques help us investigate how these aspects relate. We also simultaneously display some demographic and attitudinal variables to help add context to the behaviours we observe. Using these techniques, we were able to characterise spatial and temporal aspects of participants' social networks and suggest explanations for some of them. We reflect on the extent to which such analysis helps us understand social communication behaviour

Time-Varying Graphs and Dynamic Networks

The past few years have seen intensive research efforts carried out in some apparently unrelated areas of dynamic systems -- delay-tolerant networks, opportunistic-mobility networks, social networks -- obtaining closely related insights. Indeed, the concepts discovered in these investigations can be viewed as parts of the same conceptual universe; and the formal models proposed so far to express some specific concepts are components of a larger formal description of this universe. The main contribution of this paper is to integrate the vast collection of concepts, formalisms, and results found in the literature into a unified framework, which we call TVG (for time-varying graphs). Using this framework, it is possible to express directly in the same formalism not only the concepts common to all those different areas, but also those specific to each. Based on this definitional work, employing both existing results and original observations, we present a hierarchical classification of TVGs; each class corresponds to a significant property examined in the distributed computing literature. We then examine how TVGs can be used to study the evolution of network properties, and propose different techniques, depending on whether the indicators for these properties are a-temporal (as in the majority of existing studies) or temporal. Finally, we briefly discuss the introduction of randomness in TVGs.Comment: A short version appeared in ADHOC-NOW'11. This version is to be published in Internation Journal of Parallel, Emergent and Distributed System

From social networks to emergency operation centers: A semantic visualization approach

Social networks are commonly used by citizens as a communication channel for sharing their messages about a crisis situation and by emergency operation centers as a source of information for improving their situation awareness. However, to utilize this source of information, emergency operators and decision makers have to deal with large and unstructured data, the content, reliability, quality, and relevance of which may vary greatly. In this paper, to address this challenge, we propose a visual analytics solution that filters and visualizes relevant information extracted from Twitter. The tool offers multiple visualizations to provide emergency operators with different points of view for exploring the data in order to gain a better understanding of the situation and take informed courses of action. We analyzed the scope of the problem through an exploratory study in which 20 practitioners answered questions about the integration of social networks in the emergency management process. This study inspired the design of a visualization tool, which was evaluated in a controlled experiment to assess its effectiveness for exploring spatial and temporal data. During the experiment, we asked 12 participants to perform 5 tasks related to data exploration and fill a questionnaire about their experience using the tool. One of the most interesting results obtained from the evaluation concerns the effectiveness of combining several visualization techniques to support different strategies for solving a problem and making decisions.This work was supported by the project PACE grant funded by the Spanish Ministry of Economy and Competitivity [TIN2016-77690-R]

A typology categorization of millennials in their technology behavior

Hay un interés creciente por los millennials; y sin embargo, hasta la fecha hay escasas segmentaciones de los millennials en cuanto a su comportamiento en relación a la tecnología. En este contexto, este estudio trata las siguientes cuestiones:”¿Son los millennials monolíticos o hay diferentes segmentos en esta generación en cuanto a su comportamiento tecnológico?”. Y si este fuera el caso: “¿Existen diferencias importantes en cuanto a la forma en que los millennials usan la tecnología?”. Nuestro objetivo consiste en examinar los potenciales perfiles de los millennials en relación a su comportamiento y uso de la tecnología. Los datos obtenidos de una muestra de 707 millennials se analizaron mediante un análisis de componentes principales y análisis clúster. A continuación, los segmentos se caracterizaron mediante un análisis MANOVA. Nuestros resultados revelan la existencia de cinco segmentos o tipologías de millennials en cuanto a su comportamiento tecnológico: los “devotos de la tecnología”, los “espectadores”, los “prudentes”, los “adversos” y los “productivos”. Este estudio contribuye de forma detallada al conocimiento sobre cómo las diferentes categorías de millennials usan la tecnología.There is an increasing interest for millennials; however, to date millennials’ segmentations regarding their technology behavior are scarce. In this context, this study addresses the following questions: “Are millennials monolithic, or are there segments within this generation group regarding the technology behavior?”. And if so: “Are there important variances in the way that millennial segments use technology?”. Our purpose is to examine the potential profiles of millennials regarding their technology use and behavior. Data from a sample of 707 millennials was gathered and analyzed through principal component analysis and cluster analysis. Then, millennials’ segments were profiled using a MANOVA analysis. Our findings revealed five different segments or typologies of millennials regarding their technology behavior: technology devotees, technology spectators, circumspects, technology adverse users and productivity enhancers. This study contributes with a detailed perspective of how different millennial segments use technology

Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we developed Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2018