State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

WalkingStreet: understanding human mobility phenomena through a mobile application

Understanding human mobility patterns requires access to timely and reliable data for an adequate policy response. This data can come from several sources, such as mobile devices. Additionally, the wide availability of communications networks enables applications (mobile apps) to generate data anytime and anywhere thanks to their general adoption by individuals. Although data is generated from personal devices, if a relevant set of metrics is applied to it, it can become useful for the authorities and the community as a whole. This paper explores new methods for gathering and analyzing location-based data using a mobile application called WalkingStreet. The article also illustrates the great potential of human mobility metrics for moving spatial measures beyond census units, key measures of individual, collective mobility and a mix of the two, investigating a range of important social phenomena, the heterogeneity of activity spaces and the dynamic nature of spatial segregation.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/2020

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research

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

Retrieval, analysis and visualization of data from social media

[Abstract] This work is concerned with the development of an application that automates the identification, tracking, storage and visualization of social media contents, particularly of Twitter data. It is guided by the requirements of a client requesting such contents with regard to Vespa velutina, an invasive wasp species that is known to cause death due to severe allergic reactions.[Resumo] Este traballo trata sobre o desenvolvemento dunha aplicación que automatiza a identificación, seguimento, almacenamiento e visualización de contidos de redes sociais, concretamente de Twitter. Está guiado polos requirimentos dun cliente que precisa contidos sobre a Vespa velutina, unha especie invasora de avespa que pode causar a morte por reaccións alérxicas severas.Traballo fin de grao. Enxeñaría Informática. Curso 2021/202

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC

On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos