A Graph-based Approach for Detecting Critical Infrastructure Disruptions on Social Media in Disasters

The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for situation awareness during disasters has been highlighted in recent studies. However, the application of social sensing in detecting disruptions of critical infrastructure is limited because existing approaches cannot provide complete and non-ambiguous situational information about critical infrastructure. Therefore, to address this methodological gap, we developed a graph-based approach including data filtering, burst time-frame detection, content similarity and graph analysis. A case study of Hurricane Harvey in 2017 in Houston was conducted to illustrate the application of the proposed approach. The findings highlighted the temporal patterns of critical infrastructure events that occurred in disasters including disruptive events and their adverse impacts on communities. The findings also provided insights for better understanding critical infrastructure interdependencies in disasters. From the practical perspective, the proposed methodology study can improve the ability of community members, first responders and decision makers to detect and respond to infrastructure disruptions in disasters

Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data

Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development. Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals (SDGs). Urban sprawl has resulted in unsustainable urban development patterns from social, environmental, and economic perspectives. This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality, Tanzania. Random Forest (RF) method was applied to accomplish imagery classification and location-based social media (Twitter usage) data were obtained through a Twitter Application Programming Interface (API). Morogoro urban municipality was classified into built-up, vegetation, agriculture, and water land cover classes while the classification results were validated by the generation of 480 random points. Using the Kernel function, the study measured the location of Twitter users within a 1 km buffer from the center of the city. The results indicate that, expansion of the city (built-up land use), which is primarily driven by population expansion, has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover. In addition, social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area. The outcome of the study suggests that the combination of remote sensing, social sensing, and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro, and Africa city where data for urban planning is often unavailable, inaccurate, or stale

Sentinel: a co-designed platform for semantic enrichment of social media streams

We introduce the Sentinel platform that supports semantic enrichment of streamed social media data for the purposes of situational understanding. The platform is the result of a codesign effort between computing and social scientists, iteratively developed through a series of pilot studies. The platform is founded upon a knowledge-based approach, in which input streams (channels) are characterized by spatial and terminological parameters, collected media is preprocessed to identify significant terms (signals), and data are tagged (framed) in relation to an ontology. Interpretation of processed media is framed in terms of the 5W framework (who, what, when, where, and why). The platform is designed to be open to the incorporation of new processing modules, building on the knowledge-based elements (channels, signals, and framing ontology) and accessible via a set of user-facing apps. We present the conceptual architecture for the platform, discuss the design and implementation challenges of the underlying streamprocessing system, and present a number of apps developed in the context of the pilot studies, highlighting the strengths and importance of the codesign approach and indicating promising areas for future research

CYWARN: Strategy and Technology Development for Cross-Platform Cyber Situational Awareness and Actor-Specific Cyber Threat Communication

Despite the merits of digitisation in private and professional spaces, critical infrastructures and societies are increasingly ex-posed to cyberattacks. Thus, Computer Emergency Response Teams (CERTs) are deployed in many countries and organisations to enhance the preventive and reactive capabilities against cyberattacks. However, their tasks are getting more complex by the increasing amount and varying quality of information dissem-inated into public channels. Adopting the perspectives of Crisis Informatics and safety-critical Human-Computer Interaction (HCI) and based on both a narrative literature review and group discussions, this paper first outlines the research agenda of the CYWARN project, which seeks to design strategies and technolo-gies for cross-platform cyber situational awareness and actor-spe-cific cyber threat communication. Second, it identifies and elabo-rates eight research challenges with regard to the monitoring, analysis and communication of cyber threats in CERTs, which serve as a starting point for in-depth research within the project

Method for Detecting Far-Right Extremist Communities on Social Media

Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the subject area of far-right extremist communities, we identified three groups of factors that influence the effectiveness of the research work. These are a group of theoretical, methodological, and instrumental factors. We developed and implemented a unique algorithm of calendar-correlation analysis (CCA) to search for specific online communities. We based CCA on a hybrid calendar correlation approach identifying potential far-right communities by characteristic changes in group activity around key dates of events that are historically crucial to those communities. The developed software module includes several functions designed to automatically search, process, and analyze social media data. In the current paper we present a process diagram showing CCA’s mechanism of operation and its relationship to elements of automated search software. Furthermore, we outline the limiting factors of the developed algorithm. The algorithm was tested on data from the Russian social network VKontakte. Two experimental data sets were formed: 259 far-right communities and the 49 most popular (not far-right) communities. In both cases, we calculated the type II error for two mutually exclusive hypotheses—far-right affiliation and no affiliation. Accordingly, for the first sample, Я = 0.81. For the second sample, Я = 0.02. The presented CCA algorithm was more effective at identifying far-right communities belonging to the alt-right and Nazi ideologies compared to the neo-pagan or manosphere communities. We expect that the CCA algorithm can be effectively used to identify other movements within far-right extremist communities when an appropriate foundation of expert knowledge is provided to the algorithm

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis

The making of smart cities : borders, security and value in New Town Kolkata and Cape Town

The making of smart cities transforms not only infrastructures and practices but also the techniques of urban government and security, and economic processes. This thesis draws on analysis conducted in two research sites: Cape Town, in South Africa and New Town Rajarhat, a satellite township on the outskirts of Kolkata, to present three key arguments. Firstly, and as opposed to mainstream narratives that describe smart cities as seamlessly connected environments, this thesis suggests that urban digitalisation is linked to bordering processes. Whereas critical literature has comprehensively discussed the political implications and risks associated with smart city projects, such as corporatisation and technocratic governance, the specific relations between digital infrastructures and borders, within the urban space, have not yet been discussed. Secondly, this thesis argues that smart cities are inherently security projects, insofar as the deployment of a computing infrastructure of sensing initiates a preemptive apparatus. In security systems, such as the Emergency Policing and Incident Command (EPIC) program in Cape Town, or the Xpresso software for social media monitoring in New Town, algorithms are continuously modelling and acting upon future scenarios; from traffic jams to wildfires, from crime hotspots to citizens’ moods. My third argument is that the computing apparatus of security also serves as an infrastructure of value extraction. Recently, there has been much theorising and debate about security platforms’ economic operations, but the situated modalities in which they extract value from the urban environment remain to be examined. Overall, this thesis points to the socio-spatial, governmental and economic relations that computing infrastructures are generating, or reconfiguring, in the urban environment. These relations articulate distinct processes, including the hierarchisation and control of the urban space, preemptive policies and extractive strategies. Critically analysing these processes allows the registration of the political implications of smart city projects

Situation monitoring of urban areas using social media data streams

The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Our goal is to leverage social media users as “social sensors” in order to increase the situational awareness within and about urban areas. In addition to the well-known challenges of event and topic detection and tracking, this task involves a spatial and temporal dimension. In this paper, we present a visualization that supports analysts in monitoring events/topics and emotions both in time and in space. The visualization uses a clock-face metaphor to encode temporal and spatial relationships, a color map to reflect emotion, and tag clouds for events and topics. A hierarchy of these clock-faces supports drilling down to finer levels of granularity as well as rolling up the vast and fast flow of information. In order to showcase these functionalities of our visualization, we discuss several case studies that use the live data stream of the Twitter microblogging service. Finally, we demonstrate the usefulness and usability of the visualization in a user study that we conducted.publishe

Using social media data to understand the urban green space use before and after a pandemic

Urban green spaces (UGSs) are essential components of urban ecosystems that provide considerable benefits to residents, including recreational opportunities, improved air and water quality, and mental and physical health benefits. The COVID-19 pandemic and related restriction measures have affected people's daily lives in numerous ways, such as remote working and learning, online shopping, social distancing, travel restrictions, and outdoor activities. During the COVID-19 pandemic, UGSs have become the main places for outdoor activities. Understanding human-environment interactions in UGSs is an important research field that has broad implications for improving policies in response to a social crisis and informing urban planning strategies. The main challenges of investigating human-environment interactions lie in effectively collecting research datasets that can reflect or reveal human behaviour patterns within UGSs. Volunteered Geographical Information (VGI) and social media can provide better information about real-time perceptions, attitudes and behaviours than traditional datasets such as surveys and questionnaires. This provides great opportunities to investigate human-environment interactions in UGS in real-time. Additionally, Twitter is one of the most popular social networks, and it can provide more comprehensive and unbiased datasets through a new academic research Application Programming Interface (API). The overall aim of this thesis is to evaluate the contributions of UGS to human well-being, during a time of crisis, by investigating the characteristics and spatial-temporal patterns of UGS use across three periods: pre-, during- and after the COVID-19 pandemic. The thesis will document the process of examining spatial-temporal changes in UGS use associated with COVID-19 related pandemic, by using Twitter datasets incorporating approaches including text mining, topic modelling and spatial-temporal analysis. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can potentially inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic. This research has great potential to help improve urban green space planning and management in urban areas