Machine learning and Natural Language Processing of social media data for event detection in smart cities

Social media data analysis in a smart city context can represent an efficacious instrument to inform decision making. The manuscript strives to leverage the power of Natural Language Processing (NLP) techniques applied to Twitter messages using supervised learning to achieve real-time automated event detection in smart cities. A semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event. To select the language processing models striking the best balance between accuracy and processing speed, we conducted a pre-emptive evaluation, comparing several baseline language models formerly employed by researchers for event classification. A heuristic analysis of several smart cities and community initiatives was conducted, with a view to define real-world scenarios as basis for determining correlations between two or more co-occurring event types and their associated levels of citizen satisfaction, while further considering environmental factors. Based on Multiple Regression Analysis (MRA), we established the relationships between scenario variables, obtaining a variance of 60%–90% between the dependent and independent variables. The selected combination of supervised NLP techniques leverages an accuracy of 88.5%. We found that all regression models had at least one variable below the 0.05 threshold of the , therefore at least one statistically significant independent variable. These findings ultimately illustrate how citizens, taking the role of active social sensors, can yield vital data that authorities can use to make educated decisions and sustainably construct smarter cities

Multi-class machine classification of suicide-related communication on Twitter

The World Wide Web, and online social networks in particular, have increased connectivity between people such that information can spread to millions of people in a matter of minutes. This form of online collective contagion has provided many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. However, it also poses a potential risk to vulnerable Web users who receive this information and could subsequently come to harm. One example of this would be the spread of suicidal ideation in online social networks, about which concerns have been raised. In this paper we report the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. It also aims to identify flippant references to suicide. We built a set of baseline classifiers using lexical, structural, emotive and psychological features extracted from Twitter posts. We then improved on the baseline classifiers by building an ensemble classifier using the Rotation Forest algorithm and a Maximum Probability voting classification decision method, based on the outcome of base classifiers. This achieved an F-measure of 0.728 overall (for 7 classes, including suicidal ideation) and 0.69 for the suicidal ideation class. We summarise the results by reflecting on the most significant predictive principle components of the suicidal ideation class to provide insight into the language used on Twitter to express suicidal ideation. Finally, we perform a 12-month case study of suicide-related posts where we further evaluate the classification approach - showing a sustained classification performance and providing anonymous insights into the trends and demographic profile of Twitter users posting content of this type

Building information modelling knowledge harvesting for energy efficiency in the Construction industry

The recent adoption of building information modelling (BIM), and the quest to decarbonise our built environment, has impacted several segments of the supply chain, including design and engineering practitioners, prompting the need to redefine the construction personnel positions along with associated skills and competencies. The research informs ways in which practitioners can fully embrace the potential of BIM for energy efficiency to promote sustainable interventions by improving existing training practices and identifying new training requirements as BIM evolves and as practitioners’ ICT (Information and Communications Technology) maturity levels improve. This is achieved by adopting a novel text-mining approach which analyses social media alongside secondary sources of evidence to establish a level of correlation between BIM roles and skills. The use of ontological dependency analysis has helped to understand the degree of correlation of skills with roles as a method to inform training and educational programmes. A key outcome from the research is a semantic webbased mining environment which determines BIM roles and skills, as well as their correlation factor, with an application for energy efficiency. The paper also evidences that (a) construction skills and roles are dynamic in nature and evolve over time, reflecting the digital transformation of the Construction industry, and (b) the importance of socio-organisational aspects in construction skills and related training provision

Analysing the connectivity and communication of suicidal users on Twitter

In this paper we aim to understand the connectivity and communication characteristics of Twitter users who post content subsequently classified by human annotators as containing possible suicidal intent or thinking, commonly referred to as suicidal ideation. We achieve this understanding by analysing the characteristics of their social networks. Starting from a set of human annotated Tweets we retrieved the authors’ followers and friends lists, and identified users who retweeted the suicidal content. We subsequently built the social network graphs. Our results show a high degree of reciprocal connectivity between the authors of suicidal content when compared to other studies of Twitter users, suggesting a tightly-coupled virtual community. In addition, an analysis of the retweet graph has identified bridge nodes and hub nodes connecting users posting suicidal ideation with users who were not, thus suggesting a potential for information cascade and risk of a possible contagion effect. This is particularly emphasised by considering the combined graph merging friendship and retweeting links

Blockchain for energy efficiency training in the construction industry

The construction sector faces the urgent need to prioritize energy efficiency due to an increasingly stringent regulatory landscape in response to the climate change agenda. Central to this transition is the pivotal role of education in equipping professionals with the necessary knowledge and skills. Educational solutions have emerged as powerful tools for promoting awareness and interventions to mitigate climate change. This article provides a case study that highlights the successful utilisation of computer technology in delivering digital solutions to advance energy education and promote more informed energy practices in the construction industry. The utilisation of digital technologies can enhance collaborative efforts in energy efficiency training, which is of critical significance in ensuring the security, sovereignty, transparency, immutability, and decentralisation of interventions related to energy education. This paper presents a framework that utilises Blockchain technology to facilitate training labelling and authenticity based on smart contracts and mobile passports to provide a secure and efficient solution for the delivery of training and education in the energy domain. Our research examines the challenges and opportunities related to energy efficiency training within the construction industry. By integrating industry-specific insights, exemplifications, and case studies, we provide an in-depth understanding of the interconnection between energy efficiency education and digital solutions with the unique context of the construction industry. We underscore the importance of leveraging digital platforms as educational tools to foster a deeper understanding and adoption of energy-efficient practices. We demonstrate that educational solutions play a pivotal role in driving awareness and interventions for mitigating climate change, greatly empowering individuals and organizations to adopt energy-efficient practices and to address sustainability objectives

Social media mining for BIM skills and roles for energy efficiency

Information modelling for the construction industry can address the fragmentation, multitude of professions and companies that often require collaboration and data exchange. Construction projects involve various professions, including design teams, contractors, facility managers, product manufacturers and suppliers, user associations, clients and investors, and local/regional/national/international authorities. The increasing complexity of buildings is reflected in the continuous introduction of new procurement paths and methods, construction technologies, materials and construction methods to meet various economic, environmental and societal challenges. To address this level of complexity Building Information Modelling (BIM) can create synergies and support collaboration not only between traditional disciplines and roles (architecture, structure, mechanical and electrical), but also support many new professions and skills in areas such as energy, environment, waste and connected objects / Internet of Things.In this paper, we explore the dynamic nature of BIM with associated skills and roles and demonstrate how engagement and training can be informed by social media analysis to identify roles, skills and training needs. We conduct a data mining process by analysing the Twitter data of various companies and institutions involved in the BIM construction sector to discover new skills and roles for energy efficiency

Building energy management systems for sports facilities in the Gulf region: a focus on impacts and considerations

Sports tourism in the Gulf region started to flourish where several international sports events were secured for the next decade. This reflects on the number of sports facilities, their energy consumption, and CO2 emissions mainly due to the indoor and outdoor air conditioning requirements. This paper aims to emphasize the significance of energy management in sports facilities especially for hot climatic regions. It presents a review of the works for optimizing building management systems’ (BMSs) operation, anomaly diagnosis, and mitigation. It indicates their application scarcity for sports facilities compared to other types of buildings, and for the regions with hot and humid weather conditions compared to amiable and cold ones, in addition to the considerations for optimizing BMSs of sports facilities based on their type and regional location. An overview is presented of the impacts related to the security and the reliable operation of the BMSs of sports facilities given the advancements in the deployed technologie

Neural network-based predictive control system for energy optimization in sports facilities: a case study

Given the increased global energy demand and its associated environmental impacts, the management and optimization of sports facilities is becoming imperative as they are characterized by high energy demand and occupancy profiles. In this work, the theory of model predictive control ȋMPCȌ is combined with neural networks for temperature setpoint selection to achieve energy and performance optimization of sports facilities. It is demonstrated using the building information model ȋBIMȌ of a sports hall in the sports complex of Qatar University. MPC systems are powerful as they allow integrated dynamic optimization that accounts for the future system behavior in the decision-making process, while neural networks are advantageous for their ability to represent complex interdependencies with high accuracy. The proposed approach was able to achieve a total energy savings of around ͵͵Ψ. Considerations about the network performance, MPC settings tuning, and optimization sub-optimality or failure are essential during the design and implementation phases of the proposed system

Digitalising risk of fire resilience for UK buildings

Several attempts have been made in the past to advance fire safety standards for residential buildings. However, the UK government has only partially succeeded in delivering detailed legislation that has been both successfully implemented and enforced across all types of dwellings. To further understand the government's approach, the author carried out detailed research into the common causes and triggers of fire that took place using a case study approach involving terrace housing, whilst also assessing the fitness for purpose of legislation with respect to the UK and EU regulatory landscape. In addition, independent research addressed all key elements of terraced dwellings, including information on fire alarm systems and detection devices, fire-resistant materials within external walls, compartmentalisation and combustibility of facades in roofing, quality of egress routes, and mitigation measures in place. Compiling data from multiple reputable sources such as the HSA, BRE and Home Office and comparing it with current legislation from Approved Document B: Fire Safety (vol. 2010-2019), indicated the government's failure to identify measures that could be adopted in order to retrofit existing homes and improve their resilience to the risk of fire. This severe deficiency in the lack of applicable legislation and safety methods in place for terrace housing confirms the need to implement contemporary approaches and advanced techniques for fire safety. This paper aims at exploring the resilience of the UK domestic housing to the risk of fire and provides simulation analysis from a real building case study identifying relevant fire propagation factors