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The rapid development and deployment of artificial intelligence (AI) technologies have sparked intense public interest and debate. While these innovations promise to revolutionise various aspects of human life, it is crucial to understand the complex emotional responses they elicit from potential adopters and users. Such findings can offer crucial guidance for stakeholders involved in the development, implementation, and governance of AI technologies like OpenAI’s ChatGPT, a large language model (LLM) that garnered significant attention upon its release, enabling more informed decision-making regarding potential challenges and opportunities. While previous studies have employed data-driven approaches towards investigating public reactions to emerging technologies, they often relied on sentiment polarity analysis, which categorises responses as positive or negative. However, this binary approach fails to capture the nuanced emotional landscape surrounding technological adoption. This paper overcomes this limitation by presenting a comprehensive analysis for investigating the emotional landscape surrounding technology adoption by using the launch of ChatGPT as a case study. In particular, a large corpus of social media texts containing references to ChatGPT was compiled. Text mining techniques were applied to extract emotions, capturing a more nuanced and multifaceted representation of public reactions. This approach allows the identification of specific emotions such as excitement, fear, surprise, and frustration, providing deeper insights into user acceptance, integration, and potential adoption of the technology. By analysing this emotional landscape, we aim to provide a more comprehensive understanding of the factors influencing ChatGPT’s reception and potential long-term impact. Furthermore, we employ topic modelling to identify and extract the common themes discussed across the dataset. This additional layer of analysis allows us to understand the specific aspects of ChatGPT driving different emotional responses. By linking emotions to particular topics, we gain a more contextual understanding of public reaction, which can inform decision-making processes in the development, deployment, and regulation of AI technologies.

With the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such attacks can vary significantly, from affecting the service of the device itself to putting their owners and their personal information at risk. As a response to improving their security, the focus has been on attacks, specifically on the network layer. However, the importance and impact of other vulnerabilities, such as low-level Radio Frequency (RF) attacks, have been neglected. Such attacks are challenging to detect, and they can be deployed using non-expensive equipment and can cause significant damage. This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again. This finding highlights the lack of protection against both intentional and unintentional jamming. In addition, all devices demonstrated that they were susceptible to replay attacks, which highlights the need for more hardened security measures. Finally, this paper discusses proposals for defence mechanisms for enhancing the security of IoT devices against the aforementioned attacks.

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns.

While new technologies are expected to revolutionise and become game-changers in improving the efficiency and practices of our daily lives, it is also critical to investigate and understand the barriers and opportunities faced by their adopters. Such findings can serve as an additional feature in the decisionmaking process when analysing the risks, costs, and benefits of adopting an emerging technology in a particular setting. Although several studies have attempted to perform such investigations, these approaches adopt a qualitative data collection methodology, which is limited in terms of the size of the targeted participant group and is associated with a significant manual overhead when transcribing and inferring results. This paper presents a scalable and automated framework for tracking the likely adoption and/or rejection of new technologies from a large landscape of adopters. In particular, a large corpus of social media texts containing references to emerging technologies was compiled. Text mining techniques were applied to extract the sentiments expressed towards technology aspects. In the context of the problem definition herein, we hypothesise that the expression of positive sentiment implies an increase in the likelihood of impacting a technology user’s acceptance to adopt, integrate, and/or use the technology, and negative sentiment implies an increase in the likelihood of impacting the rejection of emerging technologies by adopters. To quantitatively test our hypothesis, a ground truth analysis was performed to validate that the sentiments captured by the text mining approach were comparable to the results provided by human annotators when asked to label whether such texts positively or negatively impact their outlook towards adopting an emerging technology. The collected annotations demonstrated comparable results to those of the text mining approach, illustrating that the automatically extracted sentiments expressed towards technologies are useful features in understanding the landscape faced by technology adopters, as well as serving as an important decisionmaking component when, for example, recognising shifts in user behaviours, new demands, and emerging uncertainties.

Topic modelling is a text mining technique for identifying salient themes from a number of documents. The output is commonly a set of topics consisting of isolated tokens that often co-occur in such documents. Manual effort is often associated with interpreting a topic’s description from such tokens. However, from a human’s perspective, such outputs may not adequately provide enough information to infer the meaning of the topics; thus, their interpretability is often inaccurately understood. Although several studies have attempted to automatically extend topic descriptions as a means of enhancing the interpretation of topic models, they rely on external language sources that may become unavailable, must be kept up to date to generate relevant results, and present privacy issues when training on or processing data. This paper presents a novel approach towards extending the output of traditional topic modelling methods beyond a list of isolated tokens. This approach removes the dependence on external sources by using the textual data themselves by extracting high-scoring keywords and mapping them to the topic model’s token outputs. To compare how the proposed method benchmarks against the state of the art, a comparative analysis against results produced by Large Language Models (LLMs) is presented. Such results report that the proposed method resonates with the thematic coverage found in LLMs and often surpasses such models by bridging the gap between broad thematic elements and granular details. In addition, to demonstrate and reinforce the generalisation of the proposed method, the approach was further evaluated using two other topic modelling methods as the underlying models and when using a heterogeneous unseen dataset. To measure the interpretability of the proposed outputs against those of the traditional topic modelling approach, independent annotators manually scored each output based on their quality and usefulness as well as the efficiency of the annotation task. The proposed approach demonstrated higher quality and usefulness, as well as higher efficiency in the annotation task, in comparison to the outputs of a traditional topic modelling method, demonstrating an increase in their interpretability.

Digital Security by Design (DSbD) is an initiative supported by the UK government aimed at transforming digital technology to deliver necessary digital resilience and prosperity across the UK. As emerging challenges in the field of digital security evolve, it becomes essential to explore how entities involved in DSbD interact and change over time. Understanding these dynamic relationships can provide crucial insights for the development and improvement of security practices. This paper presents a data-driven analysis of the evolving landscape of DSbD from 2019 to 2024, gathering insights from textual documents referencing DSbD. Using a combination of text mining techniques and network analysis, a large corpus of textual documents was examined to identify key entities, including organisations, individuals, and the relationships between them. A network was then visualised to analyse the structural connections between these entities, revealing how key concepts and actors have evolved. The results and discussion demonstrate that the network analysis offers a unique advantage in tracking and visualising these evolving relationships, providing insights into shifts in focus, emerging trends, and changes in technological adoption over time. For example, a notable finding from the analysis is the substantial increase in node relationships associated with Artificial Intelligence (AI). We hypothesise that this surge reflects the growing integration of AI into digital security strategies, driven by the need for more adaptive and autonomous solutions to tackle evolving cyber threats, as well as the rapid introduction of new AI tools to the market and their swift adoption across various industries. By mapping such connections, such results are useful, helping practitioners and researchers recognise new security demands and adjust strategies to better respond to the evolving landscape of DSbD.

The performance of emotive text classification using affective hierarchical schemes (e.g., WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation into how methods used in hierarchical classification problems in other domains, which extend traditional evaluation metrics to consider the characteristics of the hierarchical classification scheme, can be applied and subsequently improve the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all the methods improved the emotion classification. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier.

Traditionally, cyber risk assessment considers system-level risk separately from individual component-level risk, i.e., devices, data, people. This separation prevents effective impact assessment where attack intelligence for a specific device can be mapped to its impact on the entire system, leading to cascading failures. Furthermore, risk assessments typically follow a failure or attack perspective, focusing on potential problems, which means they need to be updated as attacks evolve. This approach does not scale to modern digital ecosystems. In this paper, we present a Data Science approach, which involves using machine learning algorithms and statistical models to analyse and predict the impact of cyber attacks. Specifically, this approach integrates automated attack detection on specific devices with a systems view of risk. By mapping operational goals in a top-down manner, we transform attack intelligence on individual components into system success probabilities.

Abstract Access control readers are the first line of defence for organizations to restrict access to their facilities to the people who are supposed to be there. Such readers represent a major investment for organizations and are replaced every 7–10 years. The choice of reader and credential made at the time the system was designed and installed may be vulnerable to an array of attacks, such as credential cloning and data transmission exploits, which would allow a threat actor to pass through an entrance undetected. Once the threat actor has entered the building, the people and/or assets that the organization are responsible for are at risk. Access control readers have a number of interfaces based on different technologies that may be attacked to learn more about the configuration and other features of the device. This information may then be used to craft an attack on a real system. To the best of our knowledge, this is the first paper that outlines the various technologies incorporated into these products and draws upon this data to present the first model of the contemporary access control reader. The model is then further developed by considering the cybersecurity implications of each of the technologies found in an access control reader. Finally, based on known attack vectors, the model may be used as a risk assessment framework for readers and credentials. From this foundation, a series of further research topics are then proposed.

Abstract This article presents three-tiered intrusion detection systems, which uses a supervised approach to detect cyber-attacks in industrial control systems networks. The proposed approach does not only aim to identify malicious packets on the network but also attempts to identify the general and finer grain attack type occurring on the network. This is key in the industrial control systems environment as the ability to identify exact attack types will lead to an increased response rate to the incident and the defence of the infrastructure. More specifically, the proposed system consists of three stages that aim to classify: (i) whether packets are malicious; (ii) the general attack type of malicious packets (e.g. Denial of Service); and (iii) finer-grained cyber-attacks (e.g. bad cyclic redundancy check, attack). The effectiveness of the proposed intrusion detection systems is evaluated on network data collected from a real industrial gas pipeline system. In addition, an insight is provided as to which features are most relevant in detecting such malicious behaviour. The performance of the system results in an F-measure of: (i) 87.4%, (ii) 74.5% and (iii) 41.2%, for each of the layers, respectively. This demonstrates that the proposed architecture can successfully distinguish whether network activity is malicious and detect which general attack was deployed.