About Me

I am a Lecturer (~Assistant Professor) at the School of Computer Science and Informatics at Cardiff University. My work focuses on the development of novel approaches towards automated cyber defence. In particular, my interest is on how to apply text mining and machine learning techniques in defence methodologies.

Previously, I was a Research Associate at Cardiff University working for the Discribe Hub and Data Innovation Accelerator projects.

I am a co-founder and act as a domain knowledge expert of QRLA, a start-up formed as part of the Cyber Innovation Hub initative in collaboration with Alacrity.

🚨 Looking for PhD Students! - I am interested in supervising funded or self-funded PhD students in projects involving Large Language Model (LLM) security. Please check the relevant page in FindAPhD for more information and do not hesitate to contact me if you have any questions.

Interests
  • Natural Language Processing/Text Mining/Information Retrieval
  • Machine Learning/Adversarial Machine Learning
  • Intrusion Detection
Education
  • PhD in Computer Science, 2017

    Cardiff University

  • BSc Information Systems, 2013

    Cardiff University

News

See all
The Data Gals
Data Science Mentor
Acted as Data Science Mentor for The Data Gals.
MDPI Information
New Publication
Main author of our paper Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives published in MDPI Information’s section Information Processes.
Cardiff University
PhD Viva - Internal Examiner
Acted as an internal examiner for a PhD viva at Cardiff University.

Recent Publications

Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives

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.

Contact

  • WilliamsL10@cardiff.ac.uk
  • School of Computer Science and Informatics, Cardiff University, Abacws, Senghennydd Road, Cardiff, CF24 4AG,