Lung cancer remains a leading cause of cancer-related deaths, but public support for individuals living with lung cancer is often constrained by stigma and misconceptions, leading to serious emotional and social consequences for those diagnosed. Understanding how this stigma manifests and affects individuals is vital for developing inclusive interventions. Online discussion forums offer a unique opportunity to examine how lung cancer stigma is expressed and experienced. This study combines qualitative analysis and unsupervised learning (topic modelling) to explore stigma-related content within an online lung cancer forum. Our findings highlight the role of online forums as a key space for addressing anti-discriminatory attitudes and sharing experiences of lung cancer stigma. We found that users both with and with- out lung cancer engage in discussions pertaining to supportive and welcoming topics, high- lighting the online forum’s role in facilitating social and informational support.

Identifying self-disclosed health diagnoses in social media data using regular expressions (e.g. “I’ve been diagnosed with <Disease X>”) is a well-established approach for creating ad hoc cohorts of individuals with specific health conditions. However there is evidence to suggest that this method of identifying individuals is unreliable when creating cohorts for some mental health and neurodegenerative conditions. In the case of dementia, the focus of this paper, diagnostic disclosures are frequently whimsical or sardonic, rather than indicative of an authentic diagnosis or underlying disease state (e.g. “I forgot my keys again. I’ve got dementia!”). With this work and utilising an annotated corpus of 14,025 dementia diagnostic self-disclosure posts derived from Twitter, we leveraged LLMs to distinguish between “authentic” dementia self-disclosures and “inauthentic” self-disclosures. Specifically, we implemented a genetic algorithm that evolves prompts using various state-of-the-art prompt engineering techniques, including chain of thought, self-critique, generated knowledge, and expert prompting. Our results showed that, of the methods tested, the evolved self-critique prompt engineering method achieved the best result, with an F1-score of 0.8.

Adolescents exposed to advertisements promoting addictive substances exhibit a higher likelihood of subsequent substance use. The predominant source for youth exposure to such advertisements is through online content accessed via smartphones. Detecting these advertisements is crucial for establishing and maintaining a safer online environment for young people. In our study, we utilized Multimodal Large Language Models (MLLMs) to identify addictive substance advertisements in digital media. The performance of MLLMs depends on the quality of the prompt used to instruct the model. To optimize our prompts, an adaptive prompt engineering approach was implemented, leveraging a genetic algorithm to refine and enhance the prompts. To evaluate the model’s performance, we augmented the RICO dataset, consisting of Android user interface screenshots, by superimposing alcohol ads onto them. Our results indicate that the MLLM can detect advertisements promoting alcohol with a 0.94 accuracy and a 0.94 F1 score.