Security measures like identity verification are extensively implemented and seamlessly integrated into daily life. However, these systems are often not designed to accommodate the needs of neurodivergent individuals, leading to potential accessibility and reliability challenges. Neurodiverse populations exhibit differences in cognitive abilities, behavior, and learning processes, including difficulties with memory and attention. These obstacles prevent users from recalling information or accessing instructions. Furthermore, neurodivergent individuals are more prone to exhibit differences in eye gaze, keystroke dynamics, or speech patterns than neurotypical populations. Individuals with neurodevelopmental conditions often exhibit cognitive and sensory differences that can render traditional identity verification systems less effective. This demographic disparity can increase susceptibility to cyberattacks, underscoring the urgent need for further research. The proposed study highlights these disparities and urges the community to explore strategies to improve fairness and effectiveness in such systems. This paper emphasizes the importance of modifying user identity confirmation technologies to suit better neurodiverse populations and advocates for developing more inclusive and accessible systems. It is crucial to ensure that security technologies, such as biometrics, are inclusive and effective for all users, including neurodivergent users. The proposed research promotes equal access and ethical practices in verifying digital identity.

Health communities online are popular for individuals to discuss health challenges and exchange social support. With social media, online communities also benefit neurodivergent individuals, by creating inclusive spaces where sharing of experience and knowledge is encouraged. The discussion in online communities covers a wide range of topics. As a result, the discussions differ in terms of topics, tone, and approach. This paper presents an analysis of social media posts shared on Reddit communities on Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) between 2018 and 2020. In the study, we use a computer-aided model to identify prevalent topics in each subreddit and common themes. We conduct a comparative analysis of the communities and assess theme frequency and sentiment. The study highlights common topics found in r/adhd and r/autism subreddits, including diagnosis, treatment (medication dose and side effects), and social aspects (school, work, and peer interactions).

During a pandemic, social media is a low-cost, accessible, and broad-reaching channel to disseminate information, however social media platforms can be hotbeds for misinformation. An analysis of misinformation around COVID-19 based on social media offers insights into what users perceive as misinformation, as well as their reactions. In order to identify the topics, sentiments, and user accounts in Twitter contributing to the spread of misinformation surrounding COVID-19, we analyze 12,000 tweets posted between February and May of 2020. We employ topic identification, network analysis, and sentiment analysis to study users’ behaviors around misinformation. We identify six topics and train a set using several machine learning and neural network models to automatically classify tweets. The experimental results indicate the predictions of our models for six categories related to COVID-19 misinformation, achieving the highest accuracy of 95%. The network analysis identifies clusters of accounts and indicates how misinformation is spread. In addition, our analysis shows that sentiment scores are strongly influenced by government measures and public speeches from government officials, and the primary drivers of discourse are news agencies, public figures, health organizations, and lay citizens. In general, our proposed approaches provide a better understanding of the posts and user accounts who lead the discussion about misinformation around COVID-19.

End-user generated content is responsible for the success of several collaborative applications, as it can be noted in the context of the web. The collaborative use of some of these applications is made possible, in many cases, by the availability of annotation features which allow users to include commentaries on each other's content. In this paper we first discuss the opportunity of defining vocabularies that allow third-party applications to integrate annotations to end-user generated documents, and present a proposal for such a vocabulary. We then illustrate the usefulness of our proposal by detailing a tool which allows users to add multimedia annotations to end-user generated video content.