ABSTRACT
Stuttering is a widespread speech disorder affecting people globally, and it impacts effective communication and quality of life. Recent advancements in artificial intelligence (AI) and computational intelligence have introduced new possibilities for augmenting stuttering detection and treatment procedures. In this systematic review, the latest AI advancements and computational intelligence techniques in the context of stuttering are explored. By examining the existing literature, we investigated the application of AI in accurately determining and classifying stuttering manifestations. Furthermore, we explored how computational intelligence can contribute to developing innovative assessment tools and intervention strategies for persons who stutter (PWS). We reviewed and analyzed 14 refereed journal articles that were indexed on the Web of Science from 2019 onward. The potential of AI and computational intelligence in revolutionizing stuttering assessment and treatment, which can enable personalized and effective approaches, is also highlighted in this review. By elucidating these advancements, we aim to encourage further research and development in this crucial area, enhancing in due course the lives of PWS.
ABSTRACT
People receive a wide variety of news from social media. They especially look for information on social media in times of crisis with the desire to assess the risk they face. This risk assessment, and other aspects of user reactions, may be affected by characteristics of the social media post relaying certain information. Thus, it is critical to understand these characteristics to deliver information with the reactions in mind. This study investigated various types of imagery used as thumbnails in social media posts regarding news about the COVID-19 pandemic. In an experimental design, 300 participants viewed social media posts containing each of the three types of imagery: data visualization (directly about risk information), advisory (not containing direct risk information, but instead help on how to lower risk), or clickbait (containing no risk-related information, just generic visuals). After observing the social media posts, they answered questionnaires measuring their emotions (valence, arousal, and dominance), risk perception, perceived credibility of the post, and engagement. The participants also indicated their emotions towards and interest in COVID-19 news coverage, age, gender, and how often and actively they use social media. These variables acted as controls. The data were analysed using mixed linear models. Results indicated that advisory imagery positively influenced valence, arousal, dominance, credibility, and (lower) risk perception. Alternatively, imagery showing data visualizations yielded low valence, arousal, dominance, credibility, and high risk perception. Clickbait-styled thumbnails which carry no information are usually measured between the other two types. The type of imagery did not affect the motivation to engage with a post. Aside from visual imagery, most variables were affected by COVID sentiment and the usual activity on social media. These study results indicate that one should use advisory imagery for more comfortable news delivery and data visualization when the poster wishes to warn users of existing risks.
