Understanding the relationship between AI and gender on social TV content selection - Habes, Mohammed; Alhazmi, Amal Hassan; Elareshi, Mokhtar; Attar, Razaz Waheeb (Frontiers in Communication, 2024)
As technological advancements continue to shape daily life, social television has emerged as an interactive platform that fosters connections among family and friends. Machine learning, particularly AI, within digital networks aids in curating customized content for users. This study investigates the selection of social TV content by examining the influence of machine learning, AI, along with other contributing factors, with gender proposed as a mediating factor. It involves enrolled students from several universities, using two randomly selected public universities (n=300) in Irbid, Jordan. An online survey with self-reported responses was used in this research. Using a self-reported study model, the study revealed that distinct characteristics such as information gathering, social awareness, and knowledge sharing are attributed to the implementation of AI, subsequently affecting user attitudes and facilitating content selection on social TV platforms. Furthermore, both AI and user attitudes contribute significantly to content selection, while gender emerges as a potential mediator, influencing AI integration and attitudinal shifts in the content selection process. Ultimately, AI provides seamless access to preferred content and improves ease of use, thus enriching the content recommendation system and fostering increased user interest and satisfaction. Therefore, this positive experience with AI services shapes user attitudes towards technology, particularly those enabled by AI. This research lies in its comprehensive examination of the intersection between AI, gender, attitudes, and social TV content selection within the context of Jordanian young users. Few studies have explored this specific nexus, particularly within the MENA region, making this research a pioneering contribution to the field of media studies.