Visualizing The Effect Of Frequency And Nature Of Social Media Platforms Usage On Mental Health And Family Functioning From An Ethnographic Point Of View

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Abstract

Social media has become an integral part of our daily lives, with billions of users accessing these platforms mainly from their smartphones. Existing literature suggests that social media has an impact on mental health, but it is not clear whether this impact is positive or negative. This raises concerns about the relationship between social networks and public health, which require further investigation. In response, this doctoral consortium paper proposes a research study that investigates the impact of visualizing the frequency and nature of social media use on mental health and family functioning. The research study consists of two main user studies and four supporting surveys, aimed at reflecting on and adding to the existing literature. Furthermore, the study sampled participants from different ethnographic backgrounds, including the United Kingdom, Saudi Arabia, and Malaysia. This approach is beneficial because it allows cross-cultural comparisons, which can help identify similarities and differences in the impact of social media on mental health and family functioning across different cultures. It is important to examine the impact of social media on mental health and family functioning across cultures because social norms, values, and attitudes may differ between cultures, leading to different results. The analysis includes in-group comparisons between different cultures and gender, with an emphasis on identifying negative impacts. This research addresses a literature gap, providing evidence of the negative impact of social media on mental health and family functioning and the effectiveness of visualizing this effect.

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APA

Alwuqaysi, B. H. (2023). Visualizing The Effect Of Frequency And Nature Of Social Media Platforms Usage On Mental Health And Family Functioning From An Ethnographic Point Of View. In ACM International Conference Proceeding Series (pp. 271–274). Association for Computing Machinery. https://doi.org/10.1145/3582515.3609544

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