The alcohol self‐presentation model: Using thematic qualitative analysis to elucidate how college students self‐present via alcohol‐related social media posts
Autor: | Mai‐Ly N. Steers, Lene C. Mannheim, Rose Marie Ward, Angela B. Tanygin |
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Rok vydání: | 2022 |
Předmět: | |
Zdroj: | Drug and Alcohol Review. 41:1589-1598 |
ISSN: | 1465-3362 0959-5236 |
DOI: | 10.1111/dar.13508 |
Popis: | Posting and viewing of alcohol-related content to social media is prevalent among college students and is related to problematic drinking. However, the cognitive self-presentation and self-monitoring processes behind users' alcohol-related content posts to different platforms are largely unknown.Through in-depth, qualitative interviews with college students (n = 15) who drink heavily and post alcohol-related content regularly, we developed the Alcohol Self-Presentation Model.Using a multi-step thematic analysis approach, the themes of protectiveness and acquisitiveness emerged; these themes served as the basis of our model. In total, three protectiveness (perceived permanence of the content, privacy and reputation concerns) factors and four acquisitiveness (provocative, impulsive, high-status, maintaining relevance and connections) factors were derived.Our model delineated the alcohol self-presentations which allowed participants to demonstrate disparate versions of themselves to certain social media audiences. Our results illuminated that what people identify as being alcohol-related content, and what motivates the individual to post alcohol-related content, are essential to better comprehending how their alcohol-related content uniquely contributes to their drinking. Understanding students' alcohol self-presentations is important since their alcohol-related content may be putting themselves, and others, at-risk by perpetuating patterns of frequent posting and heavy drinking within the network. Our model may inform future behavioural interventions targeting the reduction of drinking among young people who are active on social media. |
Databáze: | OpenAIRE |
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