Sharenting and social media properties: Exploring vicarious data harms and sociotechnical mitigations

• Published in: Big data & society Vol. 11; no. 1
• Main Authors: Ugwudike, Pamela, Roth, Silke, Lavorgna, Anita, Middleton, Stuart E., Djohari, Natalie, Tartari, Morena, Mandal, Arpan
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-03-2024; Sage Publications Ltd; SAGE Publishing
• Subjects: Algorithms; Criminology; Digital media; Ethnography; Mass media effects; Natural language processing; Social media; Social networks; Technology; United Kingdom > UK; harm; natural language processing; Facebook; social media platforms; Sharenting
• ISSN: 2053-9517; 2053-9517
• DOI: 10.1177/20539517231219243

In this paper, we demonstrate how social media technologies can co-produce data-related harms unless preventative measures are instituted. To this end, we draw on a passive ethnography of a public Facebook group in the UK practicing sharenting which occurs when parents and guardians post sensitive and identifying information about children in their care on social media. Theoretically, we draw on the ‘harm translation’ concept from digital criminology and the ‘seductions of crime’ perspective from cultural criminology. Further we analyse documents on the operations of Facebook's content filtering algorithms published by Meta (Facebook's parent company). With insights from these sources, we demonstrate how platform technologies go beyond facilitation to the inadvertent co-production of harm via embedded mediative properties that shape user perception and action. We show that, in the specific context of sharenting, the properties invite rather than simply facilitate the practice and can also invite subsequent misuses of child-centric data. Through our analysis of these dynamics, we set out an empirical basis for challenging reductive depictions of social media technologies as solely facilitative of human action including harmful conduct. We also outline our vision to integrate insights from the analysis into a new sociotechnical harm prevention framework informed by Natural Language Processing approaches.