ProTechThem: Digital Ethnography Data, 2013-2022

This data collection contains anonymised extracts from a passive digital ethnography of “sharenting” in open/public online communities, capturing how adults disclose information about children in everyday posts and interactions. The study was motivated by growing concerns about the potential risks and harms of sharing child-related content online, alongside limited empirical evidence about what is actually shared in naturalistic online settings and how online audiences respond. To enable cross-cultural comparison, the dataset draws on English-language communities with a largely UK-based audience (while including other English speakers) and Italian-language communities where sharers communicate in Italian, spanning multiple sharenting-relevant contexts such as parenting advice, special needs and ADHD support, child modelling, travel, and family legal issues.

Communities were selected through purposive sampling guided by ethical commitments: only forums and groups that were publicly accessible were included, and sampled spaces had to contain posts focused on sharenting themes (i.e., disclosures about children). Over a three-month observation period (January-April 2022), researchers observed group activity and, where relevant, reviewed earlier content (in some cases back to 2013). Using a structured observation grid developed and refined by the research team, posts that showed clear indicators of potentially harmful sharenting were manually extracted rather than collected through automated scraping, given the sensitivity of the material. Extracts include the textual content of posts and researcher descriptions of any images or videos, along with contextual variables such as platform, date, group topic, sharenter role (e.g., mother/father/carer), the type of sharenting (deliberate or unintentional), and the nature of comments and community norms.

All extracted material was immediately anonymised, stored securely in Excel in line with UK GDPR and institutional ethics requirements, and stripped of personal identifiers to protect privacy. The dataset also captures surrounding discourse, how sharers, other members, and moderators discuss motivations, risks, harms, and informal rules, providing a rich resource for studying the social dynamics, norms, and ethical tensions that shape sharenting practices in public online environments.