Promises and Pitfalls of Social Media Data Donations

Abstract

Studies into the association of social media use with mental health are largely based on measures of time spent on social media. The small and inconsistent results in these studies may be due to a lack of explanatory power of time-based measures. Data Download Packages (DDPs), the archives of social media platforms that each user is allowed to download, provide a new and promising method to collect content-based information about social media use. In this study, we discuss the promises and pitfalls of DDPs based on an exploratory analysis of 110 Instagram DDPs gathered from 102 adolescents. DDPs provide tremendous opportunities to get insight in the frequency, range, and content of social media activities, from browsing to searching and posting. Yet, the method is also complex and laborious and demands numerous procedural and analytical choices and decisions. Moreover, due to several factors unique to social media interactions, automated content analysis may be challenging. We successively discuss the challenges and opportunities of collecting and analyzing DDPs to help future researchers in their consideration of whether and how to use DDPs.

Happiness and Sadness in Adolescents’ Instagram Direct Messaging: A Neural Topic Modeling Approach

“…For instance, multimodal transformer models are promising as they allow researchers to analyze images and text simultaneously. Second, researchers should aim to analyze the expressions of happiness and sadness in adolescents' DMs on different social media platforms, because adolescents use different platforms in complementary ways (van Driel et al, 2019). For instance, Instagram might be primarily used by adolescents to communicate with their peers, whereas WhatsApp is used to communicate with their parents.…”

Does Passive Social Media Use Harm Well-Being?

“…Various studies, including those relying on log data, find that the overwhelming majority of time spent on SM applications consists of passively scrolling and browsing through content and profiles (Metzger et al, 2018; Valkenburg, van Driel, et al, 2022). Actively and publicly contributing to SM is rare, often done by only a few highly active accounts (e.g., van Driel et al, 2021), similar to or even more extreme than typical Pareto distributions (e.g., at least 80% of posts come from a maximum of 20% of accounts). But if passive use and the time spent on SMU are virtually synonymous for most users, then the passive use concept does not add substantial nuance to the screen time concept.…”

“…At the same time, active SMU is often better described as inter active use: it encompasses active (= sending) and passive (= receiving) elements of social interaction in quick succession (Ellison et al, 2020; Hall, 2018). Since most users do not engage in much active public SMU (e.g., van Driel et al, 2021), the conceptual merit of the active/passive dichotomy must come from distinguishing private forms of SMU engagement. Yet, private SMU is essentially mediated interpersonal communication (i.e., a constant back-and-forth of messages), making the active/passive dichotomy artificial and obsolete (Hall, 2018).…”

Leveraging Rights of Data Subjects for Social Media Analysis: Studying TikTok via Data Donations

“…A prominent movement in the medical field [5,36], researchers studying digital platforms are beginning to leverage the rich information in these packages by requesting that users donate them for study. Data donations offer unique insights into digital platforms [41]: for example, uncovering widely-used ad targeting mechanisms on Twitter that were largely ignored by prior work [43] and gaining new insights into how adolescents use Instagram [10] have all been possible via user data donation.…”