ArticleSocial network analysis and mining2026
Assessing mobile instant messenger networks with donated data.
Article in Social network analysis and mining, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Data Donation as a Method to Measure Physical Activity in Older Adults: Cross-Sectional Web Survey Assessing Consent Rates, Donation Success, and Bias.Journal of medical Internet research · 2025Article
- Best practices for studies using digital data donation.Quality & quantity · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the increasing popularity and societal relevance of mobile instant messenger services (MIMSs) such as WhatsApp, Telegram and Signal, the vast majority of the research on social media is focused on the "traditional" social media platforms such as Twitter (X) and Facebook, mostly due to the difficulty of accessing MIMSs data. Consequently, little is known about even the most basic topological features of societal-scale instant messenger networks. To overcome this knowledge gap, we employ the innovative approach of data donation by respondents of a high-quality probability-based panel in the Netherlands (the LISS panel) to collect user data while preserving their privacy. Focusing on WhatsApp as the most popular MIMS, this study collects the first measurement of MIMS usage on a national probability sample, focusing on degree distributions as a key feature of network topology. We find that the degree distribution of contacts is best approximated by a log-normal distribution, while the distribution of group membership is best approximated by the exponential distribution. At the individual level, we find that predictors of degree derived from the literature on extended networks mostly replicate. Supplementary Information: The online version contains supplementary material available at 10.1007/s13278-025-01550-8.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.