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While the presence of a behavior can be detected, it is unclear who or under what circumstances the behavior was generated. Despite the large sample sizes of SM datasets, they almost always come with significant biases, some of which have been studied before. Here, we hypothesize the presence of a largely unrecognized form of bias on SM platforms, called <jats:italic>participation bias<\/jats:italic>, that is distinct from selection bias. It is defined as the skew in the demographics of the participants who opt-in to discussions of the topic, compared to the demographics of the underlying SM platform. To infer the participant\u2019s demographics, we propose a novel generative probabilistic framework that links surveys and SM data at the granularity of demographic subgroups (and not individuals). Our method is distinct from existing approaches that elicit such information at the individual level using their profile name, images, and other metadata, thus infringing upon their privacy. We design a statistical simulation to simulate multiple SM platforms and a diverse range of topics to validate the model\u2019s estimates in different scenarios. We use Twitter data as a case study to demonstrate participation bias on the topic of gun violence delineated by political party affiliation and gender. Although Twitter\u2019s user population leans Democratic and has an equal number of men and women according to Pew, our model\u2019s estimates point to the presence of participation bias on the topic of gun control in the opposite direction, with slightly more Republicans than Democrats, and more men compared to women. Our study cautions that in the rush to use digital data for decision-making and understanding public opinions, we must account for the biases inherent in how SM data are produced, lest we may also arrive at biased inferences about the public.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-023-00405-6","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T10:02:43Z","timestamp":1690538563000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Quantifying participation biases on social media"],"prefix":"10.1140","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9410-7084","authenticated-orcid":false,"given":"Neeti","family":"Pokhriyal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin A.","family":"Valentino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soroush","family":"Vosoughi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"405_CR1","unstructured":"(2019) Foundations for evidence-based policymaking act of 2018. https:\/\/www.cio.gov\/policies-and-priorities\/evidence-based-policymaking\/"},{"key":"405_CR2","unstructured":"(2021) A European strategy for data. https:\/\/digital-strategy.ec.europa.eu\/en\/policies\/strategy-data"},{"key":"405_CR3","unstructured":"(2020) National policy development framework. https:\/\/www.gov.za\/sites\/default\/files\/gcis_document\/202101\/national-policy-development-framework-2020.pdf"},{"issue":"4","key":"405_CR4","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1177\/0894439319875692","volume":"39","author":"F Conrad","year":"2019","unstructured":"Conrad F, Gagnon-Bartsch J, Ferg R, Schober M, Pasek J, Hou E (2019) Social media as an alternative to surveys of opinions about the economy. 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