Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…based sampling (e.g., friends or followers), or topical or domain based sampling (e.g., select users taking diﬀerent drugs than the one under analysis) to identify similar users with those treated, but that have not received the treatment (Pavalanathan and Eisenstein, 2016; Olteanu et al., 2017b).”
A Social Media Study on the Effects of Psychiatric Medication Use (2019, methodology): “Social media enables individuals to candidly share their personal and social experiences (Kıcıman et al. 2018, Olteanu et al. 2017, Saha et al. 2019b), thereby providing low-cost, large-scale, non-intrusive data to understand naturalistic patterns of mood, behavior, cognition, social milieu, and…”
The Effect of Extremist Violence on Hateful Speech Online (2018, background): “Recent studies show that quasi-causal methods can be applied to social media data to e.g., distill the outcomes of a given situation (Olteanu, Varol, and Kıcıman 2016), measure the impact of an intervention (Chandrasekharan et al. 2018), or estimate the effect of online social support (Cunha,…”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “Other domains that analyze language as a confounder include news (Johansson et al., 2016), social media (De Choudhury et al., 2016; Olteanu et al., 2017), and loan descriptions (Pham and Shen, 2017).”