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<p>When and how to come out are difficult choices. In this research project, we examine one form of disclosure: the addition of an LGBTQ keyword to one's online social media profile. We construct daily time series of the prevalence of LGBTQ keywords within American Twitter users' self-descriptions. Further, we construct daily time series of inferred add and drop events. These we compare to relevant annual and one-time events. We confirm or disconfirm our pre-registered hypotheses.</p> <p><strong>Pre-Registered Hypotheses</strong></p> <p>On October 16, 2019, we finalized a list of LGBTQ-relevant events and a set of pre-registered hypotheses. We completed this before constructing the time-series datasets for this project.</p> <ul> <li><a href="https://osf.io/zc7qd/" rel="nofollow">LGBTQ-relevant events</a></li> <li><a href="https://osf.io/xrck2/" rel="nofollow">Pre-Registered Hypotheses</a></li> </ul> <p><strong>TO DO:</strong> 1. Construct daily prevalence time series (cross-sectional). 2. Construct daily prevalence time series (longitudinal). 3. Construct daily add-event time series (inferred). 4. Construct daily delete-event time series (inferred). </p>
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