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The SAB was also heavily involved in the creation, refinement, and testing of the term lists used to categorize crowdfunding campaigns as LGBTQ+ and as cancer related (Figure 1). The first iteration of the cancer-related term list was previously published by Silver et al [17], while the first iteration of the LGBTQ+ term list was developed by the analytic team. The SAB then provided feedback by adding and removing terms from each term list, focusing primarily on the LGBTQ+ term list.
JMIR Cancer 2023;9:e51605
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Examining Public Awareness of Ageist Terms on Twitter: Content Analysis
As shown in Table 1, out of 60.32 million tweets, the term “elderly” occurred in 32,700 tweets (0.05%), “old people” occurred in 26,230 tweets (0.04%), and “older adult” occurred in 11,328 tweets (0.02%).
The prevalence of terms referring to older adults on Twitter in tweets related to COVID-19 (N=60,320,000).
“Keep your elderly folks from flying.
JMIR Aging 2023;6:e41448
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Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis
Since the blue bar and the red bar represent the overall term frequency and the estimated term frequency within the selected topic, respectively, the topic content can be better interpreted based on this approach [50,51]. With regard to topic 4, users of the 8 selected AI apps in MHC preferred to use words indicating the apps’ function of cheering people up, such as make, feel, happy, good, amazing, listener, friend, and nice. In this way, we could study the content of this topic and name it.
JMIR Hum Factors 2022;9(4):e38799
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