1109 Analyzing the evolving opinions of air travel during pregnancy - a data science approach

Abstract

Given globalization and the growth of the travel industry, pregnant women are taking to the air. We hypothesized that women are obtaining safety information on air travel while pregnant on social media rather than directly from healthcare providers. This study collected publicly available YouTube video titles and descriptions before and during COVID19 (01/01/2019 to 09/01/2019 and 01/01/2020 to 09/01/2020) using a web scraping method. The number of YouTubes was compared to the number of publications on PubMed during the same time period. Search terms used for YouTube and PubMed were “flying” and “pregnant.” YouTube data were further characterized by three text mining analyses including sentiment analysis (classifying text into emotional themes), Wordclouds, and theme tables. A total of 67 YouTubes in 2019, and 55 YouTubes in 2020 met the criteria for analysis. The sentiment analysis found a positive public attitude towards flying while pregnant for both years. Wordclouds and theme tables showed pregnant women were flying in 2019 for “babymoons” and social events, while flying in 2020 were related to “flying home” and continued to be used for “babymoons.” Theme tables in both years emerged 4 major themes on safety and tips including travel purpose, pregnancy terms, travel destinations, and recommendations. COVID19 was a new theme introduced in 2020. In comparison, a PubMed search in 2019 identified 0 publications regarding flying during pregnancy; and in 2020, 3 publications were identified. In fact, 1975-2020, only 38 articles were identified, and of those only 20 directly related to flying while pregnant. It was observed women are obtaining information on air travel during pregnancy via social media. The three analyses indicate that women are continuing to fly while pregnant with a positive attitude before and during the COVID19, but “flying home” was more common in 2020. The next step would be to investigate each video to differentiate those that were educational versus those that were purely for entertainment.