Keeping you posted: analysis of fertility-related social media posts after introduction of the COVID-19 vaccine.

PURPOSE: Our objective was to analyse information and sentiments posted regarding the COVID-19 vaccine on fertility-related social media. MATERIALS AND METHODS: The first fifty accounts on Instagram and Twitter were identified with the terms: fertility doctor, fertility, OBGYN, infertility, TTC, IVF. Accounts were categorised as physician (PH), individual (ID), or fertility center/organisation (FCO). The vaccine was approved on 12/11/2020 and Instagram and Twitter posts dated 12/1/2020 - 2/28/2021 were reviewed. Posts were analysed for sentiment, mention of research studies (RS), national guidelines (NG), personal experience (PE), side effects (SE), reproductive related (RR) content and activity, including likes and comments. RESULTS: A total of 276 accounts were included. Sentiments towards the vaccine were largely positive (PH 90.3%, ID 71.4%, FCO 70%), or neutral (PH 9.7%, ID 28.6%, FCO 30%). Instagram accounts showed an increase in activity on vaccine posts compared to baseline by likes (PH 4.86% v 3.76%*, ID 7.5% v 6.37%*, FCO 2.49% v 0.52%*) and comments (PH 0.35% v 0.28%, ID 0.90% v 0.69%,* FCO 0.10% v 0.02%*). CONCLUSION: Most posts expressed positive sentiments towards the vaccine. Evaluating the sentiment of the COVID-19 vaccine as it relates to fertility on social media represents an opportunity for understanding both the patient's and health care professional's opinion on the subject. Given the potential devastating effects of misinformation on public health parameters, like vaccination, social media offers one avenue for healthcare professionals to engage online and work to make their presences more effective and influential.SHORT CONDENSATIONThis article analyses content and sentiments posted regarding the COVID-19 vaccine on fertility-related social media in order to offer a deeper understanding of available information and beliefs.

Making it (net)work: a social network analysis of "fertility" in Twitter before and during the COVID-19 pandemic.

OBJECTIVE: To characterize activity, text sentiment, and online community characteristics regarding "fertility" on Twitter before and during the COVID-19 pandemic using social network analysis. DESIGN: Cross-sectional analysis. SETTING: Publicly available Twitter data. PATIENTS: Not applicable. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Number of users (vertices); edges (connections, defined as unique and total); self-loops (tweet without connection to another user); connected components (groups of users communicating back and forth frequently); maximum vertices in a connected component (largest group size); maximum and average geodesic distance (number of tweets to connect two users in the network); graph density; positive and negative sentiment tweets; and top 5 hashtags and top 5 word pairs. RESULTS: There were 1426 unique users and 401 groups in the pre-COVID-19 data compared to 1492 unique users and 453 groups in the during COVID-19 data. There was no difference in the number of total connections (96.8% [1381/1426] vs. 96.0% [1433/1492]) or self-loops (20.0% [286/1426] vs. 22.1% [329/1492]) before and during the COVID-19 pandemic. The percentage of unique connections per user decreased during COVID-19 (91.6% [1381/1508] pre-COVID-19 vs. 83.3% [1433/1720] during COVID-19). The average and maximum distance between users in the community increased during COVID-19 (maximum: 5 pre-COVID-19, 8 during COVID-19; average 1.95 pre-COVID-19, 2.43 during COVID-19). The percentage of positive sentiments per total number of tweets increased during COVID-19 (58.1% pre-COVID-19 [773/1331] vs. 64.3% [1198/1863] during COVID-19). The top 5 hashtags changed during COVID-19 to include COVID-19. The top word pairs changed from "family, hereditary; parents, children" to "fertility, treatment; healthcare, decisions." CONCLUSIONS: Despite the challenge to the fertility community amidst the COVID-19 pandemic, the overall Twitter sentiment regarding fertility was more positive during than before the pandemic. Top hashtags and word pairs changed to reflect the emergence of COVID-19 and the unique healthcare decision-making challenges faced. While the character, the number of users, and the total connections remained constant, the number of unique connections and the distance between users changed to reflect more self-broadcasting and less tight connections.