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

To characterize activity, text sentiment, and online community characteristics regarding "fertility" on Twitter before and during the COVID-19 pandemic using social network analysis.

Cross-sectional analysis.

Publicly available Twitter data.

Not applicable.

Not applicable.

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.

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."

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.