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 (TW) before and during the COVID-19 pandemic using social network analysis (SNA) Design: Cross sectional study Materials and Methods: SNA uses graph theory to understand structure, flow, content, and relationships of networks among individuals SNA was performed using NodeXL, a software platform that performs social network and content analysis The search term “fertility” on TW was investigated during the weeks of February 20-27th, 2020 (Pre-COVID) and April 29th-May 6th, 2020 (during-COVID) User demographics, tweet content, and characteristics of the network were collected and analyzed during these time periods These included: # 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;top 5 hashtags;and top 5 word pairs Statistical analyses included a z-ratio for comparison of proportions, with p\u003c0 05 considered significant Results: There were 1426 unique users and 401 groups in the pre-COVID data compared to 1492 unique users and 453 groups in the during –COVID data There was no difference in the number of total connections [96 8% (1381/1426) vs 96 0% (1433/1492), p=0 25] or self-loops [20 0% (286/1426) vs 22 1% (329/1492), p=0 19]before and during the COVID-19 pandemic The percentage of unique connections per user decreased during COVID-19 [91 6% (1381/1508) pre-COVID vs 83 3% (1433/1720) during COVID, p\u003c0 0002] The average and maximum distance between users in the community increased during COVID (maximum: 5 pre-COVID, 8 during-COVID;average 1 95 pre-COVID, 2 43 during-COVID) The percentage of positive sentiments per total number of tweets increased during COVID [58 1% pre-COVID (773/1331) vs 64 3% (1198/1863) during-COVID, p\u003c0 0004] The overall character of the TW fertility social network remained constant at both time points with a broadcast “spoke and out wheel” shape The top 5 hashtags changed during COVID to include COVID19 The top word pairs changed from “family, hereditary;parents, children” to “fertility, treatment;healthcare, decisions ” Conclusions: Despite the challenge to the fertility community amidst COVID19, overall TW sentiment regarding fertility was more positive during than before the pandemic Top hashtags/word pairs changed to reflect the emergence of COVID and the unique healthcare decision making challenges faced While the character, # of users, and total connections remained constant, unique connections and distance between users changed to reflect more self-broadcasting and less tight connections Given no change in network structure where time at home could have led to increased social media (SM) use, further study is needed to leverage SM in these situations References: None