Population attitudes toward contraceptive methods over time on a social media platform

Abstract

BACKGROUND: Contraceptive method choice is often strongly influenced by the experiences and opinions of one's social network. Although social media, including Twitter, increasingly influences reproductive-age individuals, discussion of contraception in this setting has yet to be characterized. Natural language processing, a type of machine learning in which computers analyze natural language data, enables this analysis. OBJECTIVE: This study aimed to illuminate temporal trends in attitudes toward long-and short-acting reversible contraceptive methods in tweets between 2006 and 2019 and establish social media platforms as alternate data sources for large-scale sentiment analysis on contraception. STUDY DESIGN: We studied English-language tweets mentioning reversible prescription contraceptive methods between March 2006 (founding of Twitter) and December 2019. Tweets mentioning contraception were extracted using search terms, including generic or brand names, colloquial names, and abbreviations. We characterized and performed sentiment analysis on tweets. We used Mann-Kendall nonparametric tests to assess temporal trends in the overall number and the number of positive, negative, and neutral tweets referring to each method. The code to reproduce this analysis is available at https://github.com/ hms-dbmi/contraceptionOnTwitter. RESULTS:We extracted 838,739 tweets mentioning at least 1 contraceptive method. The annual number of contraception-related tweets increased considerably over the study period. The intrauterine device was the most commonly referenced method (45.9%). Long-acting methods were mentioned more often than short-acting ones (58% vs 42%), and the annual proportion of long-acting reversible contraceptionrelated tweets increased over time. In sentiment analysis of tweets mentioning a single contraceptive method (n¼665,064), the greatest proportion of all tweets was negative (65,339 of 160,713 tweets with at least 95% confident sentiment, or 40.66%). Tweets mentioning longacting methods were nearly twice as likely to be positive compared with tweets mentioning short-acting methods (19.65% vs 10.21%; P<.002). CONCLUSION: Recognizing the influence of social networks on contraceptive decision making, social media platforms may be useful in the collection and dissemination of information about contraception.

Attitudes Toward the Copper IUD in Sweden: A Survey Study

“…Nevertheless, negatively oriented comments had a strong presence among the open responses, overall. This is in correspondence with previous research noting negative attitudes toward the copper IUD (14,16,18,20,21), although positive experiences and attitudes have also been shown (6,14,15,23).…”

“…While research points to high levels of satisfaction with LARC methods including the copper IUD (6,14,15), studies have also indicated negative attitudes toward the copper IUD in countries including Sweden (14,(16)(17)(18)(19)(20)(21). Negative views on the copper IUD have often been associated with communication in social networks, including online social media, where negative commentaries on IUDs have been found to be prevalent (14,16,(22)(23)(24)(25)(26)(27), although positive views are also communicated (23,25). Meanwhile, the strong importance of social networks for contraceptive choice and use has been observed in countries including Sweden (28,29).…”

Advances of Sentiment Analysis Applications in Obstetrics/Gynecology and Midwifery

“…It was found that negative entries usually employ more emotional words (specifically when trying to communicate fear), while more positive entries seem to be approved on Facebook. In a different study ( 17 ), on short- and long- term contraceptive methods, online trends using tweets between March 2006 and December 2019, were examined. Relevant tweets were tracked, using search terms of generic or brand names, colloquial names and abbreviations.…”

Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

“…Mann-Kendall (MK) [23] is a non-parametric test, which is widely used to detect monotonic trends in a time-series. Its robustness against censored and non-Gaussian distributed data o time series with missing or noisy observations [5] that are frequently encountered in a term occurrence time series makes MK an almost standard trend test method in NLP [1,[6][7][8][9][10][11][12]. A brief description of the MK method is provided below.…”

“…Due to their reduced sensitivities to outliers [2], the lack of assumptions concerning the data sample distribution [3] or homoscedasticity [4], nonparametric trend tests tend to be favored by researchers over parametric methods. In particular, the Mann-Kendall (MK) test statistic being a robust trend indicator when dealing with censored data, arbitrary non-Gaussian data distributions or time series with missing observations [5] have become almost standard methods for NLP applications [1,[6][7][8][9][10][11][12].…”