Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks

Exploring Language Used in Posts on r/birthcontrol: Case Study Using Data From Reddit Posts and Natural Language Processing to Advance Contraception Research

BACKGROUND

Contraceptive choice is central to reproductive autonomy. The internet, including social networking sites like Reddit, is an important resource for people seeking contraceptive information and support. A subreddit dedicated to contraception, r/birthcontrol, provides a platform for people to post about contraception.

OBJECTIVE

This study explored the use of r/birthcontrol, from the inception of the subreddit through the end of 2020. We describe the web-based community, identify distinctive interests and themes based upon the textual content of posts, and explore the content of posts with the most user engagement (ie, "popular" posts).

METHODS

Data were obtained from the PushShift Reddit application programming interface from the establishment of r/birthcontrol to the start date of analysis (July 21, 2011, to December 31, 2020). User interactions within the subreddit were analyzed to describe community use over time, specifically the commonality of use based on the volume of posts, the length of posts (character count), and the proportion of posts with any and each flair applied. "Popular" posts on r/birthcontrol were determined based on the number of comments and "scores," or upvotes minus downvotes; popular posts had 9 comments and a score of ≥3. Term Frequency-Inverse Document Frequency (TF-IDF) analyses were run on all posts with flairs applied, posts within each flair group, and popular posts within each flair group to characterize and compare the distinctive language used in each group.

RESULTS

There were 105,485 posts to r/birthcontrol during the study period, with the volume of posts increasing over time. Within the time frame for which flairs were available on r/birthcontrol (after February 4, 2016), users applied flairs to 78% (n=73,426) of posts. Most posts contained exclusively textual content (n=66,071, 96%), had comments (n=59,189, 86%), and had a score (n=66,071, 96%). Posts averaged 731 characters in length (median 555). "SideEffects!?" was the most frequently used flair overall (n=27,530, 40%), while "Experience" (n=719, 31%) and "SideEffects!?" (n=672, 29%) were most common among popular posts. TF-IDF analyses of all posts showed interest in contraceptive methods, menstrual experiences, timing, feelings, and unprotected sex. While TF-IDF results for posts with each flair varied, the contraceptive pill, menstrual experiences, and timing were discussed across flair groups. Among popular posts, intrauterine devices and contraceptive use experiences were often discussed.

CONCLUSIONS

People commonly wrote about contraceptive side effects and experiences using methods, highlighting the value of r/birthcontrol as a space to post about aspects of contraceptive use that are not well addressed by clinical contraceptive counseling. The value of real-time, open-access data on contraceptive users' interests is especially high given the shifting landscape of and increasing constraints on reproductive health care in the United States.

COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit

Background

Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline.

Methods

We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits.

Results

There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal).

Conclusions

Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018

Social media, and in particularly Twitter, can be a resource of enormous value to retrieve information about the opinion of general population to vaccines. The increasing popularity of this social media has allowed to use its content to have a clear picture of their users on this topic. In this paper, we perform a study about vaccine-related messages published in Spanish during 2015-2018. More specifically, the paper has focused on two specific diseases: influenza and measles (and MMR as its vaccine). By also including an analysis about the sentiment expressed on the published tweets, we have been able to identify the type of messages that are published on Twitter with respect these two pathologies and their vaccines. Results showed that in contrary on popular opinions, most of the messages published are non-negative. On the other hand, the analysis showed that some messages attracted a huge attention and provoked peaks in the number of published tweets, explaining some changes in the observed trends.

Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques

Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem.

What are Ophthalmology Patients Asking Online? An Analysis of the Eye Triage Subreddit

Importance

Ophthalmology patients are seeking medical advice on social media websites like Reddit, where users are able to post comments and discuss issues pertaining to different topics that are organized in ‘subreddits’. Understanding which issues are most pertinent will guide ophthalmic providers in delivering more effective patient education.

Methods

This cross-sectional study assessed a systematic sample of the first 22 posts and their top 3 comments from each month since January 27th, 2019, the subreddit’s creation. Information was gathered from reddit.com/r/eyetriage in October 2019 and analyzed in November 2019.

Main Outcomes

The posts were characterized by date and time, inclusion of an image, type, content, emotional tone, and number of upvotes and comments. The comments were categorized based on content, emotional tone, time of comment, and user background. Post and comment content codes were categorized in an iterative manner with differences resolved by author consensus. Categorical statistics were compiled.

Results

Two hundred posts and 456 comments were analyzed since the creation of r/eyetriage, a forum created exclusively for patients to seek advice from health-care professionals. Twenty-six (13%) of the total posts included an image. On average, comments received 1.76 ± 2.17 upvotes along with 4.50 ± 4.47 replies. The most common content codes among the posts were 42 (21.0%) seeking diagnoses, 23 (11.5%) surgical complications, and 13 (6.50%) alternative medication options. Eighty-two (41%) posts conveyed a clear emotional tone, most notably 11 (13.4%) with anxiety and 10 (12.2%) with worry. The top comments came from 165 (36.2%) self-identified patients, 151 (33.1%) optometrists, and 49 (10.8%) ophthalmologists. The top comment codes for replies included 158 (34.7%) with treatment advice, 70 (15.4%) with advice deferred to follow-up appointment with other health-care specialists, and 60 (13.2%) with sharing information.

Conclusions

Patients are asking ophthalmology-related questions on the Eye Triage subreddit, and they are more likely to receive information from other patients or optometrists than from self-identified ophthalmologists. When emotions were revealed, patients were often anxious and worried. Opportunities exist for ophthalmologists to take a more active role on this subreddit and help educate patients.