Content analysis of Twitter in relation to biological treatments for chronic inflammatory arthropathies: an exploratory study

Using Twitter Data Analysis to Understand the Perceptions, Beliefs, and Attitudes about Pharmacotherapy Used in Rheumatology: An Observational Study

Twitter has become an important platform for disseminating information about rheumatology drugs by patients, health professionals, institutions, and other users. The aim of this study was to analyze tweets related to 16 drugs used in rheumatology, including their volume, content, and type of user (patients, patients' relatives, health professionals, health institutions, pharmaceutical industry, general press, scientific journals and patients' associations), and to detect inappropriate medical content. A total of 8829 original tweets were obtained, with a random sample of 25% of the total number of tweets for each drug (at least 100 tweets) analyzed. Methotrexate (MTX) accounted for a quarter of all tweets, and there were significant differences in the proportion of tweets issued according to the type of user. Patients and their relatives mainly tweeted about MTX, while professionals, institutions, and patient associations posted more about TNF inhibitors. In contrast, the pharmaceutical industry focused on IL-17 inhibitors. Medical content prevailed in all drugs except anti-CD20 and IL-1 inhibitors and the most discussed medical topic was efficacy, followed by posology and adverse effects. Inappropriate or fake content was found to be very low. In conclusion, the majority of the tweets were about MTX, which is a first-line treatment for several diseases. The distribution of medical content varied according to the type of user. In contrast to other studies, the amount of medically inappropriate content was very low.

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Background

There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs.

Objective

We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs.

Methods

We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea.

Results

Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry.

Conclusions

After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.

Analysis of Tweets Containing Information Related to Rheumatological Diseases on Twitter

BACKGROUND

Tweets often indicate the interests of Twitter users. Data from Twitter could be used to better understand the interest in and perceptions of a variety of diseases and medical conditions, including rheumatological diseases which have increased in prevalence over the past several decades. The aim of this study was to perform a content analysis of tweets referring to rheumatological diseases.

METHODS

The content of each tweet was rated as medical (including a reference to diagnosis, treatment, or other aspects of the disease) or non-medical (such as requesting help). The type of user and the suitability of the medical content (appropriate content or, on the contrary, fake content if it was medically inappropriate according to the current medical knowledge) were also evaluated. The number of retweets and likes generated were also investigated.

RESULTS

We analyzed a total of 1514 tweets: 1093 classified as medical and 421 as non-medical. The diseases with more tweets were the most prevalent. Within the medical tweets, the content of these varied according to the disease (some more focused on diagnosis and others on treatment). The fake content came from unidentified users and mostly referred to the treatment of diseases.

CONCLUSIONS

According to our results, the analysis of content posted on Twitter in regard to rheumatological diseases may be useful for investigating the public's prevailing areas of interest, concerns and opinions. Thus, it could facilitate communication between health care professionals and patients, and ultimately improve the doctor-patient relationship. Due to the interest shown in medical issues it seems desirable to have healthcare institutions and healthcare workers involved in Twitter.