e.g. mhealth
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Skip search results from other journals and go to results- 12 Journal of Medical Internet Research
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- 1 JMIR mHealth and uHealth
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Impact of Social Media Influencers on Amplifying Positive Public Health Messages
tweets
J Med Internet Res 2025;27:e73062
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Final keywords
Data extraction
An academic researcher access was applied for with Twitter.
867,485 tweets extracted.
Allocate sentiment to each tweet
Random selection of 260 tweets for sentiment allocation (positive, negative, and neutral).
A total of 3 coders manually assigned sentiments to 1 set of 260 random tweets.
Using majority voting, out of 260 tweets, coders agreed on 247 tweets (2 or 3 coders voted the same).
Kappa statistic calculated between all coders and the models.
JMIR Form Res 2025;9:e59687
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Understanding Loneliness Through Analysis of Twitter and Reddit Data: Comparative Study
Relevant tweets about loneliness were gathered and stored in a database. Twitter data analysis involved the following 3 stages: (1) data collection (tweet collection); (2) division of the collected data into negative and other tweets through preliminary analysis (sentiment analysis of tweets); and (3) further analysis of tweets with negative sentiments through manual coding to find relevant themes and categories (manual coding and analysis of tweets).
Pipeline for processing Twitter data.
Interact J Med Res 2025;14:e49464
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It is worth noting that most of the tweets posted by nonbot users came from official accounts of institutions such as the World Health Organization, ministries of health, or entities related to public health. These messages focused on reporting on the evolution of the pandemic; the number of deaths; infection rates; and the health measures implemented, such as lockdowns and vaccination campaigns to contain the spread of the virus.
JMIR Infodemiology 2025;5:e50021
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Tweets were deduplicated before analysis, as some accounts reshared content on multiple days. The distribution of these tweets across the timeline is depicted in Figure 2.
The number of tweets containing a vaccine-related keyword per day for each group over the investigation period.
To evaluate the stance expressed in these tweets regarding the COVID-19 vaccination, we used a pretrained transformer-based model.
J Med Internet Res 2024;26:e56651
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This contrasts with tweets that referred to drugs via their street names (eg, Vikes, Oxys, etc), where individuals would, at times, openly and informally discuss their drug use.
Furthermore, in contrast to tweets using street names, LDA more clearly categorized tweets containing brand names of drugs into specific drug categories, and as noted, many such tweets contained discussion of political events. Tweets containing street names were more difficult to classify using LDA for 2 reasons.
J Med Internet Res 2024;26:e57885
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A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study
To classify the full set of tweets, we first manually coded a subset of the full data set (n=10,809 tweets) into 7 emergent categories (Table 1). These human-labeled tweets were used as the training data set to train a supervised machine learning algorithm to classify the remaining tweets. Figure 1 illustrates the mixed methods approach.
Coding protocol.
Mixed methods approach. API: application programming interface; LDA: latent Dirichlet allocation; SA: sexual assault.
J Med Internet Res 2024;26:e51698
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Removing duplicates resulted in a total of 161,135 tweets from 40,289 unique users. All unique tweets were used for lexicon-based sentiment analysis. Among the 161,135 tweets from 40,289 unique accounts, we then randomly sampled a subset of 800 tweets and analyzed them using a directed content approach. Of the subset of tweets, 300 were randomly sampled and proportionately stratified by pandemic period (prepandemic, during the pandemic, and postpandemic).
JMIR Cancer 2024;10:e52061
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We removed tweets that were advertisements, pornography, or from accounts that became private or were suspended between the data capture and the qualitative coding process in April 2021 (Figure 1). Of the 5811 tweets captured, we randomly sampled 20% (n=1165) of eligible tweets captured per week during the sampling window to capture conversation from the entire sampling window, consistent with other studies of tweets [18]. Table 1 contains paraphrased tweets to protect the privacy of the users.
JMIR Dermatol 2024;7:e54052
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Use of Social Media for Health Advocacy for Digital Communities: Descriptive Study
An analysis of 750 randomly selected tweets from 188 different civil rights and advocacy organizations found that only 2% of sampled tweets leveraged partners through showcasing public events and direct action [18]. In the analysis, only 0.27% (ie, 3 of 750 Tweets analyzed) of sampled tweets incorporated the advocacy tactic of coalition building by promoting community and professional partnerships.
JMIR Form Res 2023;7:e51752
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