ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Data and Model Biases in Social Media Analyses: A Case Study of COVID-19 Tweets.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Beyond the Posts: Analyzing Breast Implant Illness Discourse With Natural Language Processing and Deep Learning.Aesthetic surgery journal · 2025Article
- Stigma of Dementia on Social Media During World Alzheimer's Awareness Month: Thematic Analysis of Posts.JMIR formative research · 2025Article
- Tweeting for Health Using Real-time Mining and Artificial Intelligence-Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter.Journal of medical Internet research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the coronavirus disease pandemic (COVID-19), social media platforms such as Twitter have become a venue for individuals, health professionals, and government agencies to share COVID-19 information. Twitter has been a popular source of data for researchers, especially for public health studies. However, the use of Twitter data for research also has drawbacks and barriers. Biases appear everywhere from data collection methods to modeling approaches, and those biases have not been systematically assessed. In this study, we examined six different data collection methods and three different machine learning (ML) models-commonly used in social media analysis-to assess data collection bias and measure ML models' sensitivity to data collection bias. We showed that (1) publicly available Twitter data collection endpoints with appropriate strategies can collect data that is reasonably representative of the Twitter universe; and (2) careful examinations of ML models' sensitivity to data collection bias are critical.
Indexed as
Identifiers
35308985PMC8861742What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.