Multi-platform content analysis examining health communication and misinformation on Meta platforms (Facebook and Instagram), using topic modeling and computational text analysis in R.
| Repository | Description |
|---|---|
| 🦠 facebook-reactions-covid19-india | PhD thesis project |
| ⏱️ timeseries-facebook-engagement-r | Time-series toolkit |
| 🧠 stm-social-media-r | STM topic modeling toolkit |
| 💬 sentiment-lexicon-comparison | AFINN, Bing, NRC lexicon comparison |
| 🗳️ reddit-political-misinfo-coding | Reddit political communication manual coding |
| 🔄 cross-platform-engagement-analysis | Unified cross-platform engagement framework |
| 🔴 disinformation-detection-ml | ML classifier for disinformation detection |
| 🟣 nlp-news-classification-r | Supervised NLP news classification |
| 🟢 crowdtangle-meta-api-workflow | Academic data collection pipeline |
| 📊 survey-data-analysis-r | Survey data cleaning, Likert analysis & descriptives |
| 📝 survey-scale-validation-r | Scale validation: EFA/CFA, Cronbach alpha, reliability |
| 🧪 survey-experiment-analysis-r | Survey experiment & vignette study analysis |
- Examine health-related misinformation spread on Facebook and Instagram
- Identify key topics and narratives using STM / LDA
- Analyze engagement patterns across content types
- Compare user interaction dynamics between platforms
- Data Collection via CrowdTangle / Meta Content Library
- Preprocessing with
tidytextandquanteda - STM topic modeling with platform/time covariates
- Engagement analysis and misinformation coding
install.packages(c("tidyverse", "tidytext", "quanteda", "stm", "ggplot2", "lubridate", "jsonlite"))Sawood Anwar — PhD in Humanities, University of Urbino Carlo Bo | Defended: 22 September 2025
- 🔗 GitHub | 💼 LinkedIn | 🎓 Google Scholar
MIT License.
Keywords: Meta Platforms, Facebook, Instagram, Health Communication, Misinformation, STM, R