Examining the consumption of radical content on YouTube
- Daily share of news consumption on YouTube, a social media platform with more than 2 billion monthly users, has increased in the last few years. Constructing a large dataset of users’ trajectories across the full political spectrum during 2016–2019, we identify several distinct communities of news consumers, including “far-right” and “anti-woke.” Far right is small and not increasing in size over the observation period, while anti-woke is growing, and both grow in consumption per user. We find little evidence that the YouTube recommendation algorithm is driving attention to this content. Our results indicate that trends in video-based political news consumption are determined by a complicated combination of user preferences, platform features, and the supply-and-demand dynamics of the broader web.
- I now have 3 and 6 epoch runs for name, review, stars models.
- Evaluate stars to see how much has changed
- Maybe try to train up a bigger model? Start with the xl model and step back to find the largest model that will fit. Then train that with the name, review, stars corpora
- Nope, the 117m model is the biggest that will fit. When I’ve got the time try the Huggingface Course and see how to do cloud training
- 3:00 Meeting. Went over results and the mapping tool proposal
- Need to adjust the counts to relative percent for easier compare
- Try training a model from scratch on the stars/votes corpora? Thaty way we could see if it learns the ratios better. This could be an artifact from finetuning
- Create models for review+star since the name sets up the review
- Sprint planning
- Plan LM Epic – DSR-646
- SMD conference – DSR-645
- Long-ish chat with Rukan about transforms in scene graphs