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Influential STM Tweets by Topic
his document presents, for each STM topic (\(k = 10\)), the tweets that are **both**:
- **Strongly associated with the topic**: we require a topic probability \(\gamma \ge 0.5\) for that topic.
- **Highly influential in terms of engagement**: we compute an engagement score
\[
\text{engagement\_score} = \text{retweet\_count} + \text{like\_count}
\]
using the tweet-level metadata from the social network analysis (`uniques_final_chatgpt`).
For each topic, we select the **top 20 tweets** by engagement score (breaking ties by higher \(\gamma\)), and combine them into a single interactive table.
This gives us a set of **influential exemplars**: tweets that not only best express each topic linguistically, but also received substantial attention in the retweet/like economy.