Enhance content discoverability: Use relevant keywords, hashtags, and mentions in your tweets, making it easier for users to find and engage with your content. This increased discoverability may help improve the ranking of your content by the Heavy Ranker.
Leverage multimedia content: Experiment with different content formats, such as videos, images, and GIFs, which may capture users’ attention and increase engagement, resulting in better ranking by the Heavy Ranker.
User feedback: Monitor and respond to feedback for your content. Positive feedback may improve your ranking, while negative feedback provides an opportunity to learn and improve.
Note that the Heavy Ranker uses a combination of machine learning models and various features to rank the content. While the above actions may help influence the ranking, there are no guarantees as the ranking process is determined by a complex algorithm, which evolves over time.
-> Question: why threads and long tweets do so well on the platform?
Answer: Threads and long tweets perform well on the platform for several reasons:
More content and context: Threads and long tweets provide more information and context about a topic, which can make the content more engaging and informative for users. People tend to appreciate a well-structured and detailed explanation of a subject or a story, and threads and long tweets can do that effectively.
Increased user engagement: As threads and long tweets provide more content, they also encourage users to engage with the tweets through replies, retweets, and likes. This increased engagement can lead to better visibility of the content, as the Twitter algorithm considers user engagement when ranking and surfacing tweets.
Narrative structure: Threads enable users to tell stories or present arguments in a step-by-step manner, making the information more accessible and easier to follow. This narrative structure can capture users’ attention and encourage them to read through the entire thread and interact with the content.
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/code/twitter-the-algorithm-analysis-deeplake.html