Twitter data shows trending topics tend to change rapidly

The social network releases new stats and data visualizations on what time of day users tweet and how quickly the phrases they tweet on a given subject turnover.

Twitter data visualization on the trending phrases the day Steve Jobs died. Twitter

Twitter has taken a deeper look into how quickly its users change the terms or phrases they tweet on a given topic, or their churn rate, and released some of this data today.

For example, when Steve Jobs passed away, people began rapidly including "Steve Jobs" in their tweets , but a short while later "Apple" began trending, then "Pixar," and finally "Stay foolish."

"Looking at terms and phrases in Tweets and in real-time search queries, we see that the most frequent terms in one hour or day tend to be very different from those in the next -- significantly more so than in other content on the web," Twitter research scientist on analytics Jimmy Lin wrote in a blog post.

Here are some of the churn highlights that Twitter engineers found:

  • Examining all search queries from October 2011, we see that, on average, about 17% of the top 1000 query terms from one hour are no longer in the top 1000 during the next hour. In other words, 17% of the top 1000 query terms "churn over" on an hourly basis.
  • Repeating this at a granularity of days instead of hours, we still find that about 13% of the top 1000 query terms from one day are no longer in the top 1000 during the next day.
  • During major events, the frequency of queries spike dramatically. For example, on October 5, immediately following news of the death of Apple co-founder and CEO Steve Jobs, the query "steve jobs" spiked from a negligible fraction of query volume to 15% of the query stream -- almost one in six of all queries issued! Check it out: the query volume is literally off the charts! Notice that related queries such as "apple" and "stay foolish" spiked as well.

Along with the churn research, Twitter engineers also looked into what times of day and year users from four different cities tweet. Studying New York, Istanbul, Tokyo, and Sao Paulo, the engineers investigated why "growth patterns in Tweet-production experience seasonal variations."

They found that users in Tokyo tend to be using the social network mostly in the evening, while users in the other cities tweet more during the day. They also concluded that users in Sao Paulo take a rest in the afternoon and then have longer nights throughout the entire year compared with the other three cities.

Twitter data visualization on when users are using the social network in New York, Tokyo, Istanbul, and Sao Paulo. Twitter

 

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