English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We predict the popularity of short messages called tweets created in themicro-blogging site known as Twitter. We measure the popularity of a tweet bythe time-series path of its retweets, which is when people forward the tweet toothers. We develop a probabilistic model for the evolution of the retweetsusing a Bayesian approach, and form predictions using only observations on theretweet times and the local network or "graph" structure of the retweeters. Weobtain good step ahead forecasts and predictions of the final total number ofretweets even when only a small fraction (i.e., less than one tenth) of theretweet path is observed. This translates to good predictions within a fewminutes of a tweet being posted, and has potential implications forunderstanding the spread of broader ideas, memes, or trends in social networks.

A Bayesian approach for predicting the popularity of tweets