A Bayesian Approach for Predicting the Popularity of Tweets

@article{Zaman2013ABA,
  title={A Bayesian Approach for Predicting the Popularity of Tweets},
  author={Tauhid Zaman and Emily B. Fox and Eric T. Bradlow},
  journal={ArXiv},
  year={2013},
  volume={abs/1304.6777}
}
We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or "graph" structure of the retweeters. We obtain good step ahead forecasts… 

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References

SHOWING 1-10 OF 30 REFERENCES

Predicting Information Spreading in Twitter

TLDR
A new methodology for predicting the spread of information in a social network based on data of who and what was retweeted and a probabilistic collaborative filter model to predict future retweets is presented.

Bad news travel fast: a content-based analysis of interestingness on Twitter

TLDR
This paper analyzes a set of high- and low-level content-based features on several large collections of Twitter messages to obtain insights into what makes a message on Twitter worth retweeting and, thus, interesting.

Predicting popular messages in Twitter

TLDR
It is shown that the method can successfully predict messages which will attract thousands of retweets with good performance and formulate the task into a classification problem and study two of its variants by investigating a wide spectrum of features based on the content of the messages.

The Pulse of News in Social Media: Forecasting Popularity

TLDR
This paper constructs a multi-dimensional feature space derived from properties of an article and evaluates the efficacy of these features to serve as predictors of online popularity and demonstrates that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy.

What is Twitter, a social network or a news media?

TLDR
This work is the first quantitative study on the entire Twittersphere and information diffusion on it and finds a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.

Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

TLDR
It is found that, amongst content features, URLs and hashtags have strong relationships with retweetability and the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, thenumber of past tweets does not predict retweetability of a user's tweet.

Measuring User Influence in Twitter: The Million Follower Fallacy

TLDR
An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.

Everyone's an influencer: quantifying influence on twitter

TLDR
It is concluded that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects and that predictions of which particular user or URL will generate large cascades are relatively unreliable.

Information resonance on Twitter: watching Iran

TLDR
This paper collected tweets of more than 20 million publicly accessible users on Twitter and analyzed over three million tweets related to the Iranian election posted by around 500K users during June and July of 2009, providing several key insights into the dynamics of information propagation that are special to Twitter.

The structure of online diffusion networks

TLDR
This work describes the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing, and finds strikingly similar patterns across all domains.