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

Suh, Bongwon; Hong, Lichan; Pirolli, Peter; H., Ed

doi:10.1109/socialcom.2010.33

Cited by 924 publications

(828 citation statements)

References 8 publications

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“…Its significance is obvious from the facts that the counters which measure the popularity of tweets are everywhere on the web and that many companies are using Twitter as an advertising tool. There have already been many data related to Twitter [1,2,3,4,5,6,7] presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

A stochastic model of tweet diffusion on the Twitter network

Kawamoto

2013

Physica A: Statistical Mechanics and its Applications

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Section: Introductionmentioning

confidence: 99%

A stochastic model of tweet diffusion on the Twitter network

Kawamoto

2013

Physica A: Statistical Mechanics and its Applications

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“…Interestingly, "ratios of retweets and all tweets (Ratios)" are the best features, performing significantly better than "past tweets (Pop) or retweets (RT)". Out-ofTwitter popularity measured by the number of URLs has been found to be a good predictor for other tasks (Petrovic et al, 2011;Suh et al, 2010). However, Popularity features (Pop and RT) perform better than the Social and URLs features for our task.…”

Section: Basic Accuracy Resultsmentioning

confidence: 67%

“…Previous work discusses a variety of prediction tasks for Twitter like box office forecasting of movies (Asur and Huberman, 2010), predicting retweetability of tweets (Hong et al, 2011;Petrovic et al, 2011;Suh et al, 2010), predicting for a pair of users, whether a tweet written by one will be retweeted by the other user (Zaman et al, 2010), and predicting information diffusion (Yang and Counts, 2010). Such prediction models deal with single messages and hence, cannot be used directly to predict the future popularity of an event, which is a collection of messages and their retweets.…”

Section: Predictive Analysis On Twitter and Other Platformsmentioning

confidence: 99%

Predicting future popularity trend of events in microblogging platforms

Gupta¹,

Gao

Zhai³

et al. 2012

Proc of Assoc for Info

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The fast information sharing on Twitter from millions of users all over the world leads to almost real-time reporting of events. It is extremely important for business and administrative decision makers to learn events' popularity as quickly as possible, as it can buy extra precious time for them to make informed decisions. Therefore, we introduce the problem of predicting future popularity trend of events on microblogging platforms. Traditionally, trend prediction has been performed by using time series analysis of past popularity to forecast the future popularity changes. As we can encode the rich Twitter dynamics using a rich variety of features from microblogging data, we explore regression, classification and hybrid approaches, using a large set of popularity, social and event features, to predict event popularity. Experimental results on two real datasets of 18382 events extracted from ~133 million tweets show the effectiveness of the extracted features and learning approaches. The predicted popularity trend of events can be directly used for a variety of applications including recommendation systems, ad keywords bidding price decisions, stock trading decisions, dynamic ticket pricing for sports events, etc.

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“…. -will affect the number of responses [2], [3], [4], [5], [6], [7], [8]. We will study the influence of the content features on a tweet's popularity, as these are most easily adaptable in comparison to other kinds of features.…”

Section: Introductionmentioning

confidence: 99%

“…RELATED WORK It has been found that the popularity of a tweet is influenced by some of the tweet's properties [2]. We distinguish a number of feature families.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Popularity of Twitter Messages through User Categories

Lemahieu

Canneyt

Boom

et al. 2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

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Abstract-In this paper, we investigate how the category of a Twitter user can be used to better predict and optimize the popularity of tweets. The contributions of this paper are threefold. First, we compare the influence of content features on the popularity of tweets for different user categories. Second, we present a regression model to predict the popularity of tweets given the content features as input. To construct this model, we interpolate a generic regression model, which is trained on all data, and a category-specific model, which is only trained on tweets from users of the same category as the user of the given tweet. In this way we can combine the advantage of the robustness of a generic model, with the ability of categoryspecific models to pick up on category-specific influence of content features. The third contribution is the investigation of the feasibility of boosting the popularity of a tweet by setting up an experiment in which we proactively adapt content features in order to optimize the popularity of tweets. Based on this research, we conclude that the introduction of user categories leads to a more precise analysis and better predictions. In the hands-on experiment, we observed a gain in popularity by proactively adapting content features.

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

Cited by 924 publications

References 8 publications

A stochastic model of tweet diffusion on the Twitter network

A stochastic model of tweet diffusion on the Twitter network

Predicting future popularity trend of events in microblogging platforms

Optimizing the Popularity of Twitter Messages through User Categories

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