Predicting popular messages in Twitter

Liu, Hong; Dan, Ovidiu; Davison, Brian D.

doi:10.1145/1963192.1963222

Cited by 471 publications

(320 citation statements)

References 2 publications

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“…The experimental results show that the result of prediction model of machine learning algorithm is superior to the artificial prediction. And Hong [12] used Logistic Regression, they successfully build the prediction model and enhanced the user scale, the number of users was increased to 2.5 million by Hong. However, the research only studied whether the message will be retweeted without taking into account the retweet times of the message will be received.…”

Section: Based On Machine Learningmentioning

confidence: 99%

“…Therefore, before the establishment of regression model, we need to give a clear definition of Retweet times. This paper adopts the definition of Retweet times in the literature [12]. First, we give each micrblog text an identifier, Text content identical microblog get the same identifier, Then all microblog were classified according to those identifier.…”

Section: Retweet Times Definitionmentioning

confidence: 99%

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Prediction of Users Retweet Times In Social Network

Yu¹,

Bai²,

Huang³

et al. 2015

IJMUE

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Section: Based On Machine Learningmentioning

confidence: 99%

Section: Retweet Times Definitionmentioning

confidence: 99%

Prediction of Users Retweet Times In Social Network

Yu¹,

Bai²,

Huang³

et al. 2015

IJMUE

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show abstract

“…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%

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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Predicting popular messages in Twitter

Cited by 471 publications

References 2 publications

Prediction of Users Retweet Times In Social Network

Prediction of Users Retweet Times In Social Network

Predicting future popularity trend of events in microblogging platforms

Optimizing the Popularity of Twitter Messages through User Categories

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