Classification-Based Prediction on the Retweet Actions over Microblog Dataset

Zhang, Lianshuai; Zhang, Zequn; Jin, Peiquan

doi:10.1007/978-3-642-35063-4_64

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…When we set the error threshold to about 20%, the proposed model achieved a significantly outstanding precision at about 0.5, in contrast to conventional models, which showed a precision of around 0.3 and 0.2, respectively. In [7], despite excluding 4,790 unpredictable datasets from all 8,944 test datasets, it shows the lowest performance among them. We concluded that the user-preference property is changeable as time passes and is not handled flexibly when new users are detected.…”

Section: Resultsmentioning

confidence: 98%

“…The precision in predicting the number of retweets was evaluated according to the respective error threshold. Because using only 33 test data in the previous models [4] and [7] caused low reliability, we evaluated the test data shown in Table 3 to enhance the reliability. Figure 12 shows the results of the experiment.…”

Section: Resultsmentioning

confidence: 99%

“…They called this phenomenon "user preference." Zhang and others [7] categorized the tweets based on the events and predicted the number of retweets by simulating a retweet curve. A retweet curve represents the number of retweets in a successive time unit.…”

Section: Predicting the Number Of Retweetsmentioning

confidence: 99%

“…Therefore, we considered only candidate users whose preference information was included in the training data. In [7], tweets were classified into several categories depending on the event, and they selected the top N tweets from each category that were retweeted the most. They inferred the target tweet's number of retweets by measuring the curve similarity that relies on the ratio between the target tweet and the top N tweets in each time unit.…”

Section: Comparison Of Prediction Modelsmentioning

confidence: 99%

“…In predicting the lifespan of the tweets, we use the root-mean-square error (RMSE) to obtain the time tolerance between the actual observed lifespan and the estimated lifespan. In (7), N represents the amount of test data, Lifespan r (t i ) represents the actual observed lifespan of tweet t i , and Lifespan p (t i ) represents estimated lifespan. RMSE is calculated by…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

Bae

Ryu

Kim

2014

ETRI J

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In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Predicting the Number Of Retweetsmentioning

confidence: 99%

Section: Comparison Of Prediction Modelsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

Bae

Ryu

Kim

2014

ETRI J

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A Fine-Grained Approach for Extracting Events on Microblogs

Zheng

Jin

Zhao

et al. 2014

Lecture Notes in Computer Science

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A comparative study between modern encryption algorithms based on cloud computing environment

Li¹,

Wu²,

Yu³

et al. 2013

2013 IEEE Third International Conference on Information Science and Technology (ICIST)

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Twitter is emerging social media. There is an important practical significance to study its communication effect in many aspects of the Marketing Management, Public Sentiment, Hot Extraction and so on. The retweeting scale is an important indicator to reflect the communication effect. Based on analyzing the factors, a prediction algorithm based on SVM was proposed for retweeting with five features: publisher influence, acceptor activity, interest level, content importance and intimacy. Furthermore, the prediction algorithm for retweeting scale was proposed based above, the method to evaluate the prediction accuracy was given. At last the experiment with Twitter data showed a good result that the prediction accuracy of retweeting scale was up to 86.63%. I. INTRODUCTIONWITTER is a platform based on relationship to share, propagate and receive information. People can set up individual communities through Web, mobile phone and other client, share immediately with about 140 words of text messages. Its originality, timeliness, grass-roots, arbitrariness and other characteristics brings a new type of social network to the Internet. The number of registered users has grown up to 200 million since 2006. As emerging social media, Twitter has become not only a tool to express opinions and communicate with others, but also a method of information release, public relation and marketing for government, enterprises, and organizations.Fundamentally speaking, Twitter is still communication media, and the ultimate goal is to deliver message with the best communication effect. As emerging social media, there are many unique properties compared with traditional media. Therefore, it's essential to study how to disseminate information effectively and efficiently in the new media environment of Twitter. Communication effect is a concept of the communications [1]; it refers to the overall impact and result on the acceptor and society from dissemination activities of newspaper, radio, television and other mass media. The communication effect is a abstract concept without recognized uniform evaluation criterion. Different media have different indicators to evaluate their effects, for example, circulation for newspaper, audience rating for television program, box office for movie and so on. The

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Classification-Based Prediction on the Retweet Actions over Microblog Dataset

Cited by 7 publications

References 3 publications

Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

A Fine-Grained Approach for Extracting Events on Microblogs

A comparative study between modern encryption algorithms based on cloud computing environment

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