Predicting microblog users’ lifetime activities – A user-based analysis

Chen, Xi; Geng, Ruibin; Cai, Shun

doi:10.1016/j.elerap.2014.06.001

Cited by 12 publications

(10 citation statements)

References 33 publications

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“…We cluster microblogs into different topics based their correlation with the event, and revise the formula (8). Topics are detected in two steps: we segment the text content of microblogs by "jieba" library of python, and delete the stop words to get the word set; then we cluster the word set into several categories by kmeans method [32], which is regarded as the topics of the microblogs. In particular, the number of the topics are determined by SSE (sum of the squared errors) [33] and silhouette coefficient [34].…”

Section: B the Event Attention Degreementioning

confidence: 99%

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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Sina Weibo has significantly impact on the information diffusion processes in many realworld social events. A large number of active users on Sina Weibo not only push the opinion diffusion, but also increase the influence abilities of events which conversely attracted much attentions of users to follow them. How to effectively track the event attention of users is one of the most important channels to get the public opinions. In order to predict the event attention more accurately, motivated by observations of social events' influence concerning with users and microblogs, we quantify the user popularity from the four dimensions: the user activity, the user behavior, the user authenticity and the user infection ability. And the non-collinearity of these four dimensions is tested to ensure the comprehensiveness and nonredundancy of the evaluation. Then, combining with the logic framework of Hidden Markov Model, we propose an algorithm to predict the Weibo event attention by using the user popularity. Meanwhile, in order to better detect the performance of the prediction algorithm, we integrate the static and dynamic information of microblog content to directly quantify the current Weibo event attention as a benchmark, and the performance of four prediction algorithms (including our algorithm) is tested with six real data sets which are chosen from the popular events in China from 2019 to 2020. Through comparison, we find that the user popularity can be used to predict the event attention, and the Hidden Markov Model prediction method by using the user popularity shows good prediction performance.

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Section: B the Event Attention Degreementioning

confidence: 99%

Tracking Attention of Social Media Event by Hidden Markov Model–Cases from Sina Weibo

Ma¹,

Jiao²,

Song³

2021

IEEE Access

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“…The smaller the perplexity is, the stronger its generalization ability is: (20) , ) ( log exp ) ( perplexity According to Fig. 2, when the topic number is 700, the perplexity value is relatively stable.…”

Section: Perplexitymentioning

confidence: 99%

“…We select two time intervals, such as natural time interval and microblog operating time interval [20]. Natural time interval means any time period of our daily life.…”

Section: Choose the Time Intervalmentioning

confidence: 99%

An Indirected Recommendation Model for Chinese Microblog

Duan

Dong

Mei

2016

Cybernetics and Information Technologies

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Microblog is a browser-based platform for web user's information sharing and communication. With the rapidly increasing of microblog population, its recommendation function becomes necessary. This paper proposes the recommendation by the Latent Dirichlet Allocation topic model, which combines the user interests into the model to meet their needs. We also conduct a comparative analysis between indirect and direct recommendation algorithms. The experimental resultsshow that the indirect recommendation is more effective for the micro-blog recommendation.

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“…Bravo-Marquez et al, (2016) made a study how to compute our word-level sentiment association attributes from tweets annotated with both hard and soft labels obtained from different collections of automatically labeled tweets. Chen et al, (2015) made an analysis on individual behavior rather than the micro-blogs created by an individual user, considered original words and retweet others' tweets for predicting future vitality. Alahmadi and Zeng (2015) had proposed Implicit Social Trust and Sentiment (ISTS) based Re-commander System RS on Online Social Networks (OSNs) by utilizing the implicit trust between friends and the sentiment they hold in their posts, also employed a probabilistic method to support the illustration of the intensity of sentiment in micro-reviews into numerical rating scales.…”

Section: Related Workmentioning

confidence: 99%