Abstract
In the field of hot event prediction on online social networks, not considering user information leads to poor prediction effect. In this paper, a novel method that considers the behaviors and characteristics of users is proposed to identify and predict suspected bursty hot events. First, the keywords in each tweet are extracted and divided into different sets according to part of speech, and then similar topics are clustered according to semantic similarity. Second, the growth rates of topics are monitored in the sliding timestamp and the suspected bursty hot events are marked. Then, a user relationship network is constructed based on the information of the registered users on Twitter. Finally, according to the propagation trend of suspected bursty hot events in the network, the quasi-burst hot events are marked and sorted in descending order. Experimental results show that only using the historical re-tweeting behavior of users as the judgment basis to predict the current re-tweeting probability of users will lead to the phenomenon of error cascading, while taking the information of users into account can effectively improve the prediction performance. Compared with the existing methods, the proposed method improves the prediction precision rate by 27.38%, accuracy rate by 23.49%, and recall rate by 20.16%, demonstrating that it can predict bursty hot events effectively.
Highlights
Bursty hot events in news reports spread rapidly through the Internet [1] and have a great impact on society [2]
The current mainstream academic community focuses on the information cascade, classification, and game theory models for prediction
OVERALL SOLUTION In order to solve the problem of bursty hot event prediction on Twitter, a method based on OWL is proposed to identify and monitor suspected bursty hot events (IMSB)
Summary
Bursty hot events in news reports spread rapidly through the Internet [1] and have a great impact on society [2]. X. Nie et al.: Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network a continuous time axis and added a time-delay parameter to each edge of the graph of information transmission [10], [11] to extend the ICM and LTM to asynchronous independent cascade (AsIC) and asynchronous linear threshold (AslT) models. Nie et al.: Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network a continuous time axis and added a time-delay parameter to each edge of the graph of information transmission [10], [11] to extend the ICM and LTM to asynchronous independent cascade (AsIC) and asynchronous linear threshold (AslT) models Such models assume that neighbor nodes have the same or similar influence over each other, regardless of the degree of intimacy between them. It can improve the accuracy of hot event prediction effectively and provide a new idea for bursty hot event prediction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
