Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network
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 keywor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9019688/ |
| _version_ | 1818616878190297088 |
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| author | Xichan Nie Wanshan Zhang Yang Zhang Dunhui Yu |
| author_facet | Xichan Nie Wanshan Zhang Yang Zhang Dunhui Yu |
| author_sort | Xichan Nie |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T16:56:48Z |
| format | Article |
| id | doaj.art-1d6040474d324cba9d3900898f166233 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:56:48Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1d6040474d324cba9d3900898f1662332022-12-21T22:23:51ZengIEEEIEEE Access2169-35362020-01-018440314404010.1109/ACCESS.2020.29774249019688Method to Predict Bursty Hot Events on Twitter Based on User Relationship NetworkXichan Nie0https://orcid.org/0000-0001-9448-3346Wanshan Zhang1Yang Zhang2Dunhui Yu3College of Computer and Information Engineering, Hubei University, Wuhan, ChinaCollege of Computer and Information Engineering, Hubei University, Wuhan, ChinaCollege of Computer and Information Engineering, Hubei University, Wuhan, ChinaCollege of Computer and Information Engineering, Hubei University, Wuhan, ChinaIn 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.https://ieeexplore.ieee.org/document/9019688/Hot event predictionsuspected bursty hot eventssemantic similarityuser relationship network |
| spellingShingle | Xichan Nie Wanshan Zhang Yang Zhang Dunhui Yu Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network IEEE Access Hot event prediction suspected bursty hot events semantic similarity user relationship network |
| title | Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network |
| title_full | Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network |
| title_fullStr | Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network |
| title_full_unstemmed | Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network |
| title_short | Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network |
| title_sort | method to predict bursty hot events on twitter based on user relationship network |
| topic | Hot event prediction suspected bursty hot events semantic similarity user relationship network |
| url | https://ieeexplore.ieee.org/document/9019688/ |
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