Abstract
Social network services (SNSs) have provided many opportunities for sharing information and knowledge in various languages due to their international popularity. Understanding the information flow between different countries and languages on SNSs can not only provide better insights into global connectivity and sociolinguistics, but is also beneficial for practical applications such as globally-influential event detection and global marketing. In this study, we characterized and attempted to detect influential cross-lingual information cascades on Twitter. With a large-scale Twitter dataset, we conducted statistical analysis of the growth and language distribution of information cascades. Based on this analysis, we propose a feature-based model to detect influential cross-lingual information cascades and show its effectiveness in predicting the growth and language distribution of cascades in the early stage.
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Acknowledgments
This work was supported by the Research and Development on Real World Big Data Integration and Analysis program of RIKEN, and the Ministry of Education, Culture, Sports, Science, and Technology, JAPAN.
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AÂ List of Features Used for Learning
AÂ List of Features Used for Learning
Root user features |
---|
Whether a user is verified number of friends/followers/followees |
Number of listed/statues/favorites |
Number of original/total tweets |
Number of reshares |
Number of reshared tweets |
Resharer features |
---|
Ratio of k resharers who are verified |
Average/max number of friends of k resharers |
Average/max number of followers of k resharers |
Average/max number of listed of k resharers |
Average/max number of statues of k resharers |
Average/max number of favorites of k resharers |
Average/max number of original tweets of k resharers |
Average/max number of total tweets of k resharers |
Average/max number of reshares of k resharers |
Average/max number of reshared tweets of k resharers |
Content features |
---|
Language of root tweet |
Whether a hashtag/mention/url is contained |
Topic distribution of the root tweet |
Structural features |
---|
Out-degree of root user and kth resharers |
In-degree of root user and kth reshares |
Number of common followers between the root user and kth resharers |
Total number of unique followers of the root user and k resharers |
Ratio of k resharers who are not first-degree connections of the root user |
Temporal features |
---|
Time interval between the root user and kth resharers |
Time interval between \(k-1th\) resharers and kth resharers |
Average time interval between first half of reshares |
Average time interval between second half of reshares |
Language features |
---|
Main language of root user |
Whether a root user is a multilingual user |
Usage rate of main language of the root user |
Whether the follower community of the root user is multilingual |
Whether the followee community of the root user is multilingual |
Language distribution of tweets of the root user |
Main language distribution of followers of the root user |
Main language distribution of followees of the root user |
Cross-lingual ratio of k resharers |
Ratio of k resharers who are multilingual users |
Ratio of k resharers whose follower community are multilingual users |
Ratio of k resharers whose followee community are multilingual users |
Average main language distribution of k resharers’ tweets |
Average main language distribution of k resharers’ followers |
Average main language distribution of k resharers’ followees |
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Jin, H., Toyoda, M., Yoshinaga, N. (2017). Can Cross-Lingual Information Cascades Be Predicted on Twitter?. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham. https://doi.org/10.1007/978-3-319-67217-5_28
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DOI: https://doi.org/10.1007/978-3-319-67217-5_28
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