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Can Cross-Lingual Information Cascades Be Predicted on Twitter?

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Social Informatics (SocInfo 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10539))

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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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Notes

  1. 1.

    https://dev.twitter.com/overview/api.

  2. 2.

    https://github.com/shuyo/language-detection.

  3. 3.

    Liblinear: https://www.csie.ntu.edu.tw/~cjlin/liblinear/.

  4. 4.

    Multi-output regression: http://scikit-learn.org/stable/modules/multiclass.html.

  5. 5.

    https://en.wikipedia.org/wiki/Help:Interlanguage_links.

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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.

Author information

Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Hongshan Jin

  2. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Masashi Toyoda & Naoki Yoshinaga

Authors
  1. Hongshan Jin
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  2. Masashi Toyoda
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  3. Naoki Yoshinaga
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Corresponding author

Correspondence to Hongshan Jin .

Editor information

Editors and Affiliations

  1. Indiana University, Bloomington, Indiana, USA

    Giovanni Luca Ciampaglia

  2. University of Washington, Seattle, Washington, USA

    Afra Mashhadi

  3. University of Oxford, Oxford, United Kingdom

    Taha Yasseri

A List of Features Used for Learning

A 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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67216-8

  • Online ISBN: 978-3-319-67217-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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