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Mining Domain-Specific Accounts for Scientific Contents from Social Media

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Advances in Web-Based Learning – ICWL 2017 (ICWL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10473))

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Abstract

This paper proposes a machine learning based approach to automatically create an initial set of domain-specific accounts by matching real-world authors of the latest domain-specific publications to corresponding social media accounts. An efficient approach based on social network analysis is further applied to extend the initial set by finding more domain-specific accounts of various types and filtering out irrelevant general or non-domain-specific accounts. Our experiments on Twitter are used to verify feasibility and effectiveness of the proposed methods.

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Notes

  1. 1.

    https://support.twitter.com/articles/76460.

  2. 2.

    https://github.com/dmlc/xgboost.

References

  1. Coleman, V.: Social media as a primary source: a coming of age. EDUCAUSE Rev. 48(6), 60–61 (2013)

    Google Scholar 

  2. Hadgu, A.T., Jäschke, R.: Identifying and analyzing researchers on twitter. In: Proceedings of the 2014 ACM Conference on Web Science, WebSci 2014, pp. 23–32. ACM, New York (2014). http://doi.acm.org/10.1145/2615569.2615676

  3. Ke, Q., Ahn, Y.Y., Sugimoto, C.R.: A systematic identification and analysis of scientists on twitter. PLoS ONE 12(4), e0175368 (2017)

    Article  Google Scholar 

  4. Wang, J., Xiang, J., Uchino, K.: Topic-specific recommendation for open education resources. In: Li, F.W.B., Klamma, R., Laanpere, M., Zhang, J., Manjón, B.F., Lau, R.W.H. (eds.) ICWL 2015. LNCS, vol. 9412, pp. 71–81. Springer, Cham (2015). doi:10.1007/978-3-319-25515-6_7

    Chapter  Google Scholar 

  5. Wang, J., Xiang, J., Uchino, K.: Domain-specific recommendation by matching real authors to social media users. In: Chiu, D.K.W., Marenzi, I., Nanni, U., Spaniol, M., Temperini, M. (eds.) ICWL 2016. LNCS, vol. 10013, pp. 246–252. Springer, Cham (2016). doi:10.1007/978-3-319-47440-3_27

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Correspondence to Jun Wang .

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Wang, J., Xiang, J., Zhang, Y., Uchino, K. (2017). Mining Domain-Specific Accounts for Scientific Contents from Social Media. In: Xie, H., Popescu, E., Hancke, G., Fernández Manjón, B. (eds) Advances in Web-Based Learning – ICWL 2017. ICWL 2017. Lecture Notes in Computer Science(), vol 10473. Springer, Cham. https://doi.org/10.1007/978-3-319-66733-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-66733-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66732-4

  • Online ISBN: 978-3-319-66733-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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