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On predicting the popularity of newly emerging hashtags in Twitter

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  • Zongyang Ma
  • Aixin Sun
  • Gao Cong

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  • Zongyang Ma & Aixin Sun & Gao Cong, 2013. "On predicting the popularity of newly emerging hashtags in Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(7), pages 1399-1410, July.
    • Handle: RePEc:bla:jinfst:v:64:y:2013:i:7:p:1399-1410
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    File URL: http://hdl.handle.net/10.1002/asi.22844
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    Citations

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    Cited by:

    1. Zhao, Qihang & Feng, Xiaodong, 2022. "Utilizing citation network structure to predict paper citation counts: A Deep learning approach," Journal of Informetrics, Elsevier, vol. 16(1).
    2. António Fonseca & Jorge Louçã, 2018. "Explaining the emergence of online popularity through a model of information diffusion," Computational and Mathematical Organization Theory, Springer, vol. 24(2), pages 169-187, June.
    3. Sharad Goel & Ashton Anderson & Jake Hofman & Duncan J. Watts, 2016. "The Structural Virality of Online Diffusion," Management Science, INFORMS, vol. 62(1), pages 180-196, January.
    4. Wai Hong Tan & Feng Chen, 2021. "Predicting the popularity of tweets using internal and external knowledge: an empirical Bayes type approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 335-352, June.
    5. Cui, Hao & Kertész, János, 2023. "“Born in Rome” or “Sleeping Beauty”: Emergence of hashtag popularity on the Chinese microblog Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    6. Jabłońska-Sabuka, Matylda & Sitarz, Robert & Kraslawski, Andrzej, 2014. "Forecasting research trends using population dynamics model with Burgers’ type interaction," Journal of Informetrics, Elsevier, vol. 8(1), pages 111-122.
    7. Arora, Anuja & Bansal, Shivam & Kandpal, Chandrashekhar & Aswani, Reema & Dwivedi, Yogesh, 2019. "Measuring social media influencer index- insights from facebook, Twitter and Instagram," Journal of Retailing and Consumer Services, Elsevier, vol. 49(C), pages 86-101.
    8. Paige Brown Jarreau & Imogene A Cancellare & Becky J Carmichael & Lance Porter & Daniel Toker & Samantha Z Yammine, 2019. "Using selfies to challenge public stereotypes of scientists," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-23, May.
    9. Ali Daud & Muhammad Ahmad & M. S. I. Malik & Dunren Che, 2015. "Using machine learning techniques for rising star prediction in co-author network," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1687-1711, February.
    10. Son, Jaebong & Lee, Hyung Koo & Jin, Sung & Lee, Jintae, 2019. "Content features of tweets for effective communication during disasters: A media synchronicity theory perspective," International Journal of Information Management, Elsevier, vol. 45(C), pages 56-68.
    11. Jaebong Son & Jintae Lee & Kai R. Larsen & Jiyoung Woo, 2020. "Understanding the uncertainty of disaster tweets and its effect on retweeting: The perspectives of uncertainty reduction theory and information entropy," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1145-1161, October.

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