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Analyzing and predicting news popularity on Twitter

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  • Wu, Bo
  • Shen, Haiying

Abstract

Twitter is not only a social network, but also an increasingly important news media. In Twitter, retweeting is the most important information propagation mechanism, and supernodes (news medias) that have many followers are the most important information sources. Therefore, it is important to understand the news retweet propagation from supernodes and predict news popularity quickly at the very first few seconds upon publishing. Such understanding and prediction will benefit many applications such as social media management, advertisement and interaction optimization between news medias and followers. In this paper, we identify the characteristics of news propagation from supernodes from the trace data we crawled from Twitter. Based on the characteristics, we build a news popularity prediction model that can predict the final number of retweets of a news tweet very quickly. Through trace-driven experiments, we then validate our prediction model by comparing our predicted popularity and real popularity, and show its superior performance in comparison with the regression prediction model. From the study, we found that the average interaction frequency between the retweeters and the news source is correlated with news popularity. Also, the negative sentiment of news has some correlations with retweet popularity while the positive sentiment of news does not have such obvious correlation.

Suggested Citation

  • Wu, Bo & Shen, Haiying, 2015. "Analyzing and predicting news popularity on Twitter," International Journal of Information Management, Elsevier, vol. 35(6), pages 702-711.
    • Handle: RePEc:eee:ininma:v:35:y:2015:i:6:p:702-711
    DOI: 10.1016/j.ijinfomgt.2015.07.003
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    References listed on IDEAS

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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
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    2. Aswani, Reema & Kar, Arpan Kumar & Ilavarasan, P. Vigneswara & Dwivedi, Yogesh K., 2018. "Search engine marketing is not all gold: Insights from Twitter and SEOClerks," International Journal of Information Management, Elsevier, vol. 38(1), pages 107-116.
    3. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.
    4. Cheng-Jun Wang & Xinzhi Zhang & Zepeng Gou & Youqin Wu, 2024. "Yesterday once more: collective storytelling and public engagement with digital cultural products on the music streaming platform," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
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    7. Nisar, Tahir M. & Prabhakar, Guru & Patil, Pushp P., 2018. "Sports clubs’ use of social media to increase spectator interest," International Journal of Information Management, Elsevier, vol. 43(C), pages 188-195.
    8. Jiayin Pei & Zhi Lu & Xiaoming Yang, 2022. "What drives people to repost social media messages during the COVID‐19 pandemic? Evidence from the Weibo news microblog," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1609-1626, December.
    9. Han, Zhongya & Tang, Zhongjun & He, Bo, 2022. "Improved Bass model for predicting the popularity of product information posted on microblogs," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    10. Martínez-Rojas, María & Pardo-Ferreira, María del Carmen & Rubio-Romero, Juan Carlos, 2018. "Twitter as a tool for the management and analysis of emergency situations: A systematic literature review," International Journal of Information Management, Elsevier, vol. 43(C), pages 196-208.

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