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Do News and Social Media Tell the Same Story? Constructing and Comparing Sentiment Spillover Networks

Author

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  • Fan Wu
  • Anqi Liu
  • Jing Chen
  • Yuhua Li

Abstract

Investor sentiment reflects the collective attitude of investors towards the asset, whether positive, negative or neutral. Market information, such as news and relevant social media posts, plays a significant role in shaping investor sentiment, which influences investment decisions accordingly. The sentiment for one single company may spill over to other relevant companies which are in the same industry. The information spillover network pattern between news and social media may also differ, as they are two different media sources. In this study, we introduce a network-based transfer entropy method to measure and compare the information transmission of news and social media sentiment across the technology companies. We examine whether and to what extent sentiment information from one company can transfer to other companies, and how different the spillover effect is for news and social media. The result signifies a stronger intensity of news information flow among the tech companies after COVID-19. We also highlight the companies which act as information hubs in the sentiment network. Furthermore, we identify the companies which lead the strongest information flow chain. Overall, this study provides a novel perspective in modelling sentiment spillover under two different media sources, and we find that news and social media show a different information transmission pattern during the studied period.

Suggested Citation

  • Fan Wu & Anqi Liu & Jing Chen & Yuhua Li, 2026. "Do News and Social Media Tell the Same Story? Constructing and Comparing Sentiment Spillover Networks," Papers 2604.26811, arXiv.org, revised May 2026.
  • Handle: RePEc:arx:papers:2604.26811
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    References listed on IDEAS

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    1. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    2. Kewei Hou, 2007. "Industry Information Diffusion and the Lead-lag Effect in Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 20(4), pages 1113-1138.
    3. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    4. Wu, Fei & Zhao, Wan-Li & Ji, Qiang & Zhang, Dayong, 2020. "Dependency, centrality and dynamic networks for international commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 118-132.
    5. Bukovina, Jaroslav, 2016. "Social media big data and capital markets—An overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 11(C), pages 18-26.
    6. Rerotlhe B. Basele & Peter C. B. Phillips & Shuping Shi, 2025. "Speculative Bubbles in the Recent AI Boom: Nasdaq and the Magnificent Seven," Journal of Time Series Analysis, Wiley Blackwell, vol. 46(5), pages 814-828, September.
    7. Lei Zhang & Chao Wang & Hong Yao, 2021. "Information Contagion and Stock Price Crash Risk," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, February.
    8. Kwon, Okyu & Yang, Jae-Suk, 2008. "Information flow between composite stock index and individual stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2851-2856.
    9. Gu, Chen & Kurov, Alexander, 2020. "Informational role of social media: Evidence from Twitter sentiment," Journal of Banking & Finance, Elsevier, vol. 121(C).
    10. Shang, Jin & Hamori, Shigeyuki, 2021. "Do crude oil prices and the sentiment index influence foreign exchange rates differently in oil-importing and oil-exporting countries? A dynamic connectedness analysis," Resources Policy, Elsevier, vol. 74(C).
    11. Gao, Yang & Zhao, Chengjie, 2023. "Investor sentiment and stock price jumps: A network analysis based on China’s carbon–neutral sectors," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    12. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    13. Xingchen Wan & Jie Yang & Slavi Marinov & Jan-Peter Calliess & Stefan Zohren & Xiaowen Dong, 2020. "Sentiment Correlation in Financial News Networks and Associated Market Movements," Papers 2011.06430, arXiv.org, revised Feb 2021.
    14. Jiao, Peiran & Veiga, André & Walther, Ansgar, 2020. "Social media, news media and the stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 63-90.
    15. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    16. Zhou, Liyun & Chen, Dongqiao & Huang, Jialiang, 2023. "Stock-level sentiment contagion and the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    17. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    18. Fernando Garcia Alvarado, 2022. "Detecting crisis vulnerability using yield spread interconnectedness," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3864-3880, October.
    19. Plakandaras, Vasilios & Tiwari, Aviral Kumar & Gupta, Rangan & Ji, Qiang, 2020. "Spillover of sentiment in the European Union: Evidence from time- and frequency-domains," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 105-130.
    20. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    21. Mensi, Walid & Gubareva, Mariya & Teplova, Tamara & Kang, Sang Hoon, 2023. "Spillover and connectedness among G7 real estate investment trusts: The effects of investor sentiment and global factors," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    22. Dash, Saumya Ranjan & Maitra, Debasish, 2019. "The relationship between emerging and developed market sentiment: A wavelet-based time-frequency analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 135-150.
    23. Audrino, Francesco & Tetereva, Anastasija, 2019. "Sentiment spillover effects for US and European companies," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 542-567.
    24. Song, Yingjie & Ji, Qiang & Du, Ya-Juan & Geng, Jiang-Bo, 2019. "The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets," Energy Economics, Elsevier, vol. 84(C).
    25. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    26. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    27. Okyu Kwon & Jae-Suk Yang, 2008. "Information flow between stock indices," Papers 0802.1747, arXiv.org.
    28. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    29. Cookson, J. Anthony & Fox, Corbin & Gil-Bazo, Javier & Imbet, Juan F. & Schiller, Christoph, 2026. "Social media as a bank run catalyst," Journal of Financial Economics, Elsevier, vol. 176(C).
    30. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    31. Naeem, Muhammad Abubakr & Senthilkumar, Arunachalam & Arfaoui, Nadia & Mohnot, Rajesh, 2024. "Mapping fear in financial markets: Insights from dynamic networks and centrality measures," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    32. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2022. "Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network," International Review of Financial Analysis, Elsevier, vol. 84(C).
    33. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    34. Bouteska, Ahmed & Ha, Le Thanh & Bhuiyan, Faruk & Sharif, Taimur & Abedin, Mohammad Zoynul, 2024. "Contagion between investor sentiment and green bonds in China during the global uncertainties," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 469-484.
    35. Zi-Sheng Ouyang & Ying Huang & Yun Jia & Chang-Qing Luo, 2020. "Measuring Systemic Risk Contagion Effect of the Banking Industry in China: A Directed Network Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(6), pages 1312-1335, May.
    36. Kingstone Nyakurukwa & Yudhvir Seetharam, 2025. "Investor sentiment networks: mapping connectedness in DJIA stocks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-19, December.
    37. Fan Wu & Anqi Liu & Jing Chen & Yuhua Li, 2024. "Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry," JRFM, MDPI, vol. 17(10), pages 1-19, September.
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