Fan Wu, Anqi Liu, Jing Chen, Yuhua Li
arXiv 29 Apr 2026 · Finance — Mathematical Finance
arXiv:2604.26811 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Nyakurukwa, K. and Seetharam, Y (2025) Investor sentiment networks: mapping connectedness in DJIA stocks | 0.843 | 3 | 3 | 100% |
| 2 | Audrino, F. and Tetereva, A (2019) Sentiment spillover effects for US and European companies | 0.737 | 3 | 2 | 100% |
| 3 | Mbarki, I., Omri, A., and Naeem, M. A (2022) From sentiment to systemic risk: Information transmission in Asia-Pacific stock markets | 0.737 | 3 | 2 | 100% |
| 4 | Cedric Thompson (2025) Magnificent seven stocks: What you need to know | 0.644 | 2 | 2 | 100% |
| 5 | Gu, C. and Kurov, A (2020) Informational role of social media: Evidence from Twitter sentiment | 0.644 | 2 | 2 | 100% |
| 6 | Nyakurukwa, K. and Seetharam, Y (2024) Sentimental showdown: News media vs. social media in stock markets | 0.644 | 2 | 2 | 100% |
| 7 | Reboredo, J. and Ugolini, A (2018) The impact of Twitter sentiment on renewable energy stocks | 0.644 | 2 | 2 | 100% |
| 8 | Wan, X., Yang, J., Marinov, S., Calliess, J.-P., Zohren, S., and Don… (2021) Sentiment correlation in financial news networks and associated market movements | 0.644 | 2 | 2 | 100% |
| 9 | Wu, F., Liu, A., Chen, J., and Li, Y (2024) Analysing Network Dynamics: The Contagion Effects of SVB’s Collapse on the US Tech Industry self | 0.644 | 2 | 2 | 100% |
| 10 | CPRAM (2024) Tech: A new bubble or a new cycle? | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 77 scored citations.