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

2026-04-29

AI Summary

This study investigates whether news and social media exhibit distinct information spillover effects in shaping investor sentiment and how these effects propagate across technology firms. Adopting a network-based perspective, the paper introduces transfer entropy—applied for the first time in this context—to model multi-source media sentiment spillovers, integrating natural language processing, sentiment classification, and time series analysis to construct and compare sentiment spillover networks driven by each media type. The findings reveal a significant increase in cross-firm spillover intensity of news sentiment following the onset of the COVID-19 pandemic, substantial structural differences between news and social media in sentiment transmission patterns, and the identification of key hub firms and dominant transmission pathways. These results provide novel evidence on how media heterogeneity influences market sentiment dynamics.

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.

Problem

• sentiment spillover
• news
• social media
• information transmission
• investor sentiment

Innovation

• sentiment spillover
• transfer entropy
• information flow
• news vs. social media
• network analysis