Do News and Social Media Tell the Same Story? Constructing and Comparing Sentiment Spillover Networks
Fan Wu, Anqi Liu, Jing Chen, Yuhua Li

TL;DR
This paper introduces a network-based transfer entropy method to compare sentiment spillover patterns from news and social media among tech companies, revealing differences in information transmission especially post-COVID-19.
Contribution
It presents a novel approach to model and compare sentiment spillover networks from news and social media using transfer entropy, highlighting differences in information flow patterns.
Findings
Stronger news information flow among tech companies after COVID-19.
Identification of key companies acting as sentiment hubs.
Different sentiment transmission patterns observed between news and social media.
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…
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