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Internet Celebrity Economy: Exploring the Value of Viewers’ Comment Features and Live Streamers’
Marketing Strategies in Forecasting Revenue

1. Introduction

Nowadays, most viewers spend time watching online videos through major live-
streaming platforms, such as DouYu, Twitch, and YouTube Live. According to a Fortune

Business Insights (2021) survey, the global live-streaming market size is USD 419.03
billion in 2021. Li (2020) reported on March 16, 2020 that China's streamers had created
a market of RMB 433.8 billion in 2019. Douyu is the largest live streaming platform
in China (Tan, 2019), among which food live streaming is one of the most brilliant

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live streaming types. Live streaming will continue to grow (Appel, Grewal, Hadi, and
Stephen, 2020), and paid gifting becomes a crucial source of revenue for streamers (Zhang,
Xiang, and Hao, 2019).
Given the impact of paid gifting on Chinese streamers' revenue, it seems imperative

for practitioners of live streamers to understand exactly what influence paid gifting on
live-streaming platforms the most. Although existing research has highlighted some of
these factors such as viewers' excited comments (Zhou, Zhou, Ding, and Wang, 2019)
and emotional attachment (Wan, Lu, Wang, and Zhao, 2017); however, prior studies are

still in a relatively nascent stage, and have yet to explore the influence of viewers' various
emotion-embedded reactions (e.g., amusement, pride, frustration, or disappointment) on
gift-sending behavior thoroughly.
Drawing on emotional arousal theory, we argue that distinct emotions have differing

effects on gift-sending behavior. Emotions are highly diverse and complex and cannot
be reduced to a simple positive-negative distinction (Lerner and Keltner, 2000; Yin,
Bond, and Zhang, 2014). Therefore, exploring the effects of various emotion-embedded
comments on viewers' gift-sending behavior during live streaming becomes essential when

conducting research regarding streamers' revenue forecasts (Lin, Yao, and Chen, 2021).
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Furthermore, although the importance of comment metrics has been recognized,

1 Information Café (2022)
2 Prior studies have often adopted the terms of comment, review, and word of mouth (WOM) to
represent message metrics in academic parlance. Mudambi and Schuff (2010) define online comments
(or reviews) as peer-generated product evaluations posted on company or third party websites.
Hennig-Thurau, Gwinner, Walsh, and Gremler (2004) define online WOM as any positive or negative
statements made by potential, actual, or former customers about a product or company, which is made

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