Analysis of the relationship between interactions by live streamers and viewers and pay-what-you-want donation behavior using LLM

Abstract: Since the COVID-19 pandemic, people's activities in the virtual world have continued to expand year by year. At the same time, the number of virtual world service recipients who donate money to service providers as a form of support has also continued to grow. For example, the behavior of viewers donating money to live streamers on social live streaming services is expanding year by year on global services such as Twitch and YouTube in the form of social tipping, redeemable digital gifts, and subscription gifting (e.g., TwitchTracker.com, 2025).Since there is no upper limit on the amount of money that service recipients can give to service providers as support, and they can do so repeatedly, this can be considered a Pay-What-You-Want (PWYW) donation. PWYW donation is done through the chat window on social live streaming services. Therefore, PWYW donation is made as a post in the chat window while voice conversations, chats, and emoji interactions are taking place between the live streamer and viewers.With regard to prior research focusing on voice conversations, chats, and emoji interactions between live streamers and viewers, no analysis related to viewers' behavior of “donating money” to live streamers has been found to the best of my knowledge. For example, Reckenwald analyzed conversations and chats between live streamers and viewers to determine what kind of interactions were taking place and what conversational skills were necessary for live streamers, but did not analyze them in relation to the behavior of “donating money” (Reckenwald, 2018).Therefore, by analyzing data from voice conversations, chats, and emoji interactions between viewers and live streamers regarding PWYW donation behavior, and clarifying how these interactions lead to the behavior of “giving money,” it is considered that we can gain many implications, e.g., for marketing plans or new service designs that contribute to promoting PWYW donation behavior among viewers and enhancing the value of services provided by live streamers.In this study, the author proposes a method of analysis using LLM (Large Language Model) to analyze complex interaction data between live streamers and viewers through multiple means, such as voice conversations, chats, and emojis. In recent years, LLM has become capable of summarizing multiple sources of video, audio, and text. This study uses LLM to summarize the voice conversations, chats, and emoji interactions between the two parties, which have not been done before, and attempts to clarify how these interactions lead to PWYW donation behavior, i.e., the behavior of donating money.This study uses Twitch as a case study and focuses on social tipping and subscription gifting as PWYW donation. Using LLM, the study summarizes actual voice conversations, chats, and emoji interactions between live streamers and viewers, analyzes them, and clarifies how these interactions lead to PWYW donation behavior.ReferencesTwitchTraker.com. 2025. “TWITCH SUBS COUNT & STATS”, TwitchTraker.com.URL: https://twitchtracker.com/subscribersReckenwald, Daniel. 2018. “The Discourse of Online Live Streaming on Twitch: Communication between Conversation and Commentary”, Doctoral dissertation, Hong Kong Polytechnic University, Hong Kong Special Administrative Region.

Keywords: LLM, PWYW, donation, PWYW donation, social live streaming service, Twitch

DOI: 10.54941/ahfe1006869

Cite this paper:

​