Exploring the role of social bots in cryptocurrency manipulation: Machine learning insights from the LUNA crash

Yanzhen Yu, Rui Zhou, Rongchuan Jiang and Feng Liu ()
Additional contact information
Yanzhen Yu: Shandong University, Business School
Rui Zhou: Shandong University, Business School
Rongchuan Jiang: Shandong University, Business School
Feng Liu: Shandong University, Business School

Electronic Markets, 2025, vol. 35, issue 1, No 110, 17 pages

Abstract: Abstract The use of social bots by cryptocurrency manipulators to affect the market price has recently emerged as a potential concern in the cryptocurrency market. This study investigates the contribution of social bots to sentiment diffusion on social media and explores the mechanisms underlying cryptocurrency manipulation. We applied 15 machine learning algorithms to examine LUNA price dynamics using 1032 price points and sentiments extracted from 33,281 tweets collected on Twitter (now X) between April 15 and May 27, 2022. The sentiment features consist of two sentiment polarities (positive and negative) and eight basic emotion categories (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust). The results reveal that social bots were highly active during this period, and the sentiments they expressed were more predictive of LUNA prices than those of human accounts, highlighting their important role in the LUNA crash. This study also examines the affective mechanisms through which social bots assist manipulators in triggering herd behavior among investors and investigates the relationship between bot-generated sentiment and price dynamics. The interaction analysis reveals that mixed emotions serve as informative predictors of LUNA’s upward and downward price dynamics. Overall, this study highlights the role of social bots in driving sentiment and price fluctuations, illustrating their impact on cryptocurrency market stability and cyberspace governance.

Keywords: Social bot; LUNA crash; Sentiment analysis; Herding behavior; Cryptocurrency market; Machine learning; G18; M15; O33 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12525-025-00849-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00849-w

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/12525

DOI: 10.1007/s12525-025-00849-w

Access Statistics for this article

Electronic Markets is currently edited by Rainer Alt and Hans-Dieter Zimmermann

More articles in Electronic Markets from Springer, IIM University of St. Gallen
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().