Dynamic Topic Modelling for Cryptocurrency Community Forums

Linton, Marco; Teo, Ernie G. S.; Bommes, Elisabeth; Chen, Cathy Yi‐Hsuan; Härdle, Wolfgang Karl

doi:10.1007/978-3-662-54486-0_18

Cited by 48 publications

(30 citation statements)

References 10 publications

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“…Topic modelling techniques have recently been applied to Bitcoin-related discussion sourced from a forum dedicated to Bitcoin (bitcointalk.org) [10,11]. In the case of [10], the application of dynamic topic modelling (explained later in Section III (A)) allowed the evolution of topics, and terms within those topics, to be tracked over time. Results showed how discussion relevant to certain related technologies has changed over time.…”

Section: B Topic Modellingmentioning

confidence: 99%

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Phillips

Gorse

2018

Proceedings of the 4th International Conference on Frontiers of Educational Technologies

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Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly.There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.

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Section: B Topic Modellingmentioning

confidence: 99%

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Phillips

Gorse

2018

Proceedings of the 4th International Conference on Frontiers of Educational Technologies

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“…They find that opinion polarization has a significant effect on price, and use this to build a model that predicts the price of the cryptocurrency. In an effort to understand the dynamics of cryptocurrency discussions, [13] performed topic modeling on a popular cryptocurrency discussion forum. They identified several common threads of discussion, such as bitcoin theft.…”

Section: Related Workmentioning

confidence: 99%

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Mirtaheri

Abu-El-Haija²,

Steeg³

et al. 2019

Preprint

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Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud-such as "pump and dump" scams-where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the scammers can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant significant presence of bots during the pump attempts. KEYWORDScryptocurrency, pump and dump, social media data mining, anomaly detection

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“…A survey of methods and models for textual sentiment in finance is presented in [63]. Dynamic topic modeling for cryptocurrency community forums is introduced in [76]. A crowdsourced perspective is presented in [80], listing future directions in international financial integration research.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

Zamuda

Crescimanna

Burguillo

et al. 2019

Lecture Notes in Computer Science

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This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking

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Dynamic Topic Modelling for Cryptocurrency Community Forums

Cited by 48 publications

References 10 publications

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

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