![]() ![]() However, the Google dataset is inherently limited, as it does not allow an in-depth analysis of the words or sentiment underlying a particular search. There has previously been excellent research in analysing the link between Google search volumes and bitcoin metrics, particularly transaction volume and price. ![]() This approach could be used more generally to look at social media and discussion forums at a granular level identifying specific words that impact the metric under investigation rather than overall sentiment. ![]() For example, the price dynamic word ‘ban’, which became significantly higher in frequency as prices fell, occurred in the context of both government regulation and internet companies banning cryptocurrency adverts. These price dynamic words are used to pull out associated words in the submissions thereby providing the context to their use. We assess the significance of these changes using Wilcoxon Rank-Sum Tests with Bonferroni corrections. Rather than associating sentiment with market activity, we describe the DDPWI method for finding specific ‘price dynamic’ words associated with changes in the bitcoin pricing pattern through 20. Reddit provides complete access to the text of submissions. With Google search volumes as a baseline, we find that Reddit submissions are both correlated with Google and have a comparable relationship with a variety of bitcoin metrics, using Spearman’s rho. ![]() We develop a new Data-Driven Phasic Word Identification (DDPWI) methodology to determine which words matter as the bitcoin pricing dynamic changes from one phase to another. ![]()
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