Evidence map›Paper›PMID 31824713›Full record

ArticleRoyal Society open science2019

Social media and bitcoin metrics: which words matter.

Andrew Burnie, Emine Yilmaz

Abstract read
In one paragraph

Article in Royal Society open science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Andrew BurnieThe Alan Turing Institute, London, UK.ORCID 0000-0002-8700-3786
Emine YilmazThe Alan Turing Institute, London, UK.

Funding

NIEHS NIH HHS 27302C0028
6 · The paper itself

Abstract

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. 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. Reddit provides complete access to the text of submissions. 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 2017 and 2018. We assess the significance of these changes using Wilcoxon Rank-Sum Tests with Bonferroni corrections. These price dynamic words are used to pull out associated words in the submissions thereby providing the context to their use. 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. 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.

Indexed as

bitcoinnon-parametric statisticsRedditsentimentsocial mediatext analysis

Identifiers

PMID31824713
PMCPMC6837202

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.