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Scientists Track Reddit Crypto Threads to Understand Disinformation

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Researchers at the Pacific Northwest National Laboratory (PNNL) have published a fresh study that looks at the frequency of Reddit crypto discussions to try and see if online chatter can help predict market behavior as well as to understand the spread of disinformation online.

PNNL data scientist Svitlana Volkova said that crypto was a “very good proxy program for disinformation” in her presentation at the recent San Francisco Web Conference 2019. She claims that the ebb and flow of mentions on Reddit about crypto have close correlations with scandals associated with disinformation campaigns, such as the 2016 US presidential campaign’s Russian disinformation scandal. Experts now believe the same evidence is emerging for similar campaigns for the 2020 presidential election.

But becauseperpetrators of disinformation are camouflaged in the crowd, it can be difficult to track them, especially on broader topics like presidential politics. However, PNNL researchers think they’ve cracked the code with Reddit threads focused on cryptos like Bitcoin, Ethereum and Monero.

Volkova and her co-authors Emily Saldanha and Maria Glenski analyzed “tens of thousands of Reddit comments made on the forums” for these three digital assets in a four-year period from 2015 to 2018. They measured how quickly threads became viral, how much volume they generated, how many Redditors participated and the extent of their engagement.

They found that Bitcoin generated the most activity, creating 3,600 daily comments, compared with only 500 for Ethereum and 380 for Monero. Bitcoin also generated responses twice as quickly as the other two. The lifetime of discussions, however, was the longest on Ethereum, although Monero had the longest median ones and were five times likelier to attract follow-up responses than the other two.

Data suggested that Ethereum discussions lasted longer because they were about blockchain development as well as crypto, while Monero was very attractive due to it being preferred over Bitcoin because of privacy features. Volkova explained:

“These social signals are quite useful, and by incorporating them with machine and deep learning, we intend to build predictive models that hit on causal relationships between different variables so we can explain model decision-making processes.”

After extracting social signals, they were able to link their metrics to “predict it [Bitcoin prices] with very decent accuracy”, claims Volkova, who says the models will now be incorporated into studies on disinformation spread and the types of social media actors most likely to be responsible.

It is part of a cyber initiative funded by the Pentagon’s Defense Advanced Research Projects Agency (DARPA) to prevent disinformation:

“Mitigation is harder. We have the models right now that can detect disinformation and can say, ‘OK, look, this is not true, this is false or misleading.’ We can make the audience aware of the way disinformation spreads. But then we have to somehow coordinate with the social platform providers to actually make a difference.”

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