Pods of Science

How Social Media Spreads Information Online

Episode Summary

Data scientists at Pacific Northwest National Laboratory examine how cryptocurrency discussions spread online via social media. Humans are social animals, and nowhere is that more apparent in today’s modern world than on social media. As part of an effort to understand communication patterns and build a quantitative framework for how information spreads online, researchers at Pacific Northwest National Laboratory, a United States Department of Energy national laboratory, recently examined cryptocurrency discussion threads on Reddit. Their findings, recently presented at the Web Conference 2019, not only shed light on how cryptocurrency discussions spread but could inform artificial intelligence applications for modeling information spread across online social environments and even forecasting cryptocurrency price.

Episode Notes

Jess Wisse: What's behind the science and inventions that impact our daily lives? Pacific Northwest National Laboratories Pods of Science are the stories of what happens before the breakthrough. Before a technology becomes a house-hold name, before the life-saving drug his pharmacy shelves, before the paper's published.

See what happens when great minds meet great challenges.

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Welcome, I'm your host Jess Wisse. On today's episode we'll be unveiling new research coming out of PNNL’s Data Sciences and Analytics Group, but before we unpack that here's a bit more information from our co-host Jessica Bernsen.

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Jessica Bernsen: Humans are social animals. Nowhere is that more apparent in today's modern world than on social media. Log in to your favorite social media platforms and you'll find a slew of conversations, debates, news, and more. PNNL researchers took all of this data and built a quantitative framework to better understand communication patterns and how information spreads online.

Meet Svitlana.

Svitlana Volkova: My name is Svitlana Volkova and I am a senior scientist at PNNL. I've been here for three years, and my work involves machine learning, deep learning, natural language processing, and computational social science. I work a lot with social data.

Jess Wisse: Findings gathered by Svitlana and her colleagues Maria Glensky and Emily Saldana shed led light on to how cryptocurrency discussions spread and they also could inform artificial intelligence applications used to forecast things like cryptocurrency prices.

Svitlana Volkova: So in this paper we analyzed almost three years worth of data; a lot of discussions, millions of posts, and comments and that’s what makes this research interesting that like we have access to this vast amount of data that we have the techniques and methodology to analyze really fast and draw some insights and scientific conclusions from this data that can in turn inform machine learning and deep learning models to predict the future.

Jessica Bernsen: Nobody really looked into how information about cryptocurrency spreads on reddit specifically and that's what Svitlana and her team did.

Svitlana Volkova: The current data set included the historical rise of the Bitcoin price and we specifically
wanted to look into social signals around this historical event when the price is increasing and then decreasing we wanted to see how social environments are reflecting this change.

We found that across of three coins, the discussion spread is very different.

We know that Bitcoin is the most popular coin, and that was reflected in our analysis.

We found that comments on a Bitcoin post about was the fastest—on average people responded in 11 minutes to discussions about Bitcoin versus Monero and Ethereum.

In Ethereum threads, it takes people at least 30 minutes to follow up on a on a post, but interestingly we found that Monero has really long, ongoing conversations compared to Bitcoin conversations that have a very short life time.
They don’t live long.
And Bitcoin conversation focus on a specific audience, which on average is between 2 and 6 people. Monero conversations involve more people, and more diverse audiences. And structurally the discussions are very different.

The Monero discussions are like chains. They go deep. And at each level they have a specific size of the audience. Bitcoin discussions are more diverse, and they form trees, and they go more viral compared to Monero.

Jess Wisse: Svitlana and her team looked in to reddit, but their research can also be applied to a variety of platforms. For example, the framework they designed for measuring information spread can be also be extended to measure the spread of other types of information. Such as images on Instagram, videos on YouTube, hashtags on Twitter.

Svitlana Volkova: This analysis would be very helpful for a different predictive analytics so for example you can look how discussions spread around different cryptocurrencies and social platforms and more specifically across social platforms that involved that goes beyond Reddit and actually try to predict cryptocurrency prices.

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Jessica Bernsen: We share a lot of information on social media everyday this information can be used in a variety of ways not only to predict cryptocurrency prices.

Svitlana Volkova: So this work is specifically focusing on one type of information - cryptocurrencies in one social environments, but you can think about more general applications and implications of this work. So for
example you would like to know how this information and false narrative spread or one might want to measure how different discussions about software vulnerabilities spread. Other people might be interested in how negative language spreads across social environments so all of this is applicable. The evaluation framework and the measurements that we use that are targeting specific social phenomena. Ideally we would like to take any piece of information whether it's text or image or video and see how it spreads
and measure how many people it reaches how my like what is the size of the audience how fast it spreads and what is the actual impact on it the society so this is the main goal of the whole project.

Jess Wisse: So how did Svitlana even get into this line of work and why does it all matter?

Svitlana Volkova: It's very interesting to understand people. I love measuring how people act and interact and respond in social environments that's what we do daily right we go to social media and we respond to posts we comment and we spread the information we are the people who are spreading the information and we actually influence the rate of spread and the impact right? So I think it's very fascinating to see what people can do and measure it actually not like talk about it and like and have a rate we can actually measure it quantitatively and maybe we can change something if we want to change something

Jessica Bernsen: In a nutshell

Svitlana Volkova: We build machine learning models that can predict the future.

Jessica Bernsen: And that’s powerful.

Jess Wisse: Thanks for listening to our first episode of Pods of Science. Want to learn more? Follow us on social media at PNNLab. We're on Twitter, Instagram, Facebook, and LinkedIn. You can also visit our website at pnnl.gov. Thanks for listening.

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