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The State of Disinformation on Social Media

CDS researchers contribute to new review of disinformation on online platforms

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CDS affiliated faculty members and researchers from NYU’s SMaPP lab have recently produced a report reviewing current research on how fake news, rumors, deliberately incorrect information—all of which is encompassed by the term “disinformation”—spreads on social media, and how this online phenomenon affects political life.

How exactly does disinformation spread on social media? How effective is it? Can we stop it?

It’s common knowledge that disinformation proliferated on social media during the 2016 U.S. election, but researchers are only recently beginning to understand the full scope of the impact and the conditions that allowed such disinformation to flow. Sanovich and Stukal’s review includes a report that 400,000 bots posted 3.8 million tweets during the final month of 2016 election. And, during the final three months, users engaged more with the top twenty fake news stories than they did with authenticated ones.

But U.S. elections are only one of many worldwide events affected: Russian bots also affected German, British, Catalonian, French, and Russian domestic elections. Another report showed that 7/10 stories about Angela Merkel were false ahead of the 2017 parliamentary elections.

Humans are clearly having a hard time identifying bots—so could automated systems do it?

The answer is yes—sort of.

Bot detection algorithms are typically trained to correctly classify bots within a certain domain; it can be challenging for these systems to identify bots outside their domains. Furthermore, according to a 2017 study, these algorithms degrade in accuracy over time by up to 20% in one year as bots evolve.

Sanovich and Stukal identify dependence on ad revenue and optimization algorithms as the two key characteristics that make social media inherently susceptible to disinformation campaigns. The U.S. government does not regulate who can and cannot advertise on social media. Consequently, one report says Twitter offered the Russian state-supported media network RT $3 million for 15% of its U.S. elections advertising; another report says Facebook avoided screening advertisers and allowed ads to be paid for in Russian rubles. Engagement optimization algorithms further exacerbate the problem by incentivizing sensational images and headlines.

So what can be done? Sanovich and Stukal consider methods for sites to verify stories through automated or manual methods, but they fear that verification could suffer from errors, bias, and have unintended consequences including censorship. The problem of disinformation on social media platforms remains very much an open one — a fundamental concern of contemporary society whose answer may lie in data science.

By Paul Oliver

Center for Data Science

This is the official research blog of the NYU Center for…

NYU Center for Data Science

Written by

Official account of the Center for Data Science at NYU, home of the Master’s and Ph.D. in Data Science.

Center for Data Science

This is the official research blog of the NYU Center for Data Science (CDS). Established in 2013, we are a leading data science training and research facility, offering a MS in Data Science and, as of 2017, one of the nation’s first universities to offer a Ph.D. in Data Science.

NYU Center for Data Science

Written by

Official account of the Center for Data Science at NYU, home of the Master’s and Ph.D. in Data Science.

Center for Data Science

This is the official research blog of the NYU Center for Data Science (CDS). Established in 2013, we are a leading data science training and research facility, offering a MS in Data Science and, as of 2017, one of the nation’s first universities to offer a Ph.D. in Data Science.

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