Lisa Friedland

There was a proliferation of fake news during the 2016 election cycle. Grinberg et al. analyzed Twitter data by matching Twitter accounts to specific voters to determine who was exposed to fake news, who spread fake news, and how fake news interacted with factual news. Fake news accounted for nearly 6% of all news consumption, but it was heavily concentrated—only 1% of users were exposed to 80% of fake news, and 0.1% of users were responsible for sharing 80% of fake news.

Recent shifts in the media ecosystem raise new concerns about the vulnerability of democratic societies to fake news and the public’s limited ability to contain it. The relatively small, but constantly changing, number of sources that produce misinformation on social media offers both a challenge for real-time detection algorithms and a promise for more targeted socio-technical interventions.

There are some possible pathways for reducing fake news, including: (1) offering feedback to users that particular news may be fake (which seems to depress overall sharing from those individuals); (2) providing ideologically compatible sources that confirm that particular news is fake; (3) detecting information that is being promoted by bots and “cyborg” accounts and tuning algorithms to not respond to those manipulations; and (4) because a few sources may be the origin of most fake news, identifying those sources and reducing promotion (by the platforms) of information from those sources.

As a research community, we identified three courses of action that can be taken in the immediate future: involving more conservatives in the discussion of misinformation in politics, collaborating more closely with journalists in order to make the truth “louder,” and developing multidisciplinary community-wide shared resources for conducting academic research on the presence and dissemination of misinformation on social media platforms.