Fake Download
Melva Simons
Attorney General James also, today, announced agreements with three of the lead generators that were responsible for millions of the fake comments submitted in the net neutrality proceeding: Fluent, Inc., responsible for approximately 4.8 million fraudulent comments; Opt-Intelligence, Inc., responsible for more than 250,000 fraudulent comments; and React2Media, Inc., responsible for approximately 329,000 comments in the net neutrality proceeding (all or nearly all of which were fraudulent). Fluent and React2Media were also responsible, collectively, for millions of fake comments and messages submitted in dozens of other advocacy campaigns. The agreements with the OAG require the companies to adopt comprehensive reforms in future advocacy campaigns and pay more than $4.4 million in penalties and disgorgement.
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Moving forward, we must expand the study of social and cognitive interventions that minimize the effects of misinformation on individuals and communities, as well as of how socio-technical systems such as Google, YouTube, Facebook, and Twitter currently facilitate the spread of misinformation and what internal policies might reduce those effects. More broadly, we must investigate what the necessary ingredients are for information systems that encourage a culture of truth.
This report is organized as follows. Section 1 describes the state of misinformation in the current media ecosystem. Section 2 reviews research about the psychology of fake news and its spread in social systems as covered during the conference. Section 3 synthesizes the responses and discussions held during the conference into three courses of action that the academic community could take in the immediate future. Last, Section 4 describes areas of research that will improve our ability to tackle misinformation in the future. The conference schedule appears in an appendix.
Once the news faker obtains access to the press wires all the honest editors alive will not be able to repair the mischief he can do. An editor receiving a news item over the wire has no opportunity to test its authenticity as he would in the case of a local report. The offices of the members of The Associated Press in this country are connected with one another, and its centers of news gathering and distribution by a system of telegraph wires that in a single circuit would extend five times around the globe. This constitutes a very sensitive organism. Put your finger on it in New York, and it vibrates in San Francisco.
A growing body of research provides evidence that fake news was prevalent in the political discourse leading up to the 2016 U.S. election. Initial reports suggest that some of the most widely shared stories on social media were fake (Silverman, 2016), and other findings show that the total volume of news shared by Americans from incredible and dubious sources is comparable in volume to news coming from individual mainstream sources such as The New York Times (Lazer, n.d.; although a limitation is that this research focused on dissemination and not consumption of information).
A parallel, perhaps even larger, concern regarding the role of social media, particularly Facebook, is their broad reach beyond partisan ideologues to the far larger segment of the public that is less politically attentive and engaged, and hence less well-equipped to resist messages that conflict with their partisan predispositions (Zaller 1992), and more susceptible to persuasion from ideologically slanted news (Benedictis-Kessner, Baum, Berinsky, and Yamamoto 2017). This raises the possibility that the largest effects may emerge not among strong partisans, but among Independents and less-politically-motivated Americans.
Most of us do not witness news events first hand, nor do we have direct exposure to the workings of politics. Instead, we rely on accounts of others; much of what we claim to know is actually distributed knowledge that has been acquired, stored, and transmitted by others. Likewise, much of our decision-making stems not from individual rationality but from shared group-level narratives (Sloman & Fernbach, 2017). As a result, our receptivity to information and misinformation depends less than we might expect on rational evaluation and more on the heuristics and social processes we describe below.
First, source credibility profoundly affects the social interpretation of information (Swire et al., 2017; Metzger et al., 2010; Berinsky, 2017, Baum and Groeling 2009; Greenhill and Oppenheim, n.d.). Individuals trust information coming from well-known or familiar sources and from sources that align with their worldview. Second, humans are biased information-seekers: we prefer to receive information that confirms our existing views. These properties combine to make people asymmetric updaters about political issues (Sunstein et al., 2016). Individuals tend to accept new information uncritically when a source is perceived as credible or the information confirms prior views. And when the information is unfamiliar or comes from an opposition source, it may be ignored.
Fake news spreads from sources to consumers through a complex ecosystem of websites, social media, and bots. Features that make social media engaging, including the ease of sharing and rewiring social connections, facilitate their manipulation by highly active and partisan individuals (and bots) that become powerful sources of misinformation (Menczer, 2016).
The polarized and segregated structure observed in social media (Conover et al, 2011) is inevitable given two basic mechanisms of online sharing: social influence and unfriending (Sasahara et al., in preparation). The resulting echo chambers are highly homogeneous (Conover et al, 2011b), creating ideal conditions for selective exposure and confirmation bias. They are also extremely dense and clustered (Conover et al., 2012), so that messages can spread very efficiently and each user is exposed to the same message from many sources. Hoaxes have higher chances to go viral in these segregated communities (Tambuscio et al., in preparation).
Even if individuals prefer to share high-quality information, limited individual attention and information overload prevent social networks from discriminating between messages on the basis of quality at the system level, allowing low-quality information to spread as virally as high-quality information (Qiu et al., 2017). This helps explain higher exposure to fake news online.
It is possible to leverage structural, temporal, content, and user features to detect social bots (Varol et al., 2017). This reveals that social bots can become quite influential (Ferrara et al., 2016). Bots are designed to amplify the reach of fake news (Shao et al., 2016) and exploit the vulnerabilities that stem from our cognitive and social biases. For example, they create the appearance of popular grassroots campaigns to manipulate attention, and target influential users to induce them to reshare misinformation (Ratkiewicz et al., 2011).
Most people who share fake news, whether it gains popularity or not, share lots of news in general. Volume of political activity is by far the strongest predictor of whether an individual will share a fake news story. The fact that misinformation is mixed with other content and that many stories get little attention from people means that traditional measures of quality cannot distinguish misinformation from truth (Metzger et al., 2010). Beyond this, certain characteristics of people are associated with greater likelihood of sharing fake news: older and more extreme individuals on the political spectrum appear to share fake news more than others (Lazer et al., n.d.).
Nation-states and politically-motivated organizations have long been the initial brokers of misinformation. Both contemporary and historical evidence suggests that the spread of impactful misinformation is rarely due to simple misunderstandings. Rather, misinformation is often the result of orchestrated and strategic campaigns that serve a particular political or military goal (Greenhill, forthcoming). For instance, the British waged an effective campaign of fake news around alleged German atrocities during WWI in order to mobilize domestic and global public opinion against Germany. These efforts, however, boomeranged during WWII, because memories of that fake news led to public skepticism, during WWII, of reports of mass murder (Schudson, 1997).
Finally, while any group can come to believe false information, misinformation is currently predominantly a pathology of the right, and extreme voices from the right have been continuously attacking the mainstream media (Benkler et al., 2017). As a result, some conservative voters are even suspicious of fact-checking sites (Allcott and Gentzkow, 2017). This leaves them particularly susceptible to misinformation, which is being produced and repeated, in fact, by those same extreme voices. That said, there is at least anecdotal evidence that when Republicans are in power, the left becomes increasingly susceptible to promoting and accepting fake news. A case in point is a conspiracy theory, spread principally by the left during the Bush Administration, that the government was responsible for 9/11. This suggests that we may expect to witness a rise in left-wing-promulgated fake news over the next several years. Regardless, any solutions for combating fake news must take such asymmetry into account; different parts of the political spectrum are affected in different ways and will need to assume different roles to counter it.
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