Background In 2018, Facebook introduced Ad Archive as a platform to improve transparency in advertisements related to politics and “issues of national importance.” Vaccine-related Facebook advertising is publicly available for the first time. After measles outbreaks in the US brought renewed attention to the possible role of Facebook advertising in the spread of vaccine-related misinformation, Facebook announced steps to limit vaccine-related misinformation. This study serves as a baseline of advertising before new policies went into effect. Methods Using the keyword ‘vaccine’, we searched Ad Archive on December 13, 2018 and again on February 22, 2019. We exported data for 505 advertisements. A team of annotators sorted advertisements by content: pro-vaccine, anti-vaccine, not relevant. We also conducted a thematic analysis of major advertising themes. We ran Mann-Whitney U tests to compare ad performance metrics. Results 309 advertisements were included in analysis with 163 (53%) pro-vaccine advertisements and 145 (47%) anti-vaccine advertisements. Despite a similar number of advertisements, the median number of ads per buyer was significantly higher for anti-vaccine ads. First time buyers are less likely to complete disclosure information and risk ad removal. Thematically, anti-vaccine advertising messages are relatively uniform and emphasize vaccine harms (55%). In contrast, pro-vaccine advertisements come from a diverse set of buyers (83 unique) with varied goals including promoting vaccination (49%), vaccine related philanthropy (15%), and vaccine related policy (14%). Conclusions A small set of anti-vaccine advertisement buyers have leveraged Facebook advertisements to reach targeted audiences. By deeming all vaccine-related content an issue of “national importance,” Facebook has further the politicized vaccines. The implementation of a blanket disclosure policy also limits which ads can successfully run on Facebook. Improving transparency and limiting misinformation should not be separate goals. Public health communication efforts should consider the potential impact on Facebook users’ vaccine attitudes and behaviors.

The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.

We present preliminary results on the online war surrounding distrust of expertise in medical science – specifically, the issue of vaccinations. While distrust and misinformation in politics can damage democratic elections, in the medical context it may also endanger lives through missed vaccinations and DIY cancer cures. We find that this online health war has evolved into a highly efficient network insurgency with direct inter-crowd links across countries, continents and cultures. The online anti-vax crowds (referred to as Red) now appear better positioned to groom new recruits (Green) than those supporting established expertise (Blue). We also present preliminary results from a mathematically-grounded, crowd-based analysis of the war’s evolution, which offers an explanation for how Red seems to be turning the tide on Blue.

Content regulation and censorship of social media platforms is increasingly discussed by governments and the platforms themselves. To date, there has been little data-driven analysis of the effects of regulated content deemed inappropriate on online user behavior. We therefore compared Twitter — a popular social media platform that occasionally removes content in violation of its Terms of Service — to Gab — a platform that markets itself as completely unregulated. Launched in mid-2016, Gab is, in practice, dominated by individuals who associate with the “alt-right” political movement in the United States. Despite its billing as “The Free Speech Social Network,” Gab users display more extreme social hierarchy and elitism when compared to Twitter. Although the framing of the site welcomes all people, Gab users’ content is more homogeneous, preferentially sharing material from sites traditionally associated with the extremes of American political discourse, especially the far right. Furthermore, many of these sites are associated with state-sponsored propaganda from foreign governments. Finally, we discovered a significant presence of German language posts on Gab, with several topics focusing on German domestic politics, yet sharing significant amounts of content from U.S. and Russian sources. These results indicate possible emergent linkages between domestic politics in European and American far right political movements. Implications for regulation of social media platforms are discussed.