Research in this area is notably terrible, possibly because our tools of causality on social graphs are weak and it is hard, or perhaps, because the tools that some of us have are really good but people with really good tools to control the public are not going to mention that.
… every product, brand, politician, charity, and social movement is trying to manipulate your emotions on some level, and they’re running A/B tests to find out how. They all want you to use more, spend more, vote for them, donate money, or sign a petition by making you happy, insecure, optimistic, sad, or angry. There are many tools for discovering how best to manipulate these emotions, including analytics, focus groups, and A/B tests.
- parasocial interaction, the way our monkey minds regard remote celebrities as our intimates.
How it could be done
How do you observational inference of these systems? Of course, standard survey modelling. There is some structure to exploit here, e.g. causalimpact? How about when the data is a mixture of time-series data and one-off results (e.g. polling before and election and the election itself)
Automatic trolling, infinite fake news
GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets.
It takes 5 minutes to download this package and start generating decent fake news; Whether you gain anything over the traditional manual method is an open question.
Assembling these into a twitter bot farm is left as an exercise for the student.
How it’s being done
The methodology here looks, at a glance, shallow but not implausible.
Craig Silverman, Jane Lytvynenko, William Kung, Disinformation For Hire: How A New Breed Of PR Firms Is Selling Lies Online
One firm promised to “use every tool and take every advantage available in order to change reality according to our client’s wishes.”
- How Facebook Agreed to Suspend Netanyahu’s Chatbot
- Aleksandr Kogan describes How Cambridge Analytica worked
- Kate Starbird, the surprising nuance behind the Russian troll strategy
- facebook political ad collector
Dan O’Sullivan, Inside the RNC Leak
In what is the largest known data exposure of its kind, UpGuard’s Cyber Risk Team can now confirm that a misconfigured database containing the sensitive personal details of over 198 million American voters was left exposed to the internet by a firm working on behalf of the Republican National Committee (RNC) in their efforts to elect Donald Trump. The data, which was stored in a publicly accessible cloud server owned by Republican data firm Deep Root Analytics, included 1.1 terabytes of entirely unsecured personal information compiled by DRA and at least two other Republican contractors, TargetPoint Consulting, Inc. and Data Trust. In total, the personal information of potentially near all of America’s 200 million registered voters was exposed, including names, dates of birth, home addresses, phone numbers, and voter registration details, as well as data described as “modeled” voter ethnicities and religions. …
“‘Microtargeting is trying to unravel your political DNA,’ [Gage] said. ‘The more information I have about you, the better.’ The more information [Gage] has, the better he can group people into “target clusters” with names such as ‘Flag and Family Republicans’ or ‘Tax and Terrorism Moderates.’ Once a person is defined, finding the right message from the campaign becomes fairly simple.”
Businessweek, which published a major look into the campaign this morning, explains how the Trump team has quietly organized a data enterprise to sharpen its White House bid. According to the magazine, the campaign is meanwhile attempting to depress votes in demographics where Hillary Clinton is winning by wide margins.
Parscale was given a small budget to expand Trump’s base and decided to spend it all on Facebook. He developed rudimentary models, matching voters to their Facebook profiles and relying on that network’s “Lookalike Audiences” to expand his pool of targets. He ultimately placed $2 million in ads across several states, all from his laptop at home, then used the social network’s built-in “brand-lift” survey tool to gauge the effectiveness of his videos, which featured infographic-style explainers about his policy proposals or Trump speaking to the camera. “I always wonder why people in politics act like this stuff is so mystical,” Parscale says. “It’s the same shit we use in commercial, just has fancier names.”
Jonathan Stray, What tools do we have to combat disinformation?
Sarah Thompson, Fake Faces: People Who Do Not Exist Invade Facebook To Influence 2020 Elections is an interesting bit of meta analysis on Lead Stories
Lead Stories uses the Trendolizer™ engine to detect the most trending stories from known fake news, satire and prank websites and tries to debunk them as fast as possible.
(I think this is publicity/loss leader for Trendolizer, a media buzz product.)
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