Am I understanding this correctly? Facebook user data (likes/profile info) was scraped to produce low-dimension feature vectors for users (similar to word2vec). These feature vectors were then run through some ML model to predict...what exactly? Targetability for effective political ads?
They used it to predict political affiliation of people that don't explicitly state a party preference.
The two parties already have a list of registered party members (and they can see who on Facebook explicitly states their party preference), for those members the main goal is higher turnout (they are the training data). The other voters they're interested in are unregistered (e.g. independent) voters that are likely to be on their side ideologically.
The core idea is very simple, they believe that if someone says they're independent, but their preferences/features (age, gender, location, likes, posts) are predicting moderate or high likelihood of $PARTY affiliation, then showing this person political ads may move them from 'maybe vote for $PARTY' category and get them in the 'definitely vote for $PARTY' category.
If you have continuous access to new Facebook data as you're serving ads, you can verify your ads are working on an individual basis by checking the predicted 'score' for $PARTY affiliation predicted by your model before and after an ad (I want to stress that this can be done on an _individual basis_). The likely sequence of events is that they did AB testing on different kinds of ads and found that fake inflammatory ads were most effective at achieving this goal in a very measurable way ($PARTY score), the resulting media/political atmosphere is collateral damage (hopefully unintended).
Source: I am a data scientist / machine learning scientist and this is how I would do it and how it seems to me others. I don't work on political data but I have worked on personalized recommendations which are similar.
Did the app really grant them continuous access to user info? I thought it was a one-off thing - they get your data at the time of use (and your friends') and that's it.
Plus the approach you outlined would require the user to like/dislike things based on the add they saw, so CA can observe a change in the predicted affiliation (they didn't have access to posts as far as I know). I don't think it would have that effect (even if the add influences you, I doubt that it would make you go unlike Obama's page for example). Not to mention that by any likelihood you shouldn't be able to verify that a particular add was shown to a given individual.
I suspect it was a simpler use case - they would group users into segments, and then craft different add strategies for each one (maybe based on other research or just expert opinion).
> I suspect it was a simpler use case - they would group users into segments, and then craft different add strategies for each one (maybe based on other research or just expert opinion).
It is in this last process that it is individual-based. It is in this last process that AB tests are done individually as a function of the specific strategy applied to him/her
It seems like the purpose was narrowly tailoring messages, which is something political campaigns are really keen to do now (Obama's campaign was kind of a trailblazer here, right?).
> Obama's campaign was kind of a trailblazer here, right?
It's a pretty big gap between using and abusing social media and as far as I know Obama's campaign did not 'narrowly tailor messages'. They did target broad groups using generic messages and they did quite effectively use social media presence to build support.
But they did not - as far as I know, so please correct me if I'm wrong - go so far as to single out individuals or really small groups with the express intent of flipping their votes or targeting them with disinformation in order to try to stop them from voting.
And Cambridge Analytica seems to have been doing just that if the currently available information is to be believed.
> The former president also hired Facebook co-founder Chris Hughes to help in developing his social media strategy. Obama furthered the use of Facebook for his 2012 re-election bid, utilizing it to encourage young people to cast their votes. His team developed a Facebook app that looked into supporters’ friends list to find younger voters. The team then asked supporters to share online content with these voters. More than 600,000 supporters responded to the call, sending content to over 5 million contacts.
> During his presidency, Obama continued to use Facebook to reach out to the public. In 2016, he became the first president to go live on the site, just before his final State of the Union Address.
Yes, that pretty much confirms what I wrote above. Your point being?
Please read the article and compare what we know about Cambridge Analytica vs what the Obama campaign did, it is comparing snipers with someone setting off fireworks.
Look, I don't think anyone can realistically doubt that Obama's campaign was the first to effectively slice-and-dice the electorate and use social media to target them. You're arguing against a much more expansive claim than I'm making.
"What sort of lies" is pretty hand-wavy when it comes to labeling training data for a model. Are you summarizing from a source? I'm interested in the technical details of what happened.
Very few "undecided" voters truly are; elections are won and lost by getting your supporters to go to the polls. So if you wanted to use scurrilous, fake news to help your candidate, you'd be better off sending stories that will get your supporters really fired up and eager to vote and get their friends to vote, not trying to persuade the practically nonexistent undecided demographic.
Are you able to specify the sources based on which you are supporting these claims, namely that elections are not not decided by "undecided" voters but rather by pushing your supporters to the election polls?
The last piece has a short summary of the salient point:
> In fact, according to an analysis of voting patterns conducted by Michigan State University political scientist Corwin Smidt, those who identify as independents today are more stable in their support for one or the other party than were “strong partisans” back in the 1970s. According to Dan Hopkins, a professor of government at the University of Pennsylvania, “independents who lean toward the Democrats are less likely to back GOP candidates than are weak Democrats.”
> While most independents vote like partisans, on average they’re slightly more likely to just stay home in November. “Typically independents are less active and less engaged in politics than are strong partisans,” says Smidt.
> [...]
> The conventional wisdom holds that the parties need independents to win general elections, but the reality is that they’re increasingly devoting their resources to getting their own voters—including their “closet partisans”—out to the polls rather than trying to sway the dwindling number of genuine swing voters. “We’ve seen a huge increase in technology and the ability to turn out the vote,” says Smidt. “So in terms of a cost-benefit analysis, the parties and candidates see that it’s much easier to turn out people who agree with them than it is to change someone’s mind. And then there’s also the question of how many of us are even open to changing our minds.”
