Corporate Spending on 2019 Municipal Elections: Mixed Results in Seattle

The story that will emerge from the 2019 Seattle, Washington elections should be of interest to anyone working in campaigns, from data appending and other management services to more ephemeral strategists. The big picture part of the story is this: Some big corporations were fixated on the outcome of very local elections, for very localized reasons, and so those corporations spent a lot of money to influence those elections—and it turns out that was money wasted. 

Geekwire is one of many news sources that noted the sudden and “unprecedented interest in Seattle’s upcoming election” by entities like Amazon, “spending big and turning the national spotlight on local politics.” Amazon threw in a million dollars to city council candidates who would not press the taxation issues that Seattle’s more leftward leaders had done. Anti-tax and free-market advocates, tech executives at Microsoft, even “rank and file” workers at Seattle companies, were donating to these champions of entrepreneurialism. Another million was poured into ballot initiative campaigns, “and tech trade groups are becoming more vocal in local politics.” The last part—actually participating in the political process—was probably more productive than the donations.

Because in the end, the races were too close to call, or the pro-business candidates lost outright. “Several business-backed candidates trailed their more progressive competitors in the first reported votes, suggesting the new City Council will not be a dramatic departure from the previous one.” One wonders how much each vote ended up costing the corporate interests, and all this at the risk of Seattle becoming “a national political spectacle.” Even the wins are too expensive to be rationalized as simply a good investment, especially given that such tax policies may be inevitable in the policy exigencies of climate crises over the next several years

One race that remained too close to call for several days was the effort by Egan Orion in District 3 to unseat Kshama Sawant. But regardless of the outcome (which had still not been called at the time this post was written), the real lesson of that race was how little the candidates themselves actually relate to the corporations that are trying to elect them. This race was a top priority for CASE, which “spent $443,000 in support of Sawant’s opponent, Orion, more than any other candidate, according to Washington’s Public Disclosure Commission records.” This money from Amazon, CASE, and other sources actually embarrassed Egan Orion, and he called it  “completely unnecessary” and said he wanted to win without  “the shadow of Amazon hanging over me.”

It’s easy to say this was all for the avoidance of a $10 million per year tax Amazon would have to pay, but of course it’s also about the tech sector’s interest in a city that tends away from, rather than further toward, regulations and taxation—so in that sense the race was symbolic more than immediately economic. And in that sense, the race was a failure, because in order to get a symbolic rebuke, you need decisive wins, not just little squeakers.

Why We Sometimes Distrust Technology

Residents of cities like Detroit are getting fed up with police surveillance technology, and don’t care whether it decreases crime. Several digital rights advocacy groups are also weighing in, calling for bans on government use of facial recognition technology. There is a rejection of consequentialist or utilitarian arguments going on here—that is to say, if you argue “but crime is decreasing because of surveillance technology,” whether that claim is wrong, those opposed to its use often do so on a deep moral level. 

But there are also powerful “policy objective” arguments against such technology, two of which have been cited by advocates of the California bill to ban police use of the tech: The first concern is that “facial recognition isn’t reliable enough to use without flagging high rates of innocent people for questioning or arrest.” The second is that “adding facial recognition to body cameras creates a surveillance tool out of a technology that was supposed to create more accountability and trust in police departments.”

Both of these arguments seem inarguably true to me. You can improve technology but it will always produce some false positives, and terrible things could happen to people’s lives as a result. And this widespread spying on people, far beyond even targeted surveillance in particular investigations, does not build trust between police and communities.

But I also think we should be careful to know that we can get what we ask for, in this case, a “ban” on the use of this technology, and what we are likely to get even if California and other states pass such laws. We’ll have to check (and in the case of the police this will mean community review) police procedures to ensure there will be no surreptitious, illegal use of the tech, or whether police will procure results of the tech from other entities. 

That seems obvious, but I’m not sure everyone gets it. “Imagine if we could go back in time and prevent governments around the world from ever building nuclear or biological weapons. That’s the moment in history we’re in right now with facial recognition,” said Evan Greer, deputy director of Fight for the Future, in a statement. But certainly, a ban biological and nuclear weapons can’t prevent their production altogether. Likewise, you can (and probably should) regulate, restrict, monitor, and ban police procedures and use of technology, but people, entities, will still develop surveillance technology. Governments and bad-acting private entities will use it if they can get away with it, and “getting away with it” takes interesting forms in the world of high corruption. 

Of course, that’s not an argument against banning police use of the tech, but instead an argument for doing more, for at least also improving the conversation about technology and trust. 

That conversation goes both ways in that it will sometimes affirm new tech even though it’s imperfect, and reject another tech for perhaps doing its job too well. As saving lives go, autonomous vehicles are probably more helpful than surveillance technology. They will certainly save millions of lives worldwide, although we can debate how many. Nevertheless, the media, and not just the media, focus on the crashes that may occur. It’s easy, and correct, to respond as philosophy professor and essayist Ryan Muldoon does in The Conversation: “autonomous cars will have been a wild technology success even if they are in millions of crashes every year, so long as they improve on the 6.5 million crashes and 1.9 million people who were seriously injured in a car crash in 2017.” 

But that’s not always how people see it; there’s an intersubjective element to risk assessment, and understanding how people’s minds work is part of understanding how to apply data. That’s why 71 percent of Americans still don’t “trust” autonomous vehicles even in 2019. Learning more about risk is important, but taking democratic, deliberative control of risk management—including against an overly enthusiastic surveillance state—would be even better.

How Influencers Use Twitter Replies to Build An Audience

Too often it seems that national news headlines are backed up by nothing other than a handful of celebrity tweets. But in a new Atlantic article, “The Resistance Media Weren’t Ready for This,” it’s appropriate. Staff writer McKay Coppins details how the large Facebook pages and Twitter celebrities who built their following on building up the Mueller investigation are adapting to its conclusion. It’s worth a quick read, but what is even more interesting for marketers and would-be pundits is the method with which these “Resistance Media” influencers were able to create a cottage industry out of their political passions.

I used a Twitter thread to detail how “resistance” and “Russiagate” personalities and pages take advantage of human psychology and the quirks of social media to amass huge followings – and how you can learn from their tactics.

The first innovation is outrage – by tapping into hot, highly emotional news stories (with the help of services like ActionSprout, NewsWhip, or CrowdTangle) and either reposting or repackaging content with a photo meme or short video, guerilla publishers push their brands into mainstream consciousness with viral content. “Found” videos – often from cell phones capturing shocking interpersonal conflicts are turned into branded viral content as well. However, Facebook is continually working on its algorithms to de-emphasise contrived viral content to keep user feeds focused on friendlier inter-personal content and its own advertising. Posting popular mainstream news under a brand or influencer page is one of the safest ways to increase name ID without running afoul of Facebook rules.

Over on Twitter, there is no sign of a crackdown on contrived content. Mini-celebrities like those mentioned in Coppins’ article found the emotional outrage in the Mueller investigation of Trump – so how did the breakouts happen? Some of the media figures involved in Russiagate had full-time content producing jobs, but others, like the notorious Krassenstein brothers, used raw marketing smarts to center themselves in Resistance Twitter. It’s a formula that’s easy to copy – if you (or a virtual assistant) have time.

The secret to predictable, overnight increases in exposure on Twitter is being among the first to offer a relevant reply to a set of large, popular accounts. In practice, what this looks like is several hundred accounts replying within seconds to tweets by top politicians, executives, and Hollywood celebrities. And what it looks like in your own Twitter analytics is the bars on the right:

This strategy to generate hundreds of dollars or more in organic impressions per day plus a steady stream of new followers works like this:

  • Create a bank of content and a strategy for updating it regularly.
  • Turn on notifications on the Twitter account you will use for replies.
  • Turn on notifications for the list of popular users (they get lots of replies) that best matches your content strategy.
  • Be one of the first to reply whenever these users tweet.
  • Mix up the content and list so that you’re not reusing a meme or link too frequently.


In a month-long experiment with this strategy, my average impressions jumped 3x. You can do the same.

Adriel Hampton is a marketing consultant and founder of The Really Online Lefty League.