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Steve Bosserman

Here's How Facebook Actually Won Trump the Presidency | WIRED - 0 views

  • Social media was Trump’s primary communication channel. It wasn’t a platform for broadcasting pre-planned messages but for interacting with supporters and starting new conversations—however controversial those conversations often were. Bleeker says one of the biggest lessons he’s learned from this election cycle is that social media is increasingly going to be part of any candidate’s so-called “earned media strategy”—that is, the coverage a candidate gets for free in the press. The President-elect has shown he can turn a news cycle in 140 characters or less; in a recent 60 Minutes interview, he said he plans to continue using Twitter as president. “He’s going to tell his side of the story from the digital bully pulpit,” Lira says. Whether fake news did or didn’t affect the election’s outcome, Facebook as a platform did. The winning candidate was not just willing, but eager to break with traditional models of campaigning. His team invested in new ways of using the digital tools and platforms that have come to dominate the media landscape. Anyone who wants to defeat him in the future will have to do the same.
Bill Fulkerson

Unspeakable Realities Block Universal Health Coverage In America - 0 views

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    "When it seems like people are voting against their interests, I have probably failed to understand their interests. We cannot begin to understand Election 2016 until we acknowledge the power and reach of socialism for white people. Americans with good jobs live in a socialist welfare state more generous, cushioned and expensive to the public than any in Europe. Like a European system, we pool our resources to share the burden of catastrophic expenses, but unlike European models, our approach doesn't cover everyone."
Bill Fulkerson

Detecting Regions At Risk for Spreading COVID-19 Using Existing Cellular Wireless Netwo... - 0 views

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    Goal: The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities-handover and cell (re)selection-used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the at-risk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation.
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