Phil 1.15.19

7:00 – 3:00 ASRC NASA

  • Cool antibubbles thing: artboard 1
  • Also, I looked into a Slack version of Antibubbles. You can download conversations as JSON files, and I’d need to build (or find) a dice bot.
  • Fake News, Real Money: Ad Tech Platforms, Profit-Driven Hoaxes, and the Business of Journalism
    • Following the viral spread of hoax political news in the lead-up to the 2016 US presidential election, it’s been reported that at least some of the individuals publishing these stories made substantial sums of money—tens of thousands of US dollars—from their efforts. Whether or not such hoax stories are ultimately revealed to have had a persuasive impact on the electorate, they raise important normative questions about the underlying media infrastructures and industries—ad tech firms, programmatic advertising exchanges, etc.—that apparently created a lucrative incentive structure for “fake news” publishers. Legitimate ad-supported news organizations rely on the same infrastructure and industries for their livelihood. Thus, as traditional advertising subsidies for news have begun to collapse in the era of online advertising, it’s important to understand how attempts to deal with for-profit hoaxes might simultaneously impact legitimate news organizations. Through 20 interviews with stakeholders in online advertising, this study looks at how the programmatic advertising industry understands “fake news,” how it conceptualizes and grapples with the use of its tools by hoax publishers to generate revenue, and how its approach to the issue may ultimately contribute to reshaping the financial underpinnings of the digital journalism industry that depends on the same economic infrastructure.
  • The structured backbone of temporal social ties
    • In many data sets, information on the structure and temporality of a system coexists with noise and non-essential elements. In networked systems for instance, some edges might be non-essential or exist only by chance. Filtering them out and extracting a set of relevant connections is a non-trivial task. Moreover, mehods put forward until now do not deal with time-resolved network data, which have become increasingly available. Here we develop a method for filtering temporal network data, by defining an adequate temporal null model that allows us to identify pairs of nodes having more interactions than expected given their activities: the significant ties. Moreover, our method can assign a significance to complex structures such as triads of simultaneous interactions, an impossible task for methods based on static representations. Our results hint at ways to represent temporal networks for use in data-driven models.
  • Brandon RohrerData Science and Robots
  • Physical appt?
  • Working on getting the histories calculated and built
    • Best contracts are: contract 4 = 6, contract 5 = 9,  contract 12 = 10, contract 18 = 140
    • Lots of discussion on how exactly to do this. I think at this point I’m waiting on Heath to pull some new data that I can then export to Excel and play with to see the best way of doing things