Search (4 results, page 1 of 1)

  • × theme_ss:"Internet"
  • × year_i:[2020 TO 2030}
  1. Dijk, J: ¬The digital divide (2020) 0.04
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    RSWK
    Soziale Ungleichheit
    Subject
    Soziale Ungleichheit
  2. Fielitz, M.; Marcks, H.: Digitaler Faschismus : die sozialen Medien afs Motor des Rechtsextremismus (2020) 0.02
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    Footnote
    Rez. u.d.T.: Witte-Petit, K.: Radikale Rattenfänger : zwei Forscher beschreiben den 'digitalen Faschismus' in: Rheinpfalz vom 19.08.2021 [Fielitz-Marcks_Rez_RP_20210819.pdf]: "Es gibt mittlerweile viele Bücher darüber, wie soziale Medien die Verbreitung extremen Gedankenguts erleichtern. Das vom Rechtsextremismusforscher Maik Fielitz und dem Radikalisierungsexperten Holger. Marcks vorgelegte Buch "Digitaler Faschismus" gehört zu den wirklich lesenswerten. Leider macht es aber auch wenig Hoffnung."
  3. Schrenk, P.: Gesamtnote 1 für Signal - Telegram-Defizite bei Sicherheit und Privatsphäre : Signal und Telegram im Test (2022) 0.01
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    Date
    22. 1.2022 14:01:14
  4. Ding, J.: Can data die? : why one of the Internet's oldest images lives on wirhout its subjects's consent (2021) 0.00
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    Abstract
    In 2021, sharing content is easier than ever. Our lingua franca is visual: memes, infographics, TikToks. Our references cross borders and platforms, shared and remixed a hundred different ways in minutes. Digital culture is collective by default and has us together all around the world. But as the internet reaches its "dirty 30s," what happens when pieces of digital culture that have been saved, screenshotted, and reposted for years need to retire? Let's dig into the story of one of these artifacts: The Lenna image. The Lenna image may be relatively unknown in pop culture today, but in the engineering world, it remains an icon. I first encountered the image in an undergrad class, then grad school, and then all over the sites and software I use every day as a tech worker like Github, OpenCV, Stack Overflow, and Quora. To understand where the image is today, you have to understand how it got here. So, I decided to scrape Google scholar, search, and reverse image search results to track down thousands of instances of the image across the internet (see more in the methods section).