Another approach might be to have signature based scanning, kind of like virus scanners or SPAM filters.
One might imagine using natural language processing to normalize tweet or Facebook excerpts into the same `meme' object, and then require Twitter, Facebook, etc. by law to highlight those meme objects that have been shown to be controversial or false, linking to background documents. Like http://www.nytimes.com/2016/11/20/business/media/how-fake-news-spreads.html<http://www.nytimes.com/2016/11/20/business/media/how-fake-news-spreads.html?hp&action=click&pgtype=Homepage&clickSource=story-heading&module=b-lede-package-region®ion=top-news&WT.nav=top-news> Multimedia objects (like the pictures above of the buses) would be an even easier way to spot variant instances of the same thing. Photoshop manipulations could probably be identified by automated means. Authors with the intent to falsify news would probably switch between identities, so quantitative metrics on writing style or statistical patterns in target selection might be one way to identify culprits. The databases of bad content and bad actors ought to be open source, so that it can be elaborated by anyone with the time and energy to do the research. Marcus ________________________________ From: Friam <[email protected]> on behalf of Joe Spinden <[email protected]> Sent: Sunday, November 20, 2016 5:54:56 PM To: The Friday Morning Applied Complexity Coffee Group Subject: Re: [FRIAM] Automated Pro-Trump Bots Overwhelmed Pro-Clinton Messages, Researchers Say Not sure that is possible. But I would be happy with identifying and responding in real time.. Not thrilled to think the elections here and elsewhere are swayed by automated misinformation.. Joe On 11/20/16 12:49 PM, Marcus Daniels wrote: "[..] with a bit of [artificial] intelligence and rudimentary communication skills" Right tool for that job.. But how do you build an army of bots that can enlighten instead of just confuse? Marcus ________________________________ From: Friam <[email protected]><mailto:[email protected]> on behalf of Joe Spinden <[email protected]><mailto:[email protected]> Sent: Sunday, November 20, 2016 11:12:04 AM To: The Friday Morning Applied Complexity Coffee Group Subject: [FRIAM] Automated Pro-Trump Bots Overwhelmed Pro-Clinton Messages, Researchers Say For discussion ??: http://www.nytimes.com/2016/11/18/technology/automated-pro-trump-bots-overwhelmed-pro-clinton-messages-researchers-say.html -- Joe ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove ============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove -- Joe
============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College to unsubscribe http://redfish.com/mailman/listinfo/friam_redfish.com FRIAM-COMIC http://friam-comic.blogspot.com/ by Dr. Strangelove
