How conspiracy theories emerge—and how their storylines fall apart
Jun 2020, phys.org
^Came for the image: "Researchers produced a graphic representation of the narratives they analyzed, with layers for major subplots of each story, and lines connecting the key people, places and institutions within and among those layers." Credit: UCLA College, UCLA Samueli School of Engineering
Stay for the story: They studied Bridgegate NJ (real conspiracy) and Pizzagate DC (fake conspiracy). And the AI can figure out which is which.
All stories are a narrative network, the elements being the characters, places and things in the story, and the network emerging from the relationships between these elements.
If you tell the AI enough stories, it will detect patterns in the networks.
"One of the characteristics of a conspiracy theory narrative framework is that it is easily 'disconnected,'" said Timothy Tangherlini, one of the paper's lead authors, a professor in the UCLA Scandinavian section whose scholarship focuses on folklore, legend and popular culture. "If you take out one of the characters or story elements of a conspiracy theory, the connections between the other elements of the story fall apart."
Real stories can still make sense even if you remove one of the elements. Something about the myriad connections between the elements, because they're real people and places, all connected in other ways not intrinsic to the story. Robustness you might call it when talking about telecommunications networks; or redundancy.
Pizzagate? You remove Wikileaks from the network and it falls apart. (People used this massive data dump to link all kinds of elements in creative ways, building vast and complex stories. But the data was too big, and had no connection to each other beyond their being in the same repository.
Also the timeframe, and for similar reasons, is a good point of contrast. Real stories build over time, because again, all the elements are connected in myriad ways with each other for reasons that have nothing to do with any one particular story. Fake stories appear out of nowhere, and tend to evaporate just as quickly.
Timothy R. Tangherlini et al. An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web, PLOS ONE (2020). DOI: 10.1371/journal.pone.0233879. http://dx.doi.org/10.1371/journal.pone.0233879
Same article but from The Conversation, Nov 2020.
Coronavirus - Harmful lies spread easily due to lack of UK law
Jul 2020, BBC News
I won't even bother to link here the other recent report about how malicious social interference dramatically (and wholly undetected?) altered public opinion in the Brexit election. I think that's general public knowledge by now.
From the article: Despite investing in measures, the UK Digital, Culture, Media and Sport Committee says tech firms can not be left to self-regulate. They also said that social media firms' advertising-focused business models had encouraged the spread of misinformation and allowed "bad actors" to make money from emotional content, regardless of the truth. By bad actors, their report identifies state actors, including Russia, China and Iran; the Islamic State group; far-right groups in the US and the UK; and scammers.
Tracking misinformation campaigns in real-time is possible, study shows
Aug 2020, phys.org
Metadata is key.
There's so much good information just in this summary alone, so I copying much of it:
Using data from past misinformation (troll) campaigns from China, Russia, and Venezuela waged against the United States before and after the 2016 election, combined with posts to Twitter and Reddit and the hyperlinks or URLs they included, using a "postURL pair," which is simply a post with a hyperlink.
8,000 accounts and 7.2 million posts from late 2015 through 2019.
They reinforced their model with a baseline of a rich dataset of politically engaged and average user posts collected over many years by NYU's Center for Social Media and Politics (CSMaP).
They teased out data for timing, word count, whether the mentioned URL domain is a news website, and most importantly "metacontent," for example, whether a URL was in the top 25 political domains shared by other trolls.
Trolls referring to other trolls and not new sources: "Both trolls and normal people often include local news URLs in their posts, but the trolls tended to mention different users in such posts, probably because they are trying to draw their audience's attention in a new direction. Metacontent lets the algorithm find such anomalies."
-Jacob N. Shapiro, professor of politics and international affairs at the Princeton School of Public and International Affairs
Across almost all of the 463 different tests, it was clear which posts were and were not part of an influence operation, meaning that content-based features can indeed help find coordinated influence campaigns on social media.
Venezuelan trolls only retweeted certain people and topics, making them easy to detect. Russian and Chinese trolls were better at making their content look organic, but they, too, could be found. In early 2016, for example, Russian trolls quite often linked to far-right URLs, which was unusual given the other aspects of their posts, and, in early 2017, they linked to political websites in odd ways. Overall, Russian troll activity became harder to find as time went on. It is possible that investigative groups or others caught on to the false information, flagging the posts and forcing trolls to change their tactics or approach, though Russians also appear to have produced less in 2018 than in previous years.
via Princeton University, New York University and New Jersey Institute of Technology: M. Alizadeh el al., "Content-based features predict social media influence operations," Science Advances (2020). doi:10.1126/sciadv.abb5824. https://advances.sciencemag.org/lookup/doi/10.1126/sciadv.abb5824
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| Nikon Small World 2020 - Single Neuron - Nadia Efimova |
A novel strategy for quickly identifying twitter trolls
Aug 2020, phys.org
50 tweets is all it takes.
Using sociolinguistic "troll-specific restrictions" on words and word pairs, the signal in the noise becomes clear. Granted, they would have to update their restrictions depending on what cultural phenomena they're using in their campaigns, but it works in real time.
Also, they checked their model using a library of Russian troll tweets; because this exists.
Monakhov S (2020) Early detection of internet trolls: Introducing an algorithm based on word pairs / single words multiple repetition ratio. PLoS ONE 15(8): e0236832. doi.org/10.1371/journal.pone.0236832
Calls to city 311 lines can predict opioid overdose hotspots
Nov 2020, phys.org
This is just old school predictive analytics.
"Complaints to the city about issues like streetlight repair, abandoned vehicles and code violations reflect disorder and distress that are also linked to opioid use," said Yuchen Li, lead author of the study and doctoral candidate in geography at The Ohio State University.
via Ohio State: Scientific Reports (2020). DOI: 10.1038/s41598-020-76685-z
New algorithm signals a possible disease resurgence
Sep 2020, phys.org
The algorithm monitors public health data to detect statistical patterns associated with impending outbreaks to predict the reemergence of existing infectious diseases.
Changes in vaccination rates, or even changes in climate, can predict the emergence of an epidemic.
They used 10,000 sets of simulated case reports covering a period of 10 years. When tested on a 2004-5 Mumps outbreak, the model predicted it four years on advance. Looking at Pertussis, it could predict 100% at the state level 1990-2000. It works for vector-borne disease as well (ticks, mosquitoes).
Tobias S. Brett et al. Dynamical footprints enable detection of disease emergence, PLOS Biology (2020). DOI: 10.1371/journal.pbio.3000697
How Facebook, Twitter, YouTube, and Reddit are handling the election
Nov 2020, Ars Technica
"In addition to its stated policies, Facebook is reportedly standing ready to implement a slew of policies it has used in the past for managing election content in "at-risk" countries such as Sri Lanka and Myanmar. These tools would include limiting the rate at which content beginning to go viral can travel, as well as tweaking the newsfeed to change what type of content users see."
So we can stop the virus from spreading all along.
