{"id":112177,"date":"2020-11-13T18:10:46","date_gmt":"2020-11-13T15:10:46","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/ai-tool-can-distinguish-between-a-conspiracy-theory-and-a-true-conspiracy\/"},"modified":"2020-11-13T18:10:46","modified_gmt":"2020-11-13T15:10:46","slug":"ai-tool-can-distinguish-between-a-conspiracy-theory-and-a-true-conspiracy","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/ai-tool-can-distinguish-between-a-conspiracy-theory-and-a-true-conspiracy\/","title":{"rendered":"#AI tool can distinguish between a conspiracy theory and a true conspiracy"},"content":{"rendered":"

“#AI tool can distinguish between a conspiracy theory and a true conspiracy<\/strong>”<\/p>\n

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\"conspiracy\"
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The audio on the otherwise shaky body camera footage<\/a> is unusually clear. As police officers search a handcuffed man who moments before had fired a shot inside a pizza parlor, an officer asks him why he was there. The man says to investigate a pedophile ring. Incredulous, the officer asks again. Another officer chimes in, “Pizzagate. He’s talking about Pizzagate.”<\/p>\n

In that brief, chilling interaction in 2016, it becomes clear that conspiracy theories, long relegated to the fringes of society, had moved into the real world in a very dangerous way.<\/p>\n

Conspiracy theories, which have the potential to cause significant harm, have found a welcome home on social media<\/a>, where forums free from moderation allow like-minded individuals to converse. There they can develop their theories and propose actions to counteract the threats they “uncover.”<\/p>\n

But how can you tell if an emerging narrative on social media<\/a> is an unfounded conspiracy theory? It turns out that it’s possible to distinguish between conspiracy theories and true conspiracies by using machine learning tools to graph the elements and connections of a narrative. These tools could form the basis of an early warning system to alert authorities to online narratives that pose a threat in the real world.<\/p>\n

The culture analytics group at the University of California, which I<\/a> and Vwani Roychowdhury<\/a> lead, has developed an automated app<\/a>roach to determining when conversations on social media reflect the telltale signs of conspiracy theorizing. We have applied these methods successfully to the study of Pizzagate<\/a>, the COVID-19 pandemic<\/a> and anti-vaccination movements<\/a>. We’re currently using these methods to study QAnon<\/a>. <\/p>\n

Collaboratively constructed, fast to form<\/b><\/p>\n

Actual conspiracies are deliberately hidden, real-life actions of people working together for their own malign purposes. In contrast, conspiracy theories are collaboratively constructed and develop in the open.
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Conspiracy theories are deliberately complex and reflect an all-encompassing worldview. Instead of trying to explain one thing, a conspiracy theory tries to explain everything, discovering connections across domains of human interaction that are otherwise hidden\u2014mostly because they do not exist. <\/p>\n

While the popular image of the conspiracy theorist is of a lone wolf piecing together puzzling connections with photographs and red string, that image no longer applies in the age of social media. Conspiracy theorizing has moved online and is now the end-product of a collective storytelling<\/a>. The participants work out the parameters of a narrative framework: the people, places and things of a story and their relationships.<\/p>\n

The online nature of conspiracy theorizing provides an opportunity for researchers to trace the development of these theories from their origins as a series<\/a> of often disjointed rumors and story pieces to a comprehensive narrative. For our work, Pizzagate presented the perfect subject.<\/p>\n

Pizzagate began to develop in late October 2016 during the runup to the presidential election. Within a month, it was fully formed, with a complete cast of characters drawn from a series of otherwise unlinked domains: Democratic politics, the private lives of the Podesta brothers, casual family dining and satanic pedophilic trafficking. The connecting narrative thread among these otherwise disparate domains was the fanciful interpretation of the leaked emails of the Democratic National Committee dumped by WikiLeaks<\/a> in the final week of October 2016.<\/p>\n

AI narrative analysis<\/b><\/p>\n

We developed a model\u2014a set of machine learning<\/a> tools\u2014that can identify narratives<\/a> based on sets of people, places and things and their relationships. Machine learning algorithms process large amounts of data to determine the categories of things in the data and then identify which categories particular things belong to.<\/p>\n


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