Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • About
Liwaiwai Liwaiwai
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
    • Architecture
    • Design
    • Software
    • Hybrid Cloud
    • Data
  • About
  • Artificial Intelligence
  • Machine Learning

An AI Tool Can Distinguish Between A Conspiracy Theory And A True Conspiracy

  • November 18, 2020
  • admin
In the age of social media, conspiracy theories are collective creations. AP Photo/Ted S. Warren

The audio on the otherwise shaky body camera footage 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.”

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.

Conspiracy theories, which have the potential to cause significant harm, have found a welcome home on social media, 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.”

But how can you tell if an emerging narrative on social media 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.

The culture analytics group at the University of California, which I and Vwani Roychowdhury lead, has developed an automated approach 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, the COVID-19 pandemic and anti-vaccination movements. We’re currently using these methods to study QAnon.

 

Collaboratively constructed, fast to form

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.

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 – mostly because they do not exist.

Read More  IBM Announces Demonstration Project With U.S. Patent And Trademark Office To Help Make Patent Research Insights More Accessible And Usable
People are susceptible to conspiracy theories by nature, and periods of uncertainty and heightened anxiety increase that susceptibility.

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. The participants work out the parameters of a narrative framework: the people, places and things of a story and their relationships.

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

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 in the final week of October 2016.

 

AI narrative analysis

We developed a model – a set of machine learning tools – that can identify narratives 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.

Read More  Sowing The Seeds Of Ethical AI: 4 Tasks To Stay On Track

We analyzed 17,498 posts from April 2016 through February 2018 on the Reddit and 4chan forums where Pizzagate was discussed. The model treats each post as a fragment of a hidden story and sets about to uncover the narrative. The software identifies the people, places and things in the posts and determines which are major elements, which are minor elements and how they’re all connected.

The model determines the main layers of the narrative – in the case of Pizzagate, Democratic politics, the Podesta brothers, casual dining, satanism and WikiLeaks – and how the layers come together to form the narrative as a whole.

To ensure that our methods produced accurate output, we compared the narrative framework graph produced by our model with illustrations published in The New York Times. Our graph aligned with those illustrations, and also offered finer levels of detail about the people, places and things and their relationships.

 

Sturdy truth, fragile fiction

To see if we could distinguish between a conspiracy theory and an actual conspiracy, we examined Bridgegate, a political payback operation launched by staff members of Republican Gov. Chris Christie’s administration against the Democratic mayor of Fort Lee, New Jersey.

As we compared the results of our machine learning system using the two separate collections, two distinguishing features of a conspiracy theory’s narrative framework stood out.

First, while the narrative graph for Bridgegate took from 2013 to 2020 to develop, Pizzagate’s graph was fully formed and stable within a month. Second, Bridgegate’s graph survived having elements removed, implying that New Jersey politics would continue as a single, connected network even if key figures and relationships from the scandal were deleted.

The Pizzagate graph, in contrast, was easily fractured into smaller subgraphs. When we removed the people, places, things and relationships that came directly from the interpretations of the WikiLeaks emails, the graph fell apart into what in reality were the unconnected domains of politics, casual dining, the private lives of the Podestas and the odd world of satanism.

Read More  If Machines Can Be Inventors, Could AI Soon Monopolise Technology?

In the illustration below, the green planes are the major layers of the narrative, the dots are the major elements of the narrative, the blue lines are connections among elements within a layer and the red lines are connections among elements across the layers. The purple plane shows all the layers combined, showing how the dots are all connected. Removing the WikiLeaks plane yields a purple plane with dots connected only in small groups.

Two graphs, one above and one below, showing dots with interconnecting lines
The layers of the Pizzagate conspiracy theory combine to form a narrative, top right. Remove one layer, the fanciful interpretations of emails released by WikiLeaks, and the whole story falls apart, bottom right. Tangherlini, et al., CC BY

Early warning system?

There are clear ethical challenges that our work raises. Our methods, for instance, could be used to generate additional posts to a conspiracy theory discussion that fit the narrative framework at the root of the discussion. Similarly, given any set of domains, someone could use the tool to develop an entirely new conspiracy theory.

However, this weaponization of storytelling is already occurring without automatic methods, as our study of social media forums makes clear. There is a role for the research community to help others understand how that weaponization occurs and to develop tools for people and organizations who protect public safety and democratic institutions.

Developing an early warning system that tracks the emergence and alignment of conspiracy theory narratives could alert researchers – and authorities – to real-world actions people might take based on these narratives. Perhaps with such a system in place, the arresting officer in the Pizzagate case would not have been baffled by the gunman’s response when asked why he’d shown up at a pizza parlor armed with an AR-15 rifle.The Conversation

 

This article is republished from The Conversation under a Creative Commons license by Timothy R. Tangherlini, Professor of Danish Literature and Culture, University of California, Berkeley

admin

Related Topics
  • Artifical Intelligence
  • conspiracy
  • conspiracy theory
  • Covid-19 pandemic
  • Machine Learning
  • Pizzagate
You May Also Like
View Post
  • Artificial Intelligence
  • Software
  • Technology

Bard And ChatGPT — A Head To Head Comparison

  • March 31, 2023
View Post
  • Artificial Intelligence
  • Platforms

Modernize Your Apps And Accelerate Business Growth With AI

  • March 31, 2023
View Post
  • Artificial Intelligence
  • Technology

Unlocking The Secrets Of ChatGPT: Tips And Tricks For Optimizing Your AI Prompts

  • March 29, 2023
View Post
  • Artificial Intelligence
  • Technology

Try Bard And Share Your Feedback

  • March 29, 2023
View Post
  • Artificial Intelligence
  • Data
  • Data Science
  • Machine Learning
  • Technology

Google Data Cloud & AI Summit : In Less Than 12 Hours From Now

  • March 29, 2023
View Post
  • Artificial Intelligence
  • Technology

Talking Cars: The Role Of Conversational AI In Shaping The Future Of Automobiles

  • March 28, 2023
View Post
  • Artificial Intelligence
  • Tools

Document AI Introduces Powerful New Custom Document Classifier To Automate Document Processing

  • March 28, 2023
View Post
  • Artificial Intelligence
  • Design
  • Practices

How AI Can Improve Digital Security

  • March 27, 2023

Leave a Reply

Your email address will not be published. Required fields are marked *

Stay Connected!
LATEST
  • 1
    Bard And ChatGPT — A Head To Head Comparison
    • March 31, 2023
  • 2
    Modernize Your Apps And Accelerate Business Growth With AI
    • March 31, 2023
  • 3
    Why Your Open Source Project Needs A Content Strategy
    • March 31, 2023
  • 4
    From Raw Data To Actionable Insights: The Power Of Data Aggregation
    • March 30, 2023
  • 5
    Effective Strategies To Closing The Data-Value Gap
    • March 30, 2023
  • 6
    Unlocking The Secrets Of ChatGPT: Tips And Tricks For Optimizing Your AI Prompts
    • March 29, 2023
  • 7
    Try Bard And Share Your Feedback
    • March 29, 2023
  • 8
    Google Data Cloud & AI Summit : In Less Than 12 Hours From Now
    • March 29, 2023
  • 9
    Talking Cars: The Role Of Conversational AI In Shaping The Future Of Automobiles
    • March 28, 2023
  • 10
    Document AI Introduces Powerful New Custom Document Classifier To Automate Document Processing
    • March 28, 2023

about
About
Hello World!

We are liwaiwai.com. Created by programmers for programmers.

Our site aims to provide materials, guides, programming how-tos, and resources relating to artificial intelligence, machine learning and the likes.

We would like to hear from you.

If you have any questions, enquiries or would like to sponsor content, kindly reach out to us at:

[email protected]

Live long & prosper!
Most Popular
  • 1
    Introducing GPT-4 in Azure OpenAI Service
    • March 21, 2023
  • 2
    How AI Can Improve Digital Security
    • March 27, 2023
  • 3
    ChatGPT 4.0 Finally Gets A Joke
    • March 27, 2023
  • 4
    Mr. Cooper Is Improving The Home-buyer Experience With AI And ML
    • March 24, 2023
  • 5
    My First Pull Request At Age 14
    • March 24, 2023
  • /
  • Artificial Intelligence
  • Machine Learning
  • Robotics
  • Engineering
  • About

Input your search keywords and press Enter.