The Truth is Out There: Investigating Conspiracy Theories in Text Generation

• URL: http://arxiv.org/abs/2101.00379v1
• Date: Sat, 2 Jan 2021 05:47:39 GMT
• Title: The Truth is Out There: Investigating Conspiracy Theories in Text Generation
• Authors: Sharon Levy, Michael Saxon, William Yang Wang
• Abstract summary: We investigate the propensity for language models to generate conspiracy theory text. Our study focuses on testing these models for the elicitation of conspiracy theories. We introduce a new dataset consisting of conspiracy theory topics, machine-generated conspiracy theories, and human-written conspiracy theories.
• Score: 66.01545519772527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing adoption of text generation models in today's society, users are increasingly exposed to machine-generated text. This in turn can leave users vulnerable to the generation of harmful information such as conspiracy theories. While the propagation of conspiracy theories through social media has been studied, previous work has not evaluated their diffusion through text generation. In this work, we investigate the propensity for language models to generate conspiracy theory text. Our study focuses on testing these models for the elicitation of conspiracy theories and comparing these generations to human-written theories from Reddit. We also introduce a new dataset consisting of conspiracy theory topics, machine-generated conspiracy theories, and human-written conspiracy theories. Our experiments show that many well-known conspiracy theory topics are deeply rooted in the pre-trained language models, and can become more prevalent through different model settings.

Related papers

• Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization [42.242551342068374]
  We conducted a comprehensive analysis of two distinct X datasets: one comprising users with conspiracy theorizing patterns and another made of users lacking such tendencies. Our findings reveal marked differences in the lexicon and language adopted by conspiracy theorists with respect to other users. We developed a machine learning classifier capable of identifying users who propagate conspiracy theories based on a rich set of 871 features.
  arXiv  Detail & Related papers  (2024-05-21T08:07:38Z)
• Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections [4.594855794205588]
  This work establishes a general scheme for classifying discussions related to conspiracy theories. We leverage human-labeled ground truth to train a BERT-based model for classifying online CTs. We present the first large-scale classification study using posts from the most active conspiracy-related Reddit forums.
  arXiv  Detail & Related papers  (2024-03-29T20:29:12Z)
• Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
  We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news. Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
  arXiv  Detail & Related papers  (2023-11-02T08:39:45Z)
• The Conspiracy Money Machine: Uncovering Telegram's Conspiracy Channels and their Profit Model [50.80312055220701]
  We discover that conspiracy channels can be clustered into four distinct communities comprising over 17,000 channels. We find conspiracy theorists leverage e-commerce platforms to sell questionable products or lucratively promote them through affiliate links. We conclude that this business involves hundreds of thousands of donors and generates a turnover of almost $66 million.
  arXiv  Detail & Related papers  (2023-10-24T16:25:52Z)
• SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning [55.44065342292846]
  Generative commonsense reasoning is the task that requires machines, given a group of keywords, to compose a single coherent sentence with commonsense plausibility. We formalize this challenging task as SituatedGen, where machines with commonsense should generate a pair of contrastive sentences given a group of keywords including geographical or temporal entities. Experiments show that state-of-the-art generative language models struggle to generate sentences with commonsense plausibility and still lag far behind human performance.
  arXiv  Detail & Related papers  (2023-06-21T20:36:55Z)
• YouNICon: YouTube's CommuNIty of Conspiracy Videos [7.135697290631831]
  YOUNICON is a dataset with a large collection of videos from suspicious channels that were identified to contain conspiracy theories. This paper seeks to develop a dataset, YOUNICON, to enable researchers to perform conspiracy theory detection as well as classification of videos with conspiracy theories into different topics.
  arXiv  Detail & Related papers  (2023-04-11T15:20:51Z)
• Where the Earth is flat and 9/11 is an inside job: A comparative algorithm audit of conspiratorial information in web search results [62.997667081978825]
  We examine the distribution of conspiratorial information in search results across five search engines: Google, Bing, DuckDuckGo, Yahoo and Yandex. We find that all search engines except Google consistently displayed conspiracy-promoting results and returned links to conspiracy-dedicated websites in their top results. Most conspiracy-promoting results came from social media and conspiracy-dedicated websites while conspiracy-debunking information was shared by scientific websites and, to a lesser extent, legacy media.
  arXiv  Detail & Related papers  (2021-12-02T14:29:21Z)
• An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web [0.0]
  We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction.
  arXiv  Detail & Related papers  (2020-08-23T05:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.