MisinfoEval: Generative AI in the Era of "Alternative Facts"

• URL: http://arxiv.org/abs/2410.09949v2
• Date: Tue, 15 Oct 2024 02:46:27 GMT
• Title: MisinfoEval: Generative AI in the Era of "Alternative Facts"
• Authors: Saadia Gabriel, Liang Lyu, James Siderius, Marzyeh Ghassemi, Jacob Andreas, Asu Ozdaglar,
• Abstract summary: We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
• Score: 50.069577397751175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The spread of misinformation on social media platforms threatens democratic processes, contributes to massive economic losses, and endangers public health. Many efforts to address misinformation focus on a knowledge deficit model and propose interventions for improving users' critical thinking through access to facts. Such efforts are often hampered by challenges with scalability, and by platform users' personal biases. The emergence of generative AI presents promising opportunities for countering misinformation at scale across ideological barriers. In this paper, we introduce a framework (MisinfoEval) for generating and comprehensively evaluating large language model (LLM) based misinformation interventions. We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users with the goal of countering misinformation by appealing to their pre-existing values. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior (improving overall user accuracy at reliability labeling by up to 41.72%). Furthermore, we find that users favor more personalized interventions when making decisions about news reliability and users shown personalized interventions have significantly higher accuracy at identifying misinformation.

Related papers

• Countering Misinformation via Emotional Response Generation [15.383062216223971]
  proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and democracy. Previous research has shown how social correction can be an effective way to curb misinformation. We present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs.
  arXiv  Detail & Related papers  (2023-11-17T15:37:18Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Measuring the Effect of Influential Messages on Varying Personas [67.1149173905004]
  We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona might have upon seeing a news message. The proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response. This enables more accurate and comprehensive inference on the mental state of the persona.
  arXiv  Detail & Related papers  (2023-05-25T21:01:00Z)
• Debiasing Recommendation by Learning Identifiable Latent Confounders [49.16119112336605]
  Confounding bias arises due to the presence of unmeasured variables that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. We propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables to resolve the aforementioned non-identification issue.
  arXiv  Detail & Related papers  (2023-02-10T05:10:26Z)
• Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda [0.9659642285903421]
  Data scientists need to take a stance on the objectivity, authoritativeness and legitimacy of the sources of truth" used for model training and testing. Despite (and due to) their reported high accuracy and performance, ML-driven moderation systems have the potential to shape online public debate and create downstream negative impacts such as undue censorship and the reinforcing of false beliefs.
  arXiv  Detail & Related papers  (2022-10-05T17:34:51Z)
• Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes [79.79659145328856]
  We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation. We make the case that polarization and misinformation adherence are closely tied.
  arXiv  Detail & Related papers  (2022-06-30T12:34:24Z)
• Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management [61.88858330222619]
  We present an approach for predicting trust links between peers in social media. We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
  arXiv  Detail & Related papers  (2021-11-11T19:40:51Z)
• Characterizing User Susceptibility to COVID-19 Misinformation on Twitter [40.0762273487125]
  This study attempts to answer it who constitutes the population vulnerable to the online misinformation in the pandemic. We distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation.
  arXiv  Detail & Related papers  (2021-09-20T13:31:15Z)
• Incentives for Federated Learning: a Hypothesis Elicitation Approach [10.452709936265274]
  Federated learning provides a promising paradigm for collecting machine learning models from distributed data sources. The success of a credible federated learning system builds on the assumption that the decentralized and self-interested users will be willing to participate. This paper introduces solutions to incentivize truthful reporting of a local, user-side machine learning model.
  arXiv  Detail & Related papers  (2020-07-21T04:55:31Z)

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.