Approaches to Identify Vulnerabilities to Misinformation: A Research Agenda

• URL: http://arxiv.org/abs/2210.11647v1
• Date: Fri, 21 Oct 2022 00:33:39 GMT
• Title: Approaches to Identify Vulnerabilities to Misinformation: A Research Agenda
• Authors: Nattapat Boonprakong, Benjamin Tag, Tilman Dingler
• Abstract summary: Given the prevalence of online misinformation, Internet users have been shown to frequently fall victim to such information. We highlight two ongoing avenues of research to identify vulnerable users: detecting cognitive biases and exploring misinformation spreaders.
• Score: 24.80321868079655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given the prevalence of online misinformation and our scarce cognitive capacity, Internet users have been shown to frequently fall victim to such information. As some studies have investigated psychological factors that make people susceptible to believe or share misinformation, some ongoing research further put these findings into practice by objectively identifying when and which users are vulnerable to misinformation. In this position paper, we highlight two ongoing avenues of research to identify vulnerable users: detecting cognitive biases and exploring misinformation spreaders. We also discuss the potential implications of these objective approaches: discovering more cohorts of vulnerable users and prompting interventions to more effectively address the right group of users. Lastly, we point out two of the understudied contexts for misinformation vulnerability research as opportunities for future research.

Related papers

• MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
  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.
  arXiv  Detail & Related papers  (2024-10-13T18:16:50Z)
• How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
  Browser fingerprinting can be used to identify and track users across the Web, even without cookies. This technique and resulting privacy risks have been studied for over a decade. We provide a first-of-its-kind dataset to enable further research.
  arXiv  Detail & Related papers  (2024-10-09T14:51:58Z)
• Banal Deception Human-AI Ecosystems: A Study of People's Perceptions of LLM-generated Deceptive Behaviour [11.285775969393566]
  Large language models (LLMs) can provide users with false, inaccurate, or misleading information. We investigate peoples' perceptions of ChatGPT-generated deceptive behaviour.
  arXiv  Detail & Related papers  (2024-06-12T16:36:06Z)
• A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures [50.987594546912725]
  Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures.
  arXiv  Detail & Related papers  (2024-03-31T12:44:48Z)
• Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure [10.288282142373976]
  Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources. The adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels.
  arXiv  Detail & Related papers  (2023-11-09T20:16:06Z)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [76.47138162283714]
  Forgetting refers to the loss or deterioration of previously acquired information or knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• Machine Learning for Detection and Mitigation of Web Vulnerabilities and Web Attacks [0.0]
  Cross-site scripting (XSS) and cross-site request forgery (CSRF) have been a great concern in the field of web security. Several ideas have been put forth that can be used to improve the performance of detecting these web vulnerabilities. Machine learning techniques have lately been used by researchers to defend against XSS and CSRF.
  arXiv  Detail & Related papers  (2023-04-27T18:27:26Z)
• 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)
• 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)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)
• Information Credibility in the Social Web: Contexts, Approaches, and Open Issues [2.2133187119466116]
  Credibility, also referred as believability, is a quality perceived by individuals, who are not always able to discern, with their own cognitive capacities, genuine information from fake one. Several approaches have been proposed to automatically assess credibility in social media.
  arXiv  Detail & Related papers  (2020-01-26T15:42:43Z)

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.