Health Misinformation in Social Networks: A Survey of IT Approaches

• URL: http://arxiv.org/abs/2410.18670v1
• Date: Thu, 24 Oct 2024 12:00:51 GMT
• Title: Health Misinformation in Social Networks: A Survey of IT Approaches
• Authors: Vasiliki Papanikou, Panagiotis Papadakos, Theodora Karamanidou, Thanos G. Stavropoulos, Evaggelia Pitoura, Panayiotis Tsaparas,
• Abstract summary: Survey aims at providing a systematic review of related research. We first present manual and automatic approaches for fact-checking. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation.
• Score: 2.1440886607229563
• License:
• Abstract: In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Specifically, we first present manual and automatic approaches for fact-checking. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation and of publicly available tools. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation.

Related papers

• 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)
• Multi-task Learning for Personal Health Mention Detection on Social Media [70.23889100356091]
  This research employs a multitask learning framework to leverage available annotated data to improve the performance on the main task. We focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task.
  arXiv  Detail & Related papers  (2022-12-09T23:49:00Z)
• Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues [24.428582199602822]
  The rise of various social media platforms also enables the proliferation of online misinformation. Health misinformation in social media has become an emerging research direction that attracts increasing attention from researchers of different disciplines.
  arXiv  Detail & Related papers  (2022-11-10T01:52:12Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• Medical Visual Question Answering: A Survey [55.53205317089564]
  Medical Visual Question Answering(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer.
  arXiv  Detail & Related papers  (2021-11-19T05:55:15Z)
• Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media [7.194177427819438]
  This paper presents a mechanism to detect COVID-19 health-related misinformation in social media. We defined misinformation themes and associated keywords incorporated into the misinformation detection mechanism using applied machine learning techniques. Our method shows promising results with at most 78% accuracy in classifying health-related misinformation versus true information.
  arXiv  Detail & Related papers  (2021-06-12T16:26:04Z)
• Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges [35.0667998623823]
  A large fraction of the common public turn to social media platforms for news and even information regarding highly concerning issues such as COVID-19 symptoms. There is a significant amount of ongoing research in the directions of disinformation detection and mitigation. We discuss the online disinformation problem, focusing on the recent 'infodemic' in the wake of the coronavirus pandemic.
  arXiv  Detail & Related papers  (2020-10-18T21:44:23Z)
• Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation [23.449057978351945]
  We frame health misinformation as a risk assessment task. We study the severity of each misinformation story and how readers perceive this severity. We evaluate several traditional and state-of-the-art models and show there is a significant gap in performance.
  arXiv  Detail & Related papers  (2020-10-17T08:34:57Z)
• Towards Domain-Specific Characterization of Misinformation [14.136862418249764]
  The rapid dissemination of health misinformation poses an increasing risk to public health. It is important to acknowledge how the fundamental characteristics of misinformation differ from domain to domain. This paper presents a pathway towards domain-specific characterization of misinformation.
  arXiv  Detail & Related papers  (2020-07-29T12:46:45Z)
• 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)
• Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements [55.33496599723126]
  disinformation including fake news has become a global phenomenon due to its explosive growth. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation.
  arXiv  Detail & Related papers  (2020-01-02T21:01:02Z)

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.