FireAnt: Claim-Based Medical Misinformation Detection and Monitoring

Pecher, Branislav; Srba, Ivan; Móro, Róbert; Tomlein, Matúš; Bieliková, Mária

doi:10.1007/978-3-030-67670-4_38

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…Due to significant negative consequences of misinformation on our society (especially during the ongoing COVID-19 pandemic), tackling misinformation attracted also a plethora of research works (see [9,56,58] for recent surveys). The majority of such research focuses on various characterization studies [46] or detection methods [40,49].…”

Section: Background: Filter Bubbles and Misinformationmentioning

confidence: 99%

Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles

Srba¹,

Móro²,

Tomlein³

et al. 2023

ACM Trans. Recomm. Syst.

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In this paper, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it takes to “burst the bubble”, i.e., revert the bubble enclosure. We employ a sock puppet audit methodology, in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content. Then they try to burst the bubbles and reach more balanced recommendations by watching misinformation debunking content. We record search results, home page results, and recommendations for the watched videos. Overall, we recorded 17,405 unique videos, out of which we manually annotated 2,914 for the presence of misinformation. The labeled data was used to train a machine learning model classifying videos into three classes (promoting, debunking, neutral) with the accuracy of 0.82. We use the trained model to classify the remaining videos that would not be feasible to annotate manually. Using both the manually and automatically annotated data, we observe the misinformation bubble dynamics for a range of audited topics. Our key finding is that even though filter bubbles do not appear in some situations, when they do, it is possible to burst them by watching misinformation debunking content (albeit it manifests differently from topic to topic). We also observe a sudden decrease of misinformation filter bubble effect when misinformation debunking videos are watched after misinformation promoting videos, suggesting a strong contextuality of recommendations. Finally, when comparing our results with a previous similar study, we do not observe significant improvements in the overall quantity of recommended misinformation content.

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Section: Background: Filter Bubbles and Misinformationmentioning

confidence: 99%

Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles

Srba¹,

Móro²,

Tomlein³

et al. 2023

ACM Trans. Recomm. Syst.

Self Cite

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“…Apart from the existing health crisis, the spread of the ubiquitous problem of misinformation poses additional health threats and presents another unprecedented issue to the world [7,8]. This creates a severe effect on people's life and medical experts as well [9].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Fact-checking Misinformation using “Veracity Scanning Model”

Barve¹,

Saini²,

Kotecha³

et al. 2022

IJACSA

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The expeditious flow of information over the web and its ease of convenience has increased the fear of the rampant spread of misinformation. This poses a health threat and an unprecedented issue to the world impacting people's life. To cater to this problem, there is a need to detect misinformation. Recent techniques in this area focus on static models based on feature extraction and classification. However, data may change at different time intervals and the veracity of data needs to be checked as it gets updated. There is a lack of models in the literature that can handle incremental data, check the veracity of data and detect misinformation. To fill this gap, authors have proposed a novel Veracity Scanning Model (VSM) to detect misinformation in the healthcare domain by iteratively factchecking the contents evolving over the period of time. In this approach, the healthcare web URLs are classified as legitimate or non-legitimate using sentiment analysis as a feature, document similarity measures to perform fact-checking of URLs, and incremental learning to handle the arrival of incremental data. The experimental results show that the Jaccard Distance measure has outperformed other techniques with an accuracy of 79.2% with Random Forest classifier while the Cosine similarity measure showed less accuracy of 60.4% with the Support Vector Machine classifier. Also, when implemented as an algorithm Euclidean distance showed an accuracy of 97.14% and 98.33% respectively for train and test data.

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