Comparing Traditional and Neural Approaches for Detecting Health-Related Misinformation

Fernández-Pichel, Marcos; Losada, David E.; Pichel, Juan C.; Elsweiler, David

doi:10.1007/978-3-030-85251-1_7

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…Ferna ´ndez-Pichel et al [82], provided a comprehensive comparison of recent deep NLP models, such as newer BERT-based models (DistilBERT, DistilRoBERTa), and traditional algorithms, such as SVM, RF, NB, and kNN, for detecting health-related misinformation, and identifying low-quality online content (web pages that are unreliable and hard to read). The CLEF 2018 Consumer Health Search task dataset was chosen by the authors.…”

Section: Public Healthmentioning

confidence: 99%

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A survey of uncover misleading and cyberbullying on social media for public health

et al. 2022

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Misleading health information is a critical phenomenon in our modern life due to advance in technology. In fact, social media facilitated the dissemination of information, and as a result, misinformation spread rapidly, cheaply, and successfully. Fake health information can have a significant effect on human behavior and attitudes. This survey presents the current works developed for misleading information detection (MLID) in health fields based on machine learning and deep learning techniques and introduces a detailed discussion of the main phases of the generic adopted approach for MLID. In addition, we highlight the benchmarking datasets and the most used metrics to evaluate the performance of MLID algorithms are discussed and finally, a deep investigation of the limitations and drawbacks of the current progressing technologies in various research directions is provided to help the researchers to use the most proper methods in this emerging task of MLID.

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Section: Public Healthmentioning

confidence: 99%

“…CLEF 2018 Consumer Health Search Task [82] focuses on the efficacy of health-related information offered by search engines. The collection includes 5,535,120 web pages retrieved from CommonCrawl3.…”

Section: Cyberbullying (Psychological Health)mentioning

confidence: 99%

A survey of uncover misleading and cyberbullying on social media for public health

et al. 2022

View full text Add to dashboard Cite

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“…Thanks to these open shared tasks, in the past years many prominent approaches have been developed to improve the search for higher-quality health information. In the absence of sufficiently large training data, classical bag-of-words-based methods tend to outperform neural network approaches in the detection of health-related misinformation [26]. On the other hand, a combination of classical handcrafted feature-based methods and deep learning-based methods can be effective when customized for health misinformation detection in Web content [17].…”

Section: Introductionmentioning

confidence: 99%

“…Thanks to these open shared tasks, several significant methodologies have been developed to improve the search for higher-quality health information. While classical bag-of-words-based methods outperform neural network approaches in detecting health-related misinformation when training data is limited [29], more advanced approaches are needed for Web content. Specifically, research has proven the effectiveness of a hybrid approach that integrates classical handcrafted features with deep learning [18].…”

Section: Introductionmentioning

confidence: 99%

“…Further to this, multi-stage ranking systems [30,31] have been proposed, which couple the system with a label prediction model or employ T5 [33] to re-rank BM25 results. Particularly, Lima et al [29] consider the stance of the search query and engage two assessors for an interactive search, integrating a continuous active learning method [58]. This approach sets a baseline of human effort in separating helpful from harmful Web content.…”

Section: Introductionmentioning

confidence: 99%

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Online health search via multi-dimensional information quality assessment based on deep language models

Zhang

Naderi

Mishra

et al. 2023

Preprint

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Background: The presence of widespread misinformation in Web resources and the limited quality control provided by search engines can lead to serious implications for individuals seeking health advice. Objective: We aimed to investigate a multi-dimensional information quality assessment model based on deep learning to enhance the reliability of online healthcare information search results. Methods: In this retrospective study, we simulated online health information search scenarios with a topic set of 35 different health-related inquiries and a corpus containing one billion Web documents from the April 2019 snapshot of Common Crawl. Using state-of-the-art pre-trained language models, we inferred the usefulness, supportiveness, and credibility quality dimensions of the retrieved documents for a given search query. Results: The usefulness model provided the largest distinction between help and harm compatibility documents with a difference of 0.053. The supportiveness model achieved the best harm compatibility (0.024), while the combination of usefulness, supportiveness, and credibility models achieved the best help and harm compatibility on helpful topics. Conclusions: Our results suggest that integrating automatic ranking models created for specific information quality dimensions can increase the effectiveness of health-related information retrieval for decision-making.

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What Happened in CLEF$$\ldots $$ For Another While?

Ferro

2024

Lecture Notes in Computer Science

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Comparing Traditional and Neural Approaches for Detecting Health-Related Misinformation

Cited by 5 publications

References 24 publications

A survey of uncover misleading and cyberbullying on social media for public health

A survey of uncover misleading and cyberbullying on social media for public health

Online health search via multi-dimensional information quality assessment based on deep language models

What Happened in CLEF$$\ldots $$ For Another While?

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