|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM351554009 |
| 003 |
DE-627 |
| 005 |
20240916231949.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1007/s11227-022-05032-y
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1535.xml
|
| 035 |
|
|
|a (DE-627)NLM351554009
|
| 035 |
|
|
|a (NLM)36644509
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Barve, Yashoda
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Detecting and classifying online health misinformation with 'Content Similarity Measure (CSM)' algorithm
|b an automated fact-checking-based approach
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 16.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
|
| 520 |
|
|
|a Information dissemination occurs through the 'word of media' in the digital world. Fraudulent and deceitful content, such as misinformation, has detrimental effects on people. An implicit fact-based automated fact-checking technique comprising information retrieval, natural language processing, and machine learning techniques assist in assessing the credibility of content and detecting misinformation. Previous studies focused on linguistic and textual features and similarity measures-based approaches. However, these studies need to gain knowledge of facts, and similarity measures are less accurate when dealing with sparse or zero data. To fill these gaps, we propose a 'Content Similarity Measure (CSM)' algorithm that can perform automated fact-checking of URLs in the healthcare domain. Authors have introduced a novel set of content similarity, domain-specific, and sentiment polarity score features to achieve journalistic fact-checking. An extensive analysis of the proposed algorithm compared with standard similarity measures and machine learning classifiers showed that the 'content similarity score' feature outperformed other features with an accuracy of 88.26%. In the algorithmic approach, CSM showed improved accuracy of 91.06% compared to the Jaccard similarity measure with 74.26% accuracy. Another observation is that the algorithmic approach outperformed the feature-based method. To check the robustness of the algorithms, authors have tested the model on three state-of-the-art datasets, viz. CoAID, FakeHealth, and ReCOVery. With the algorithmic approach, CSM showed the highest accuracy of 87.30%, 89.30%, 85.26%, and 88.83% on CoAID, ReCOVery, FakeHealth (Story), and FakeHealth (Release) datasets, respectively. With a feature-based approach, the proposed CSM showed the highest accuracy of 85.93%, 87.97%, 83.92%, and 86.80%, respectively
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Content Similarity Measure
|
| 650 |
|
4 |
|a Content similarity score
|
| 650 |
|
4 |
|a Fact-checking
|
| 650 |
|
4 |
|a Healthcare
|
| 650 |
|
4 |
|a Misinformation detection
|
| 650 |
|
4 |
|a Natural language processing
|
| 650 |
|
4 |
|a Similarity measures
|
| 700 |
1 |
|
|a Saini, Jatinderkumar R
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g 79(2023), 8 vom: 16., Seite 9127-9156
|w (DE-627)NLM098252410
|x 0920-8542
|7 nnns
|
| 773 |
1 |
8 |
|g volume:79
|g year:2023
|g number:8
|g day:16
|g pages:9127-9156
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1007/s11227-022-05032-y
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 79
|j 2023
|e 8
|b 16
|h 9127-9156
|