Pith. sign in

REVIEW 4 major objections 4 minor 38 references

Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Adding ELM-derived text features to a hybrid CNN-LSTM lifts accuracy on COVID-19 misinformation detection from 94.90% to 97.37%, with the improvement statistically significant (Wilcoxon p<0.0001).

desk verdict A clearly written incremental study whose statistical significance claim is demonstrably wrong and whose dataset source confound undercuts the ELM attribution. read the letter →

arxiv 2507.09149 v1 pith:WGP7X42Z submitted 2025-07-12 cs.SI cs.AIcs.CYcs.LG

Mkululi Sikosana , Sean Maudsley-Barton , Oluwaseun Ajao This is my paper · ORCID
classification cs.SIcs.AIcs.CYcs.LG
keywords COVID-19healthmisinformationdetectionfakenewsclassificationmachinelearninginpublicelaborationlikelihoodmodelhybridCNN-LSTMfeatureengineeringtextstatistics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that features derived from the Elaboration Likelihood Model can improve automated detection of COVID-19 misinformation on Twitter. The authors build a hybrid CNN-LSTM classifier that reads tweet text, then concatenate ten hand-crafted text statistics—readability, vocabulary richness, sentiment polarity, text length, average words per sentence, punctuation ratios, capitalization, and urgency terms—to the learned representation. On the COVID19-FNIR dataset the enhanced model raises accuracy from 94.90% to 97.37%, with precision, recall, F1, and ROC-AUC all moving up, and a Wilcoxon signed-rank test reports p<0.0001 across cross-validation folds. A further model with extended engineered features reaches 99.37% accuracy. The paper's point, if it holds, is that cheap, psychologically motivated features give deep text classifiers a small but reliable boost rather than replacing content analysis.

What carries the argument

The paper's central object is the ELM feature vector $E(x_i) = [C(x_i), P(x_i)]$, the concatenation of central-route statistics (Flesch-Kincaid grade level, vocabulary richness, sentiment polarity, text length, average words per sentence) and peripheral-route statistics (exclamation-mark ratio, question-mark ratio, capitalization ratio, all-caps word count, urgency-term frequency). The ELM itself is a persuasion theory that distinguishes deep content scrutiny (central route) from surface heuristic cues (peripheral route); the paper treats the former as content-like signals and the latter as style-like signals. In the enhanced model this engineered vector is scaled and concatenated with the final LSTM hidden state of the text before a dense sigmoid layer, so the classifier sees both learned semantic representations and explicit theory-guided cues.

What would settle it

Run the same enhanced versus base model comparison on a second, independently collected health misinformation dataset, or on a version of COVID19-FNIR where URLs and source markers are stripped and balanced across classes; if the accuracy gain over the text-only baseline disappears, the reported benefit is a dataset artifact rather than an ELM feature effect.

Watch

Extended reading notes

Core claim

The central claim is that ELM-informed feature engineering improves a hybrid CNN-LSTM's ability to separate true from fake COVID-19 health news. The base model, which sees only raw text, achieves 94.90% accuracy; appending the ELM feature vector lifts it to 97.37% accuracy, 96.88% precision, 98.50% recall, 97.41% F1, and 99.50% ROC-AUC, with the gain reported as statistically significant under a Wilcoxon signed-rank test (p<0.0001). A features-only model reaches only 90.05% accuracy, which the paper reads as evidence that these cues are auxiliary: they help a strong semantic model fine-tune its decisions, but cannot stand alone. The paper also reports a combined model with a broader engineered feature set reaching 99.37% accuracy, and it frames the overall pattern as support for using dual-route persuasion theory to guide feature design in misinformation detection.

Load-bearing premise

The load-bearing premise is that the COVID19-FNIR corpus is an artifact-free benchmark of health misinformation, with no leftover systematic differences between true and fake classes (such as links or writing style) that the model could learn instead of the intended ELM cues.

Editorial extensions

If this is right

  • Appending roughly ten hand-crafted text statistics to a CNN-LSTM raises accuracy, precision, recall, and F1 on the COVID19-FNIR benchmark by two to three percentage points over the text-only model.
  • Because the features-only model clearly underperforms the text model, the paper's result is that theory-guided cues complement learned semantic representations rather than replace them.
  • The gain being consistent across 10-fold cross-validation and significant under a Wilcoxon test supports treating ELM-style features as a low-cost addition to existing detectors.
  • The paper itself cautions that near-ceiling scores on a single dataset make external validation necessary before the approach is generalized.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves implicit: since true-news entries in COVID19-FNIR frequently contained links that were removed during preprocessing, some of the reported gain may stem from residual source-format differences; re-running with URL fragments and source indicators balanced between classes would isolate the true feature effect.
  • The ELM's central and peripheral routes are operationalized through readability and punctuation proxies, which are only approximate; a stronger test would compare feature importance against human ratings of message elaboration or argument quality.
  • The same feature-augmentation recipe could be carried to other health misinformation corpora (different platforms, languages, or topics) to see whether the two-to-four point accuracy gain persists or shrinks.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes a hybrid CNN-LSTM model that augments text embeddings with handcrafted features derived from the Elaboration Likelihood Model (ELM), dividing them into central-route features (readability, vocabulary richness, sentiment, text length, words per sentence) and peripheral-route features (punctuation ratios, capitalization, all-caps counts, urgency terms). The model is evaluated on the COVID19-FNIR dataset with 10-fold stratified cross-validation. The authors report that the ELM-enhanced model outperforms a text-only base model (accuracy 97.37% vs. 94.90%), and that a combined model with extended features reaches 99.37% accuracy. They claim the improvement is statistically significant via a Wilcoxon signed-rank test (p < 0.0001) and a one-tailed paired t-test. The paper discusses confusion matrices and ROC curves and concludes that ELM-based features provide consistent, statistically significant gains to deep text classifiers.

Significance. If substantiated, the result would be a useful demonstration of how psychological theory can inform feature engineering for misinformation detection, with a clear and reproducible architecture (CNN-LSTM plus feature concatenation) and a publicly available dataset. The paper also compares text-only, feature-only, and combined models, which is informative for understanding the marginal contribution of handcrafted cues. However, the significance is substantially weakened by three load-bearing problems: the reported p-value is mathematically impossible for a 10-fold paired test, the dataset exhibits a source/genre confound that the ELM features are precisely designed to capture, and the feature set was selected through exploratory analysis on the same dataset used for evaluation. These issues currently prevent the paper from supporting its central claim that ELM-informed features improve misinformation detection per se.

major comments (4)
  1. [Section IV (Statistical Significance Testing)] The claim that the Wilcoxon signed-rank test yielded p < 0.0001 is mathematically impossible with 10 cross-validation folds. With k = 10 paired observations, the minimum achievable two-sided p-value is 2 / 2^10 = 0.001953125, so any p-value below 0.00195 cannot be correct. The one-tailed paired t-test statement is also unclear: reporting "p=1.0000 under Base > Enhanced" and then concluding this reinforces the enhanced model's superiority is contradictory; a p-value of 1.0000 for the alternative that Base > Enhanced would mean no support for that alternative, but it does not support Enhanced > Base. These errors undermine the statistical significance claim that is central to the paper's conclusion.
  2. [Section III.A and Section IV (Dataset Source Confound)] The COVID19-FNIR dataset draws fake news from Poynter fact-check records and true news from verified publishers' Twitter accounts. These are different document genres: fact-check narratives versus publisher tweets. Removing URLs during preprocessing does not eliminate the systematic differences in register, length, punctuation, and capitalization that distinguish these sources. The ELM features in Section III.B—text length, vocabulary richness, capitalization ratio, punctuation ratios—are precisely surface cues that separate these genres. The reported 94.90% to 97.37% accuracy improvement and near-ceiling ROC-AUC (99.50%) may therefore reflect source/genre classification rather than misinformation detection. The paper itself acknowledges this risk and calls for external validation (Section IV.B, [44]), but the abstract and conclusion still attribute the gains to ELM-based misinformation detection. This is a load-bearing threat to the paper's central claim.
  3. [Section II.E (Theoretical and Methodological Foundation)] The ELM feature set was chosen after exploratory data analysis on a subset of 500 tweets from the same COVID19-FNIR dataset, and the evaluation is then performed on the full dataset with the same cross-validation splits. This is data-driven feature selection on the test distribution, which inflates the apparent benefit of the features. The paper does not report a nested cross-validation or a held-out selection procedure to account for this. Consequently, the improvement over the base model may be an artifact of feature selection on the evaluation data rather than evidence that the ELM framework specifically provides useful information.
  4. [Section IV (Results, Tables 2 and 3)] There are internal numerical inconsistencies that make the reported results difficult to trust. Table 2 reports the enhanced model's ROC-AUC as 99.50%, but the prose states "the enhanced model's ROC-AUC (100%) is slightly above the base." Section IV.A similarly says recall improved "from 98.43% to 100%" for the enhanced and combined models, but Table 3 lists the combined model's recall as 99.80% and the enhanced model's recall as 98.50%. These discrepancies need correction and clarification of which numbers are final.
minor comments (4)
  1. [Throughout] The manuscript contains numerous typographical and formatting errors, including inconsistent citation formatting (e.g., missing closing brackets for references like [22]), incomplete sentences, and duplicated training descriptions in Section III.C. A thorough proofread is needed.
  2. [Section I (Introduction)] The claim that this is "the first study to examine the effectiveness of ELM in the context of health misinformation detection" is too strong, since references [14] and [33] apply ELM to health-related misinformation or debunking contexts. The novelty should be narrowed to the specific combination of ELM-derived textual features with a hybrid CNN-LSTM model.
  3. [Section III.C (Model Architecture)] The description of the CNN-LSTM architecture is redundant, with the same setup explained twice in the same section. The mathematical formulation of the CNN and LSTM is generic and could be condensed, while the actual implementation details (e.g., how ELM features are concatenated and scaled) would benefit from more specificity.
  4. [Section IV (Statistical Testing)] If statistical testing is retained, the authors should report the full distribution of per-fold accuracies (or at least the mean and standard deviation) and clearly state the exact test statistic and p-value. With only 10 folds, exact permutation tests or confidence intervals would be more appropriate than asymptotic approximations.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the enhanced-model gain is measured on held-out cross-validation folds and does not reduce to the ELM feature definitions or the exploratory data analysis that motivated them.

full rationale

The paper's derivation chain is: ELM supplies a taxonomy of central-route (readability, sentiment, text length) and peripheral-route (punctuation, capitalization, urgency) text statistics; these are concatenated with CNN-LSTM text embeddings; performance is measured by stratified 10-fold cross-validation comparing a text-only base model with the same architecture plus features. The features are fixed text statistics, not parameters fitted to the target, and the reported metrics are out-of-fold, so the +2.47% accuracy gain (Table 1) and the combined-model gain (Table 3) are not equivalent by construction to the feature values or to the Section II.E EDA p-values. The exploratory analysis on 500 tweets from COVID19-FNIR is a legitimate feature-selection/validity concern, since the ELM feature set was chosen with knowledge of class differences in the same corpus, but it does not force the held-out result, and the paper explicitly limits generalizability to this single dataset (Section V.B) and flags dataset-specific artifacts (Section IV.B and IV.C, where reference [44] is cited but missing from the reference list). There is no load-bearing self-citation, no imported uniqueness theorem, and no ansatz hidden in a citation. The missing reference and the unusual Wilcoxon p-value are reporting or statistical defects, not circularity. Overall, the central empirical claim retains independent content and could have been disconfirmed by the held-out comparisons.

Assumptions & free parameters 7 free parameters · 3 assumptions · 0 invented entities

The central claim rests on a single dataset with potential built-in class differences, on an assumed mapping from simple text statistics to a psychological theory, and on several hand-chosen hyperparameters. No new theoretical entities are introduced.

free parameters (7)
  • learning_rate = 0.001
    Adam optimizer learning rate, chosen by hand in Section III.C.
  • batch_size = 32
    Training batch size, hand-chosen in Section III.C.
  • epochs = 10
    Training epochs, hand-chosen in Section III.C.
  • dropout_rate = 0.5
    Dropout rate in the CNN component, hand-chosen in Section III.C.
  • embedding_dim = 100
    Keras embedding dimension, hand-chosen in Section III.C.
  • cnn_filters = 64
    Number of 1D convolutional filters, hand-chosen in Section III.C.
  • lstm_units = 100
    LSTM output dimension, hand-chosen in Section III.C.
assumptions (3)
  • domain assumption COVID19-FNIR dataset labels are reliable and the dataset is representative of social media misinformation
    Section III.A describes the dataset sourced from Poynter and verified news publishers; label validity and representativeness are assumed without external checks.
  • domain assumption The selected text statistics (readability, sentiment, punctuation) operationalize the Elaboration Likelihood Model's central and peripheral routes
    Sections II.E and III.B map these features to ELM constructs, but no validation is provided that they actually represent cognitive elaboration or peripheral cue processing.
  • domain assumption Removing URLs and special characters eliminates the confound that true news texts often include links
    Section III.A notes true news texts often include links; preprocessing removes URLs, but other systematic differences between the two classes may remain.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)." pith.science (2026). https://pith.science/paper/WGP7X42Z

@misc{pith2026250709149,
  author       = {Pith},
  title        = {Pith review of: Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WGP7X42Z}},
  note         = {Machine review of arXiv:2507.09149}
}
read the original abstract

Health misinformation during the COVID-19 pandemic has significantly challenged public health efforts globally. This study applies the Elaboration Likelihood Model (ELM) to enhance misinformation detection on social media using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The model aims to enhance the detection accuracy and reliability of misinformation classification by integrating ELM-based features such as text readability, sentiment polarity, and heuristic cues (e.g., punctuation frequency). The enhanced model achieved an accuracy of 97.37%, precision of 96.88%, recall of 98.50%, F1-score of 97.41%, and ROC-AUC of 99.50%. A combined model incorporating feature engineering further improved performance, achieving a precision of 98.88%, recall of 99.80%, F1-score of 99.41%, and ROC-AUC of 99.80%. These findings highlight the value of ELM features in improving detection performance, offering valuable contextual information. This study demonstrates the practical application of psychological theories in developing advanced machine learning algorithms to address health misinformation effectively.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

38 extracted references · 36 canonical work pages

  1. [38]

    S., & Tra, Y

    Scannell, D., Desens, L., Day, D. S., & Tra, Y. (2022). Combatting Mis/Disinformation: Combining Predictive Modeling and Machine Learning with Persuasion Science to Understand COVID -19 Vaccine Online Discourse. Medical Research Archives, 10(3), 1-19

    work page 2022

  2. [1]

    J., Pizarro, A

    Borges do Nascimento, I. J., Pizarro, A. B., Almeida, J. M., Azzopardi -Muscat, N., Gonçalves, M. A., Björklund, M., & Novillo -Ortiz, D. (2022). Infodemics and health misinformation: a systematic review of reviews. Bulletin of the World Health Organization, 100(9), 544 –561. https://doi.org/10.2471/BLT.21.287654

    doi:10.2471/blt.21.287654 2022

  3. [2]

    Caceres, F. M. M., Sosa, J. P., Lawrence, J. A., Sestacovschi, C., Tidd -Johnson, A., Rasool, M. H. U., Gadamidi, V. K., Ozair, S., Pandav, K., Cuevas - Lou, C., Parrish, M., Rodriguez, I., & Fernandez, J. P. (2022). The impact of misinformation on the COVID-19 pandemic. AIMS public health, 9(2), 262–277. https://doi.org/10.3934/publichealth.2022018

    doi:10.3934/publichealth.2022018 2022

  4. [3]

    Chou, S. W. Y., Gaysynsky, A., & Cappella, J. N. (2020). Where We Go From Here: Health Misinformation on Social Media. American journal of public health, 110(S3), S273 –S275. https://doi.org/10.2105/AJPH.2020.305905

    work page arXiv 2020

  5. [4]

    D., & Keikhosrokiani, P

    Ravichandran, B. D., & Keikhosrokiani, P. (2023). Classification of Covid-19 misinformation on social media based on neuro -fuzzy and neural network: A systematic review. Neural computing & applications, 35(1), 699–717

    work page 2023

  6. [5]

    Using Network Analysis to Detect Fake News in Social Media,

    Kaur, H (2023). "Using Network Analysis to Detect Fake News in Social Media," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-9,

    work page 2023

  7. [6]

    Suarez-Lledo, V., & Alvarez -Galvez, J. (2021). Prevalence of health misinformation on social media: systematic review. Journal of medical Internet research, 23(1), e17187

    work page 2021

  8. [7]

    Fact- checking

    Lee, S., Xiong, A., Seo, H., & Lee, D. (2023). “Fact- checking” fact checkers: A data -driven approach. Harvard Kennedy School Misinformation Review

    work page 2023

Show all 38 references
  1. [8]

    The elaboration likelihood model of persuasion

    Petty R, Cacioppo J (1986). The elaboration likelihood model of persuasion. Adv Exp Soc Psychol (C) 19:123-205

    1986

  2. [9]

    Muhammed T, S., & Mathew, S. K. (2022). The disaster of misinformation: a review of research in social media. International journal of data science and analytics, 13(4), 271–285

    2022

  3. [10]

    Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic literature review on the spread of health-related misinformation on social media. Social science & medicine, 240, 112552

    2019

  4. [11]

    Zhou, C., Xiu, H., Wang, Y., & Yu, X. (2021). Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID -19. Information processing & management, 58(4), 102554

    2021

  5. [12]

    Medfact: towards improving veracity of medical information in social media using applied machine learning

    Samuel H, Zaïane O (2018). Medfact: towards improving veracity of medical information in social media using applied machine learning. Lect Notes Comput Sci. 10832 LNAI:108–120

    2018

  6. [13]

    Kapusta, J., Drlik, M., & Munk, M. (2021). Using of n-grams from morphological tags for fake news classification. Peer J. Computer science, 7, e624

    2021

  7. [14]

    Zhang, S., Zhang, Y., Li, J., Ni, Z., & Liu, Z. (2024). Heart or Mind? The Impact of Congruence on the Persuasiveness of Cognitive versus Affective Appeals in Debunking Messages on Social Media 10 During Public Health Crises. Social Media + Society, 10(1), 2056305123111234

    2024

  8. [15]

    -L., Yang, Y

    Saini, V., Liang, L. -L., Yang, Y. -C., Le, H. M., & Wu, C. -Y. (2022). The Association Between Dissemination and Characteristics of Pro -/Anti- COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model. Health Communication, 37(2), 183-193. [16 Luo...

    2022

  9. [17]

    Guo, X., Chen, S., Zhang, X., Ju, X., & Wang, X. (2020). Exploring patients’ intentions for continuous usage of mHealth services: elaboration -likelihood perspective study. JMIR Mhealth Uhealth, 8(4), e17258

    2020

  10. [18]

    Hanmei, F., Reeva, L., Stephen, S., & Shanton, C. (2013). How online health forum users assessuser - generated content: mixed -method research. Presented at: Proceedings of the 21st European Conference on Information Systems (ECIS), Utrecht, the Netherlands

    2013

  11. [19]

    Munzel, A. (2016). Assisting consumers in detecting fake reviews: the role of identity information disclosure and consensus. Journal of Retailing and Consumer Services, 32, 96-108

    2016

  12. [20]

    Shi, J., Hu, P., Lai, K.K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites: an elaboration likelihood model perspective. Internet Research, 28(2), 393-418

    2018

  13. [21]

    Cheung, C., Sia, C., & Kuan, K. (2012). Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective. Journal of the Association for Information Systems, 13(8), 618-635. [22 Strahan, E., White, K., Fong, G., Fabriga...

    2012

  14. [23]

    Using the elaboration likelihood model to address drunkorexia among college students

    Glassman T, Paprzycki P, Castor T, Wotring A, Wagner-Greene V, Ritzman M, et al., (2018). Using the elaboration likelihood model to address drunkorexia among college students. Subst Use Misuse 53(9):1411-1418

    2018

  15. [24]

    Berger, J. (2012). What makes online content viral? Strategic Direction, 28(8), 90-91

    2012

  16. [25]

    Stieglitz, S., & Dang-Xuan, L. (2014). Emotions and information diffusion in social media —sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217-248

    2014

  17. [26]

    Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network

    Suh B, Hong L, Pirolli P, Chi E (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. 2010 Presented at: SocialCom 2010: the Second IEEE International Conference on Social Computing, PASSAT nd IEEE International Conference on Priv...

    2010

  18. [27]

    Making retweeting social: the influence of content and context information on sharing news in Twitter

    Rudat A, Buder J (2015). Making retweeting social: the influence of content and context information on sharing news in Twitter. Comput Human Behav 46:75-84

    2015

  19. [28]

    Lai, L., & To, W. (2015). Content analysis of social media: a grounded theory approach. Journal of Electronic Commerce Research, 16(2), 138

    2015

  20. [29]

    Melki, J., Tamim, H., Hadid, D., Makki, M., El Amine, J., & Hitti, E. (2021). Mitigating infodemics: the relationship between news exposure and trust and belief in COVID -19 fake news and social media spreading. PLOS ONE, 16(6), e0252830

    2021

  21. [30]

    Trope, Y., & Liberman, N. (2010). Construal -level theory of psychological distance. Psychological Review, 117(2), 440-463

    2010

  22. [31]

    The effect of abstract and concrete thinking on risk-taking behavior in women and men

    Lermer E, Streicher B, Sachs R, Raue M, Frey D (2016). The effect of abstract and concrete thinking on risk-taking behavior in women and men. SAGE Open 6(3):215824401666612

    2016

  23. [32]

    J., Du, Q., Zhang, X., Qiao, Z., Wang, A., & Fan, W

    Zhou, M. J., Du, Q., Zhang, X., Qiao, Z., Wang, A., & Fan, W. P. (2015). Money talks: A predictive model on crowdfunding success using project descriptions. Paper presented at the Twenty -first Americas Conferences on Information Systems, Puerto Rico

    2015

  24. [33]

    M., Chiu, C

    Cheng, H.-L., Tan, C. M., Chiu, C. -M., Huang, H. - Y., & Lee, Y. -C. (2024). Using the Elaboration Likelihood Model to Explore the Impact of Fake News on Panic Vaccination Intention: Taiwan’s COVID-19 Vaccination Phenomenon. Journal of Medical Internet Research, 26(1), e123456

    2024

  25. [34]

    Janze, C., & Risius, M. (2018). Automatic Detection of Fake News on Social Media Platforms. Proceedings of the 23rd European Conference on Information Systems (ECIS). Retrieved from: https://aisel.aisnet.org/ecis2018_rp/158

    2018

  26. [35]

    Cyr, D., Head, M., Lim, E., & Stibe, A. (2018). Using the elaboration likelihood model to examine online persuasion through website design. Information & Management, 55(7), 807-821

    2018

  27. [36]

    Sui, H., & Zhang, Y. (2021). Determinants of the Perceived Credibility of Rebuttals Concerning Health Misinformation: Application of the Elaboration Likelihood Model. International Journal of Environmental Research and Public Health, 18(4), 2045

    2021

  28. [37]

    Chen, X., & Chang, C. (2019). Factors associated with belief or disbelief in false news: From the perspective of elaboration likelihood and moderating effect model. Journal of Health Communication, 24(12), 1033-1042. 11

    2019

  29. [39]

    Covid -19 Fake News Infodemic Research Dataset (CoVID19-FNIR Dataset)

    Saenz, J. A., Kalathur Gopal, S. R., & Shukla, D. (2021). "Covid -19 Fake News Infodemic Research Dataset (CoVID19-FNIR Dataset)", IEEE Dataport, doi: https://dx.doi.org/10.21227/b5bt-5244

    2021 doi

  30. [40]

    Liu, Z., Zhang, T., Yang, K., Thompson, P., Yu, Z., & Ananiadou, S. (2024). Emotion detection for misinformation: A review. Information Fusion, 107, 102300

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.