Open research questions in Misinformation and Its Impacts

What the literature leaves open

• Future research is needed to develop first-hand data on the association between disinformation content generated by AI and attitudes, behaviours, and the occurrence of offline events, in a privacy- and civil-liberties-protecting manner.

  Artificial Intelligence-driven disinformation campaigns and their influence on political violence and extremism · 2026 · DOI

• A prevalent challenge in mitigating this issue arises in non-English contexts and low socioeconomic classes, where limited data hinders the training of AI models for effective detection.

  Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study) · 2026

• While this study is among the first to analyze the factors associated with support for misinformation interventions, several limitations could be addressed in future work. First, we focused our survey on active social media users, as those individuals would be the most affected by any potential policies and the most familiar with current interventions. Our sample was a convenience sample of US residents recruited via CloudResearch’s MTurk Toolkit and was not designed to be demographically representative of US social media users. MTurk samples are known to differ from population benchmarks on characteristics such as age, education, and partisanship (e.g., Stagnaro et al. (2024)), and although our participants’ demographics and platform use are broadly consistent with recent estimates for US social media users (Gottfried and Park 2025), generalizations should be made with caution. Moreover, we only examined preferences in a single cultural and national context. The US is a relevant and important context to study public support for misinformation interventions. Many of the largest social media platforms (e.g., Facebook, X) are headquartered in the US, so US legal and political debates about content moderation (e.g., around Section 230) likely shape how platforms design and justify their policies globally. Furthermore, cross-national work on resilience to disinformation suggests that the US may be especially vulnerable due to high political polarization and a fragmented commercial media system (Humprecht, Esser, and Van Aelst 2020). However, there is reason to expect preferences over who should intervene against misinformation, and how, to vary across cultures and regions. For instance, Germans tend to attribute greater responsibility to the government to respond to problematic online content than Americans do (Riedl et al. 2021), plausibly increasing their support for interventions implemented by government rather than companies. We therefore view our findings as characterizing the contemporary US context and encourage future research to investigate how and why intervention preferences vary across regions. Journal of Online Trust and Safety (2026) 19 We also only included ten interventions to limit the length of the survey, allowing us to focus more on the factors behind support. However, several new and emerging interventions were not included. For example, X’s Community Notes program has been relatively successful at increasing the volume of fact-checks and boosting trust in misinformation flags by using crowd-sourced misinformation detection and labels (Chuai et al. 2024; Drolsbach, Solovev, and Pröllochs 2024), although reports of its overall effectiveness in reducing engagement with misleading content are mixed (Chuai et al. 2024; Kankham and Hou 2024). Future research should supplement these results by surveying a wider range of interventions.

  Public Support for Misinformation Interventions Depends On Perceived Fairness, Effectiveness, and Intrusiveness · 2026 · DOI

• Surprisingly, the same study revealed that higher human development (measured by factors such as education and health) had a positive correlation with fake news propensity, suggesting that general education alone is insufficient to build resilience against sophisticated misinformation campaigns (Shirish & Kotwal, 2023).

  Designing a Decentralized, Gamified Platform for Misinformation Detection · 2026 · DOI

• In particular, further work is required to refine proportionality reasoning in constitutional terms and to justify more precisely the criteria for identifying sufficiently high levels of epistemic power, both with respect to the appropriate follower thresholds and the possible restriction of criminal responsibility to narrowly defined domains of high-risk public interest, such as public health.

  Disinformation and Superspreaders: With Epistemic Power Should Come Criminal Responsibility · 2026 · DOI

• However, several limitations should be noted when interpret- ing these findings. First, the data were drawn exclusively from Reddit, which is mostly popular in North America and other English-speaking countries. Therefore, the results may not reflect the global landscape of Mpox discourse or the communication norms of other platforms. Second, only 4 subreddits were included, and r/Monkeypox [11] contributed a disproportionately large number of posts and comments. This imbalance could bias the observed patterns toward the norms of that particular community, potentially underrepresenting the perspectives seen in more generalist spaces. Third, engagement was measured using Reddit scores (upvotes minus downvotes), which capture one form of audience response, but do not fully represent other inter- action behaviors such as commenting, sharing, or passive viewing. Fourth, engagement differences by sentiment may be influenced by unmeasured factors such as author visibility, posting frequency, or source type (eg, personal narratives vs news links), which were not explicitly controlled for in the analysis. Although no individual author contributed more than 25 posts out of the 1169 total (2.1% of the dataset), sug- gesting that no single author dominated the dataset, residual confounding by author-level or source-level characteristics cannot be ruled out. Fifth, the long temporal range of the dataset (2021‐2025) spans both the early emergency phase of the outbreak and later periods of reduced transmission. As a result, the analysis may conflate discourse dynamics that differ between acute crisis communication and more endemic-phase discussion. Sixth, keyword-based filtering was used to identify Mpox-related posts, which may have omitted relevant discussions occurring in broader news or general- interest subreddits that did not explicitly use the selected terms. In addition, concise topic theme labels were generated with assistance from an LLM after statistical topic assignments were finalized. While the clustering itself was produced using LDA, the summarization step introduces some degree of stochasticity and subjectivity inherent to LLM-generated outputs. To improve stability, the summarization process was repeated multiple times using different random sam- ples of documents per topic, and the resulting summaries were aggregated, though the themes were not independently validated by multiple human coders or alternative models. Furthermore, due to computational and token limita- tions, the LLM-based interpretation step relied on randomly sampled subsets of documents from each topic rather than the full set of posts and comments assigned to that topic. Although these samples were drawn repeatedly across multiple runs to capture representative content, a small proportion of topic-related messages may not have been included in the summaries used to derive the final theme labels. Finally, sentiment analysis relied on a lexicon-based approach, which may not fully capture sarcasm, irony, or context-specific language commonly found in Reddit discussions. Such nuances may affect the precision of sentiment classification. While these limitations restrict the generalizability of the findings, they also highlight the value of Reddit as a lens into globally circulating narratives, concerns, and framings of Mpox that may cross national boundaries.

  Reddit Discussions During the 2022 Mpox Outbreak: Observational Analysis of Sentiment, Topics, and Audience Engagement · 2026 · DOI

• We use rank-based percentiles to capture users’ relative posi- tion in a way that is robust to outliers and platform-specific scale (because engagement distributions vary widely across platforms like X and Truth Social, and are often heavy- tailed, raw comparisons or z-scores can be misleading). To evaluate how robust each method is under limited data conditions, we progressively subsample user posts and measure the minimum percent- age of data needed to reach 95% of each method’s peak AUC.

  Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems · 2026 · DOI

• The importance of correctness and trustworthiness of information may be due to how AI chatbots are seen as newcomers which have not yet established their position as credible sources information, as compared to Google search engine and Wikipedia.

  The credibility of information generated by AI chatbots: an analysis of online discussions in Reddit · 2026 · DOI

• This study introduced FRARS, which integrates transformer-based deception probabilities into a Neural Matrix Factorisation recommender. Evaluated end-to-end on YelpCHI and YelpNYC—datasets that differ in scale, region, and deception prevalence—FRARS consistently improved top-k performance under both soft weighting and hard filtering, raising NDCG@10 by roughly 20% on each. The detector–recommendation link was strong and significant on both datasets (Spearman ρ = 0.964 and 0.929, p < 0.01), confirming that detector quality measurably determines recommendation robustness. The consistency of these results across two independent datasets supports the external validity of the approach. The results show that strengthening the epistemic quality of training data does not require major architectural redesign; meaningful gains are achievable by intelligently incorporating deception risk into existing collaborative filtering pipelines. This provides a practical, low-friction pathway for platforms seeking to improve the robustness and trustworthiness of their recommendation systems. Future research directions include: (1) cross-platform evaluation on Amazon, TripAdvisor, and other datasets; (2) cross-architecture validation on LightGCN, BPR, and graph-based recommenders; (3) multilingual and multimodal deceptive opinion detection; (4) non-linear credibility weighting and uncertainty-aware loss formulations; (5) formal calibration procedures; (6) compatibility with emerging generative retrieval Scientific Reports | (2026) 16:17510 | https://doi.org/10.1038/s41598-026-56220-2 15 recommenders and LLM-based detectors; (7) user trust perception studies and fairness audits; and (8) direct empirical comparison with trust-aware CF baselines.

  Integrating transformer-based credibility signals into neural collaborative filtering for fake review-aware recommendation · 2026 · DOI

• In this paper, we propose G-Defense, a novel graph-enhanced defense framework for explainable fake news detection with LLM. The framework first decomposes a news claim into several relevant sub-claims and then constructs a claim-centered graph to capture their dependency relationships. Then, it conducts evidence retrieval for each sub-claim and generates competing explanations for two competing veracity labels. After that, to infer the final veracity, G-Defense performs a defense- like inference over the claim-centered graph supplemented with competing explanations, comparing the quality of the competing explanations and analyzing the rich dependency relationships. Finally, an LLM is prompted with the graph and prediction result to generate a fine-grained explanation graph and a summarized textual explanation. Experimental results on two real-world datasets demonstrate that our proposed G-Defense achieves state-of-the-art performance in both veracity prediction and explanation quality. Extensive ablation study further verifies the effectiveness of each component in G-Defense. Human evaluation and case study further validate that the explanations generated by our framework are helpful and intuitive for identifying fake news. In the future, we plan to broaden the applicability of G-Defense by evaluating it on other types of textual claims and potential evidence beyond datasets collected from social media platforms, such as scientific, financial, and health-related misinformation. This will help us further examine the framework’s robustness and generalizability across different domains and evidence sources. We also plan to extend G-Defense to support multi-modal fake news detection, incorporating images and videos that are increasingly common in real-world misinformation. Another promising direction is to explore post-trained graph-aware LLMs. Recent graph foundation models [33, 60, 69] pretrained on large-scale graph datasets can further improve the structural reasoning of LLMs beyond the format of graph-to-text translation. These models are designed for classical graph tasks and do not directly apply to our setting, where nodes are natural-language sub-claims and edges represent logical dependencies. Nevertheless, adapting graph-aware pretraining to claim-centered graphs may further strengthen G-Defense.

  A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM · 2026 · DOI

• One significant limitation of this work is that we did not in- vestigate the number of views for each post. However, this was not an analytical oversight, but an empirical decision. We found that the view data returned by the TikTok API was unreliable. That is, there were posts with 0 views but more than 0 likes. We researched this discrepancy and found a range of non-conclusive hypotheses. For example, it is pos- sible that TikTok fails to reliably log these characteristics. It is also possible that TikTok manually sets the views of cer- tain types of content or accounts to 0. However, we found no difference in the prevalence of this discrepancy across the three categories. Ethical Implications There may be concerns that collecting posts made by individuals is a violation of privacy. However, the posts we accessed were all publicly available and we do not share any individual’s data or content. For example, we do not publish data related to whether any individual post or content creator was labeled as anti-establishment. Addi- tionally, all annotators were provided with informed consent and an institutional IRB reviewed the study and ruled it to be exempt.

  Anti-Establishment Sentiment on TikTok: Implications for Understanding Influence(rs) and Expertise on Social Media · 2026 · DOI

• This study provided a consistency-based evaluation of SHAP and LIME explanations for fake news detection using machine learning. The primary aim was not just to create a robust fake news classifier, but also to investigate whether two widely used explanation techniques offer consistent and reliable explanations for the same model predictions. This is crucial as fake news detection is a sensitive task and the user requires more than just a predicted label. They also require evidence that is fully trustworthy and understandable in support of the stated prediction. The WELFake dataset, which includes fake and real news samples at the article level was used for the experiment. The final cleaned data set comprised of 63,547 articles. Stratified splitting was used to split the dataset into training, validation and test sets. The machine learning pipeline used was TF-IDF based and multiple models were tested. The final model chosen was a balanced Logistic Regression classifier with 80,000 unigram TF-IDF features and a decision threshold of 0.52 that was optimized on the validation set. The chosen model had a good classification accuracy on the test set. It obtained 96.06% pg. 159 KJMR VOL.03 NO. 05 (2026) CONSISTENCY-BASED EVALUATION OF SHAP AND LIME ……. accuracy, 95.65% F1-score, 99.31% ROC-AUC, 99.18% PR-AUC, and 92.05% MCC. The confusion matrix also revealed a balanced distribution of errors, with 253 fake articles being classified as real and 248 real articles being classified as fake. From these results, it can be concluded that the trained classifier is reliable enough for further explanation analysis. The main contribution of the study is the corrected SHAP-LIME explanation comparison. The implementation aligns both explanation methods to the same predicted class before comparison. This is important because, in binary classification, SHAP values may naturally describe the direction of class 1, while LIME explains the predicted class. Without this correction, the comparison can become unfair and misleading. The study also normalizes explanation features by lowercasing, removing punctuation, normalizing spaces, and merging duplicate feature forms. This makes SHAP and LIME explanations more directly comparable at the word level. The corrected SHAP-LIME explanation comparison is the main contribution of the study. The implementation matches both explanation approaches to the same anticipated class prior to the comparison. This is crucial since in binary classification, SHAP values can naturally describe the direction of class 1, while LIME explains the predicted class. If this correction is not made, the comparison can be completely unfair and misguiding.

  CONSISTENCY-BASED EVALUATION OF SHAP AND LIME EXPLANATIONS FOR MACHINE LEARNING-BASED FAKE NEWS DETECTION · 2026 · DOI

• In order to better understand the true impacts that civic tech has on Taiwanese democratic resilience, future research should consider surveying a wider population in Taiwan about their prior knowledge and usage of civic tech resources, or incorporating quantitative analysis of different indicators of democratic resilience where civic tech is prevalent or absent/relatively unknown.

  Structured Skepticism and Community Trust: How Civic Tech Grassroots Organizers in Taiwan Promote Digital Age Democratic Resilience · 2026 · DOI

• Strengths include a large, nationally representative sample, inclusion of multiple potential correlates, and the use of sensitivity analyses. However, some limitations must be noted. This study cannot distinguish between exposure to misinformation and individual skepticism toward health information. Given the cross- sectional design, reverse causality cannot be ruled out. In addition, the HINTS 2024 survey did not ask participants which social media sites participants used, limiting conclusions about differences across platforms.

  Perceptions of health misinformation on social media: patterns and predictors · 2026 · DOI

• misleading health information, particularly how this ability varies across sociodemographic groups and in different information environments. Given differences across social media platforms, researchers should assess platform-specific variation in perceived misinformation and how trust in health institutions moderates these perceptions. Finally, while interventions such as social media campaigns, educational efforts, and warning labels have been developed (51–53), further research is needed to determine how these strategies influence perceptions of misinformation, particularly when content aligns or misaligns with prior beliefs.

  Perceptions of health misinformation on social media: patterns and predictors · 2026 · DOI

• Most importantly, future work should explore whether epistemic traits can 28 be shifted through targeted educational or civic interventions — and whether doing so reduces disinformation susceptibility across partisan lines. It is a single-country study, and whether our findings gener- alise to other authoritarian contexts remains to be established.

  Belief and Resistance: Vulnerability to Authoritarian Disinformation in Turkey and the Effect of Counter-Strategies · 2026 · DOI

• Study 1. Our study encountered a challenge within the multi-label classification of the SemEval-23 Task 3 Subtask 3 due to the inaccessibility of the test set labels, which hin- dered our ability to perform a detailed error analysis. Fur- thermore, the discrepancy in the average number of persua- sion techniques used in the development and test sets, as highlighted in Table 1 and Figure 2, indicates some incon- sistencies in the data, where persuasion techniques present in the development set did not closely mirror those in the test set, indicating that improvements or decreases in perfor- mance on the development set do not necessarily translate to similar changes in the test set. This limitation highlights the difficulty in calibrating our model’s performance from development to test conditions, given the different intensity of the features in each set. Study 2. The adaptation to a binary classification task in our analysis (due to the different annotation schemes used for the SemEval-23 and APA22 datasets) streamlined model training and evaluation but potentially restricted the granu- larity of our investigation into the depth of persuasive tech- niques in political ads. This meant that the diversity of per- suasive strategies employed across various political cam- paigns might not have been fully observed. This limitation could impact the model ability to generalize across the wider spectrum of political advertising content encountered in dif- ferent contexts. To better understand our model’s behavior and limita- tions, we conducted a comprehensive error analysis that revealed important insights about classification patterns. While our model achieved 81.8% accuracy, the analysis un- covered a systematic bias toward false positives, with neutral sentences containing politeness markers or future-oriented language frequently misclassified as persuasive. Conversely, the model struggled to identify persuasive content in com- plex, multi-clause sentences or those containing interrog- ative structures. These findings, detailed in the Appendix (Figures 6 and 7), highlight the challenges in distinguish- ing between genuine persuasive intent and conventional dis- course patterns, suggesting areas for future refinement in po- litical ads classification models. While our results show strong trends in Figure 4, de- biasing techniques like Prediction-Powered Inference (An- gelopoulos et al. 2023) or Design-Based Supervised Learn- ing (Egami et al. 2024) could further mitigate potential tem- poral biases in classification errors, especially when analyz- ing datasets with less pronounced patterns. Additionally, we experienced some limitations that in- evitably arise when using data from the Meta Ad Library. The analysis was limited to political ads posted on Face- book, which may not fully represent the broader political campaigning strategies employed across different platforms. Our dataset comprised only ads explicitly flagged as po- litical by the entity which posted it8. This introduces po- tential biases, as some political ads might not have been flagged and were thus excluded from our data collection and analysis, while others could have been mis-flagged as political when they were not, which is a phenomenon that has been studied by Sosnovik and Goga (2021). Further- more, Facebook reports only cover ranges of ad spend and reach, rather than precise figures, which can obfuscate the true scale and impact of individual ads. Lastly, the ad buy- ing tool on Facebook allows advertisers to target audiences with a level of specificity that is not captured in the publicly available Ad Library data, concealing critical aspects of ad 8It is legally required for political ads in Australia to have a “paid by” disclaimer 1595 subset of manually annotated instances from the APA22 dataset successfully restored performance, underscoring the importance of domain-specific fine-tuning for achieving optimal performance. By applying the model to APA22 dataset, we gained initial insights into the use of persuasive language and strategies in political advertisements on social media. These include differences in the persuasion intensity and the prevalence of targeted messaging. While this anal- ysis serves as a proof-of-concept, it highlights the practical potential of computational methods for studying persuasive communication in social media, contributing valuable tools for research in political discourse. These advancements not only enhance transparency and deepen our understanding of how persuasive techniques shape public opinion and influ- ence electoral outcomes but also open new avenues for ad- dressing broader societal challenges, such as mitigating ma- nipulation and fostering informed public engagement.

  Towards Detecting Persuasion on Social Media: From Model Development to Insights on Persuasion Strategies · 2026 · DOI

• While we go beyond previous work by assigning multiple annotators per example, budget and annotator availability limited us to only two or three annotators. This is less than ideal for obtaining a representative sample, so future work might want to obtain annotations from additional annota- tors. Nevertheless, we highlight the importance of our novel dataset, given the complexity of the task. Another limitation of our dataset is the substantial costs associated with reproducing it, as the data collection and an- notation required significant financial costs. Also, in compli- ance with X’s terms and conditions, we are only able to re- lease the Twitter IDs rather than the full texts. Consequently, users of the SCRum-9 dataset have to rehydrate the tweets, leading to additional costs. Furthermore, since our dataset is related to misinformation, some tweets may no longer be available due to deletion or account suspension, resulting in potential incompleteness when reconstructing our dataset. Our model evaluation did not take full advantage of the label aggregation methods, to our knowledge, there are no accepted methods for prompting LLMs to predict a distri- bution over labels. Future work ought to determine the ex- tent to which LLMs can predict such distributions, and eval- uate them against the various label aggregations described above. Also, although we analyse the effectiveness of LLM- generated synthetic data, we did not perform data analysis or human analysis to evaluate the multilingual data qual- ity (e.g., lexical diversity or whether the generated replies truly express the stance required in the prompt) mainly due to lack of native speakers in the nine target languages. Al- though our experimental results have demonstrate their ef- fectiveness, future work could explore how it correlates with the characteristics of the generated multilingual data. While our evaluation focused on stance classification, be- cause SCRum-9 links each source tweet to fact-checked claims, it is also possible to perform claim verification, which we did not evaluate here. Future work is required to build and evaluate claim verification models on our dataset. Finally, although we make efforts to cover nine lan- guages, the languages in this study are relatively high- resource languages, and future work should consider estab- lishing datasets for low-resource languages, and benefiting more under-represented communities.

  SCRum-9: Multilingual Stance Classification over Rumours on Social Media · 2026 · DOI

• problems and of 7. Gusenbauer M, Haddaway NR 2020 Which for academic search systems are suitable systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, resources. Research Synthesis and 26 other Methods 11(2): 181–217. 8. Mahl D, Zeng J, Schäfer MS 2021 From “nasa lies” to “reptilian eyes”: mapping communication about 10 conspiracy theories, their communities, and main propagators on Twitter. Social Media + Society 7(2): 1–12. 9. Schatto-Eckrodt T, Boberg S, Wintterlin F, et al. 2020 Use and assessment of sources in conspiracy theorists’ communities. In: Grimme C, Preuss M, Takes FW, et al. (eds) Disinformation in Open Online Media. Cham: Springer International Publishing, pp. 25–32. 10. heocharis Y, Cardenal A, Jin S, et al. 2021 Does the platform matter? Social media and COVID-19 conspiracy theory beliefs in 17 countries. New Media & Society. Epub ahead of print 9 October. 11. van Prooijen J-W, Douglas KM 2017 Conspiracy theories as part of history: the role of societal crisis situations. Memory Studies 10(3): 323–333. 12. Bruns, A., & Burgess, J. 2011. The use of Twitter hashtags in the formation of ad hoc publics. In Proceedings of the 6th European Consortium for Political Research (ECPR) General Conference. 13. Bruns, A., & Burgess, J. 2013. Researching news discussion on Twitter: New methodologies. Journalism Studies, 14(5), 801-814. 14. Colombo, F. 2020. “Bill Gates Created COVID- 19”: A Case Study in Online Conspiracy Theories.

  Discourse Analysis of Digital Conspiracy Theories: Comprehending Information Dissemination · 2026 · DOI

• Conflict of interest Future research could consider longitudinal studies measuring the efficacy of MIL educational interventions for older adults and rural populations in the context of AI; as well as comparative analyses The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

  Media and information literacy in the face of election disinformation generated by artificial intelligence: experiences from Ecuador · 2026 · DOI

• of the Third-Party Although this research covers the majority of ac- credited fact-checking organisations in France, Italy, Spain, Portugal, and Greece, it is crucial to note that the study is limited to the context of Mediterranean Europe and to a specific period, which is subject to ongoing technological change.

  Mapping AI uses and perceptions in fact-checking organisations in Mediterranean Europe · 2026 · DOI

• Future work will focus on expanding the training corpus with larger benchmark datasets such as LIAR and FakeNewsNet, incorporating transformer-based models such as BERT for improved semantic understanding, and adding multi-lingual support for Indian regional language news sources.

  Fake News Detection Using Machine Learning And Natural LanguageProcessing · 2026 · DOI

• The study mentions the need for larger and more diverse datasets for fake news detection but does not specify dataset characteristics such as multilingual coverage, temporal distribution of fake news trends, or domain-specific fake news categories (e.g., political, health, financial) that would improve robustness of machine learning and deep learning models.

  A Comparative Analysis of Machine Learning and Deep Learning Approaches to Enhanced Fake News Detection · 2026 · DOI

• The paper recommends exploring transformer-based models such as BERT and RoBERTa for fake news detection but does not specify the scale of datasets required, the specific configuration parameters, or comparative benchmarking protocols needed to validate these architectures against the machine learning and deep learning baselines already evaluated in the study.

  A Comparative Analysis of Machine Learning and Deep Learning Approaches to Enhanced Fake News Detection · 2026 · DOI

• The paper identifies Large Language Models (LLMs) specifically designed to detect deceptive and nuanced information in fake news detection as a future direction, but does not specify which LLM architectures (beyond general reference to natural language comprehension) should be evaluated or how they would handle domain-specific deception patterns compared to the machine learning and deep learning approaches tested.

  A Comparative Analysis of Machine Learning and Deep Learning Approaches to Enhanced Fake News Detection · 2026 · DOI

Most-cited papers in Misinformation and Its Impacts

• Systematic Literature Review on the Spread of Health-related Misinformation on Social Media · Social Science & Medicine · 2019 · 1,413 citations
• The Implied Truth Effect: Attaching Warnings to a Subset of Fake News Headlines Increases Perceived Accuracy of Headlines Without Warnings · Management Science · 2020 · 521 citations
• Fake news, fast and slow: Deliberation reduces belief in false (but not true) news headlines. · Journal of Experimental Psychology General · 2020 · 393 citations
• ChatGPT is bullshit · Ethics and Information Technology · 2024 · 337 citations
• COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information · International Sociology · 2020 · 325 citations
• Reliance on emotion promotes belief in fake news · Cognitive Research Principles and Implications · 2020 · 270 citations
• Attitudes towards AI: measurement and associations with personality · Scientific Reports · 2024 · 257 citations
• A decade of social bot detection · Communications of the ACM · 2020 · 252 citations
• Durably reducing conspiracy beliefs through dialogues with AI · Science · 2024 · 248 citations
• More human than human: measuring ChatGPT political bias · Public Choice · 2023 · 244 citations

Most recent work

• The Indistinguishability Threshold: Measuring Cognitive Vulnerabilities to AI-Generated Disinformation · 2026
• Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems · 2026
• Examining Algorithmic Curation on Social Media: An Empirical Audit of Reddit’s r/popular Feed · Proceedings of the International AAAI Conference on Web and Social Media · 2026
• Evaluating Organizations in an Age of Absurdity: How Fake News Disrupts the Relationship Between Business and Society · Business & Society · 2026
• “Is This Fake News?” Examining the Antecedents of Generative AI Chatbot Use for Fact-Checking of News Online · Journal of Broadcasting & Electronic Media · 2026
• Normalizing AI: The Evanishing Effect and Rethinking the Machine Heuristic · Human-Machine Communication · 2026
• Navigating the Disagreement Space: A Case Study on Persistent YouTube Users’ Interactions in Immigration-Related Discussions · 2026
• Mitigating Misinformation in Health and Nutrition: Assessing Risk, Harm, and Scalable Solutions · American Journal of Health Promotion · 2026
• RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking · 2026
• Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild · 2026

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