{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T15:05:30Z","timestamp":1763910330817,"version":"build-2065373602"},"reference-count":129,"publisher":"Emerald","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,4]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This paper conducts a systematic literature review (SLR) to explore research directions in fake financial news, addressing a growing concern in today\u2019s digital financial landscape.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>The paper applies the search, appraisal, synthesis, and analysis (SALSA) framework, utilizing nine databases and reviewing 40 studies from 2010\u20132025 (March).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>This study unveils major trends, themes, state-of-the-art detection methods, and theoretical foundations in fake financial news research. It identifies significant variables such as impact, timing, target, and responses, providing a deeper understanding of fake financial news. Moreover, this review highlights that despite advances in large language models (LLMs)- based detection, human-centered factors remain largely overlooked. By extracting empirical and analytical insights from prior research, this study proposes a framework that connects detection methods and behavioral theories to guide future research in human-centered, interdisciplinary approaches for financial misinformation detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Research limitations\/implications<\/jats:title>\n                    <jats:p>The study offers practical take-aways for misinformation detection and amelioration for various financial stakeholders, including investors, firms, and policymakers. It recommends integration of LLM tools for real-time misinformation detection, establishing financial fact-checking platforms, incorporating corporate responses, and increasing financial literacy education. The study also suggests that stronger regulatory interventions by Congress and the SEC are essential to mitigating the threat of financial misinformation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This paper contributes to the field of information management by synthesizing the current knowledge on fake financial news. It is among the frontier systematic reviews, integrating research trends, themes, theoretical foundations, and LLM-based detection advancements beyond prior computational approaches.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/ajim-08-2024-0632","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T01:07:46Z","timestamp":1751332066000},"page":"1223-1254","source":"Crossref","is-referenced-by-count":1,"title":["Investigating fake financial news: a\u00a0systematic review of trends, themes, LLM-based detection methods, and theories"],"prefix":"10.1108","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2683-7191","authenticated-orcid":true,"given":"Mohotarema","family":"Rashid","sequence":"first","affiliation":[{"name":"University of North Texas , , ,","place":["Denton, Texas, USA"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-8180","authenticated-orcid":true,"given":"Lingzi","family":"Hong","sequence":"additional","affiliation":[{"name":"University of North Texas , , ,","place":["Denton, Texas, USA"]}]},{"given":"Sarah","family":"Ryan","sequence":"additional","affiliation":[{"name":"University of North Texas , , ,","place":["Denton, Texas, USA"]}]}],"member":"140","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"2025110321521981700_ref001","first-page":"313","article-title":"Deloitte (drocks) at the financial misinformation detection challenge task: enhancing misinformation detection through instruction-tuned models","author":"Abburi","year":"2025"},{"issue":"5","key":"2025110321521981700_ref002","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1108\/AJIM-01-2021-0034","article-title":"Creation, dissemination and mitigation: toward a disinformation behavior framework and model","volume":"73","author":"Agarwal","year":"2021","journal-title":"Aslib Journal of Information Managment"},{"key":"2025110321521981700_ref003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1057\/s41299-024-00203-4","article-title":"When misleading information hits: how Canadian companies react?","author":"Agbodoh-Falschau","year":"2024","journal-title":"Corporate Reputation Review"},{"issue":"24","key":"2025110321521981700_ref004","doi-asserted-by":"publisher","DOI":"10.3390\/app122412727","article-title":"A systematic literature review on fake news in the COVID-19 pandemic: can AI propose a solution?","volume":"12","author":"Ahmad","year":"2022","journal-title":"Applied Sciences"},{"key":"2025110321521981700_ref005","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2201.08032","article-title":"Combining machine learning with knowledge engineering to detect fake news in social networks-a survey","author":"Ahmed","year":"2022","journal-title":"arXiv"},{"issue":"1","key":"2025110321521981700_ref006","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s13278-023-01028-5","article-title":"Fake news, disinformation and misinformation in social media: a review","volume":"13","author":"A\u00efmeur","year":"2023","journal-title":"Social Network Analysis and Mining"},{"issue":"17","key":"2025110321521981700_ref007","doi-asserted-by":"publisher","first-page":"51009","DOI":"10.1007\/s11042-023-17470-8","article-title":"A comprehensive survey on machine learning approaches for fake news detection","volume":"83","author":"Alghamdi","year":"2023","journal-title":"Multimedia Tools and Applications"},{"issue":"2","key":"2025110321521981700_ref008","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1257\/jep.31.2.211","article-title":"Social media and fake news in the 2016 election","volume":"31","author":"Allcott","year":"2017","journal-title":"The Journal of Economic Perspectives"},{"key":"2025110321521981700_ref009","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconbus.2023.106130","article-title":"Does fake news impact stock returns? 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