{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T13:45:51Z","timestamp":1793454351341,"version":"4.5.0"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001047","name":"Ian Potter Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Drug and Alcohol Review"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      Alcohol marketing significantly influences consumption patterns, particularly among youth, heavy drinkers and women. Digital platforms have amplified this impact through targeted, immersive campaigns. However, monitoring such marketing remains a challenge due to its opaque and dynamic nature. This study introduces\n                      <jats:italic>SCANNER Alcohol<\/jats:italic>\n                      , an AI\u2010enabled system designed to detect alcohol marketing in online content at the brand level.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      <jats:italic>SCANNER Alcohol<\/jats:italic>\n                      integrates object detection (logos) and optical character recognition (text) to automatically identify 134 alcohol brands within image and video data. The system was trained using annotated datasets of brand logos and validated through standard machine learning metrics. Real\u2010world performance was assessed using social media screen recordings (brand accounts and 12\u2009h of general use), benchmarked against manual coding.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      <jats:italic>SCANNER Alcohol<\/jats:italic>\n                      achieved high algorithmic accuracy, with a mean average precision of 0.94, recall of 0.96 and\n                      <jats:italic>F<\/jats:italic>\n                      1 score of 0.95. In real\u2010world testing, the model demonstrated strong performance, correctly identifying 98.9% of alcohol\u2010branded posts in social media videos.\n                      <jats:italic>SCANNER Alcohol<\/jats:italic>\n                      achieved a low 6.7% false discovery rate, indicating high precision and low noise in the detection output.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusions<\/jats:title>\n                    <jats:p>\n                      <jats:italic>SCANNER Alcohol<\/jats:italic>\n                      is the first system to combine brand\u2010level object and text detection for automated digital alcohol marketing surveillance. Its high accuracy on real\u2010world data and ethical design make it a valuable tool for public health monitoring.\n                      <jats:italic>SCANNER Alcohol<\/jats:italic>\n                      offers a scalable, adaptable and ethically sensitive solution to support regulatory efforts to hold alcohol companies accountable for their digital marketing practices and to ultimately reduce alcohol\u2010related harm.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dar.70095","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T06:31:56Z","timestamp":1766730716000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated Detection of Digital Alcohol Marketing Using\n                    <scp>SCANNER<\/scp>\n                    : An Integrated Deep\u2010Learning 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