{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T18:53:49Z","timestamp":1781636029623,"version":"3.54.5"},"reference-count":142,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"crossref","award":["NIHR202639"],"award-info":[{"award-number":["NIHR202639"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Adverse drug events (ADEs) remain a significant burden to public health and a persistent challenge for pharmacovigilance. The proliferation of patient-generated discourse on social media offers a complementary, real-time signal for ADE surveillance. This article provides a concise yet comprehensive review of recent natural language processing (NLP) research on identifying ADEs in social media text. We systematically reviewed 100 peer-reviewed studies (2017\u20132025) on NLP\/AI for detecting or analysing ADEs in social media. Searches in Google Scholar targeted English-language journal and conference papers; patents and protocols were excluded. Of 130 records screened, 6 were protocols and 24 were excluded because the full text could not be located or the item was a conference abstract lacking methodological detail (i.e., no description of approaches or experiments), yielding a final sample of 100 studies. One reviewer performed screening, with full-text eligibility verified by a second. We extracted objectives, data sources\/languages, preprocessing and annotation practices, datasets, model families, evaluation metrics, and stated limitations. Studies were grouped into five task categories\u2013classification, extraction, normalization, corpus creation, and broader analytical work\u2013with evidence tables summarizing contributions, toolchains, datasets, and performance. Recurrent challenges include noisy\/imbalanced data, multilingual and code-mixed content, and variability in annotation standards. Twitter remains the primary data source: 60% of studies analyse Twitter alone and a further 18% combine Twitter with other platforms (78% in total). English overwhelmingly dominates; only about 5% of studies draw on non-English sources (e.g., French, Chinese, Arabic). Standard pre-processing\u2013URL removal, tokenisation, and lowercasing\u2013is near-universal. Transformer-based models predominate, with BERT and its biomedical or \u201ctweet\u201d variants (e.g., RoBERTa, BioBERT, BERTweet) used in more than 60% of approaches. Persistent obstacles include severe class imbalance and ambiguous or implicit drug-event expressions. Although shared tasks such as SMM4H provide widely used benchmarks, comprehensive annotation guidelines remain uncommon (12% of papers). Recent work increasingly incorporates multimodal inputs and integrates structured biomedical knowledge, yet gaps persist in multilingual coverage, temporal\/longitudinal modelling, and real-world deployment. To our knowledge, this is the first review to synthesise findings from a corpus of 100 peer-reviewed studies on ADE detection in social media using NLP. By organising the literature by task type and tracing methodological trends and limitations, it provides practical guidance for researchers and practitioners. The review also outlines actionable directions for future work, including model explainability, support for low-resource languages, and closer collaboration with regulatory authorities to enable real-world deployment.<\/jats:p>","DOI":"10.1007\/s42979-026-04752-9","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T11:23:39Z","timestamp":1770809019000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Adverse Drug Events in Social Media: A Brief Literature Review"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3858-3999","authenticated-orcid":false,"given":"Imane","family":"Guellil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yousra","family":"Berrachedi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nidhal Eddine","family":"Chenni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massi-Nissa","family":"Abboud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinge","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0213-5668","authenticated-orcid":false,"given":"Honghan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7279-1476","authenticated-orcid":false,"given":"Beatrice","family":"Alex","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"issue":"4","key":"4752_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ. Principal component analysis. Wiley Interdiscip Rev Comput Stat. 2010;2(4):433\u201359.","journal-title":"Wiley Interdiscip Rev Comput Stat"},{"key":"4752_CR2","doi-asserted-by":"crossref","unstructured":"Aji AF, Nityasya MN, Wibowo HA, Prasojo RE, Fatyanosa, T. Bert goes brrr: a venture towards the lesser error in classifying medical self-reporters on twitter. In: Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 58\u201364.","DOI":"10.18653\/v1\/2021.smm4h-1.9"},{"key":"4752_CR3","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.10327","author":"SE Alex","year":"2020","unstructured":"Alex SE, Wong C, Shah A, Reddy P, DeBord L, Dao H Jr. Social media as a surveillance tool for monitoring of isotretinoin adverse effects. Cureus. 2020. https:\/\/doi.org\/10.7759\/cureus.10327.","journal-title":"Cureus"},{"key":"4752_CR4","doi-asserted-by":"publisher","first-page":"55367","DOI":"10.1109\/ACCESS.2024.3389655","volume":"12","author":"MK Alhumayani","year":"2024","unstructured":"Alhumayani MK, Alhazmi HN. Detecting reported side effects of covid-19 vaccines from arabic twitter (x) data. IEEE Access. 2024;12:55367\u201388.","journal-title":"IEEE Access"},{"key":"4752_CR5","doi-asserted-by":"crossref","unstructured":"Alsentzer E, Murphy JR, Boag W, Weng W-H, Jin D, Naumann T, McDermott M. Publicly available clinical bert embeddings; 2019. arXiv preprint arXiv:1904.03323.","DOI":"10.18653\/v1\/W19-1909"},{"issue":"2","key":"4752_CR6","doi-asserted-by":"publisher","DOI":"10.2196\/publichealth.6396","volume":"3","author":"N Alvaro","year":"2017","unstructured":"Alvaro N, Miyao Y, Collier N, et al. Twimed: Twitter and pubmed comparable corpus of drugs, diseases, symptoms, and their relations. JMIR Public Health Surveill. 2017;3(2):e6396.","journal-title":"JMIR Public Health Surveill"},{"key":"4752_CR7","unstructured":"Arnoux-Guenegou A, Girardeau Y, Chen X, Deldossi M, Aboukhamis R, Faviez C, Dahamna B, Karapetiantz P, Guillemin-Lanne S, Texier N, et al. Protocol for evaluating the extraction of adverse drug reactions information in social media, the adr-prism project. JMIR Res Protocols, 2018."},{"key":"4752_CR8","doi-asserted-by":"crossref","unstructured":"Barry P, Uzuner O. Deep learning for identification of adverse effect mentions in twitter data. In: Proceedings of the fourth social media mining for health applications (# SMM4H) Workshop & shared task, 2019; p. 99\u2013101.","DOI":"10.18653\/v1\/W19-3215"},{"key":"4752_CR9","doi-asserted-by":"crossref","unstructured":"Bartal A, Jagodnik KM, Pliskin N, Seidmann A. Utilizing ai and social media analytics to discover adverse side effects of glp-1 receptor agonists, 2024. arXiv preprint arXiv:2404.01358.","DOI":"10.2139\/ssrn.4790676"},{"issue":"1","key":"4752_CR10","doi-asserted-by":"publisher","first-page":"e12","DOI":"10.1016\/S2352-3026(21)00378-1","volume":"9","author":"CL Bennett","year":"2022","unstructured":"Bennett CL, Gundabolu K, Kwak LW, Djulbegovic B, Champigneulle O, Josephson B, et al. Using twitter for the identification of covid-19 vaccine-associated haematological adverse events. Lancet Haematol. 2022;9(1):e12\u20133.","journal-title":"Lancet Haematol"},{"issue":"5","key":"4752_CR11","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1002\/(SICI)1097-0258(20000315)19:5<723::AID-SIM379>3.0.CO;2-A","volume":"19","author":"NJ-M Blackman","year":"2000","unstructured":"Blackman NJ-M, Koval JJ. Interval estimation for cohen\u2019s kappa as a measure of agreement. Stat Med. 2000;19(5):723\u201341.","journal-title":"Stat Med"},{"key":"4752_CR12","doi-asserted-by":"crossref","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T. Enriching word vectors with subword information, 2017.","DOI":"10.1162\/tacl_a_00051"},{"issue":"2","key":"4752_CR13","doi-asserted-by":"publisher","DOI":"10.2196\/publichealth.8214","volume":"4","author":"D Bollegala","year":"2018","unstructured":"Bollegala D, Maskell S, Sloane R, Hajne J, Pirmohamed M, et al. Causality patterns for detecting adverse drug reactions from social media: text mining approach. JMIR Public Health Surveill. 2018;4(2):e8214.","journal-title":"JMIR Public Health Surveill"},{"key":"4752_CR14","doi-asserted-by":"crossref","unstructured":"Bollegala D, Sloane R, Maskell S, Hajne J, Pirmohamed M. Learning causality patterns for detecting adverse drug reactions from social media. J Med Internet Res, 2018.","DOI":"10.2196\/preprints.8214"},{"key":"4752_CR15","unstructured":"Bonial C, Babko-Malaya O, Choi JD, Hwang J, Palmer M. Propbank annotation guidelines. Center for Computational Language and Education Research Institute of Cognitive Science University of Colorado at Boulder, 2010."},{"key":"4752_CR16","doi-asserted-by":"publisher","first-page":"S309","DOI":"10.1016\/j.jval.2018.09.1837","volume":"21","author":"A Booth","year":"2018","unstructured":"Booth A, Halhol S, Merinopoulou E, Oguz M, Pan S, Cox A. Pmu1-frequency of reportable adverse events in health-related social media posts. Value Health. 2018;21:S309.","journal-title":"Value Health"},{"key":"4752_CR17","doi-asserted-by":"publisher","first-page":"S526","DOI":"10.14309\/00000434-201810001-00942","volume":"113","author":"S Chalasani","year":"2018","unstructured":"Chalasani S, Vuppalanchi V, Tilmans L, Petersen K, Weber R, Chalasani N, et al. Novel approach leveraging social media indicates complementary and alternative medicine use highly prevalent and is sometimes associated with serious adverse events in patients with autoimmune hepatitis. Am J Gastroenterol. 2018;113:S526\u2013S526.","journal-title":"Am J Gastroenterol"},{"key":"4752_CR18","doi-asserted-by":"crossref","unstructured":"Cho K, van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha, Qatar: Association for Computational Linguistics. 2014; p. 1724\u20131734.","DOI":"10.3115\/v1\/D14-1179"},{"key":"4752_CR19","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation, 2014. arXiv preprint arXiv:1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"4752_CR20","doi-asserted-by":"crossref","unstructured":"Clemens KS, Faasse K, Tan W, Colagiuri B, Colloca L, Webster R, Vase L, Jason E, Geers A. Social pathways to side-effects: personal contacts and social media predict covid-19 vaccine side-effect expectations and experience. 2022.","DOI":"10.31234\/osf.io\/e2bfv"},{"key":"4752_CR21","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273\u201397.","journal-title":"Mach Learn"},{"issue":"2","key":"4752_CR22","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","volume":"20","author":"DR Cox","year":"1958","unstructured":"Cox DR. The regression analysis of binary sequences. J R Stat Soc Ser B (Methodol). 1958;20(2):215\u201332.","journal-title":"J R Stat Soc Ser B (Methodol)"},{"key":"4752_CR23","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ijmedinf.2019.05.017","volume":"129","author":"H-J Dai","year":"2019","unstructured":"Dai H-J, Wang C-K. Classifying adverse drug reactions from imbalanced twitter data. Int J Med Inform. 2019;129:122\u201332.","journal-title":"Int J Med Inform"},{"key":"4752_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104744","volume":"160","author":"X Dai","year":"2024","unstructured":"Dai X, Karimi S, Sarker A, Hachey B, Paris C. Multiade: A multi-domain benchmark for adverse drug event extraction. J Biomed Inform. 2024;160:104744.","journal-title":"J Biomed Inform"},{"key":"4752_CR25","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.future.2020.08.020","volume":"114","author":"M De Rosa","year":"2021","unstructured":"De Rosa M, Fenza G, Gallo A, Gallo M, Loia V. Pharmacovigilance in the era of social media: discovering adverse drug events cross-relating twitter and pubmed. Futur Gener Comput Syst. 2021;114:394\u2013402.","journal-title":"Futur Gener Comput Syst"},{"key":"4752_CR26","doi-asserted-by":"publisher","DOI":"10.1080\/10543406.2024.2403442","author":"Y Deng","year":"2024","unstructured":"Deng Y, Xing Y, Quach J, Chen X, Wu X, Zhang Y, et al. Developing large language models to detect adverse drug events in posts on x. J Biopharm Stat. 2024. https:\/\/doi.org\/10.1080\/10543406.2024.2403442.","journal-title":"J Biopharm Stat"},{"key":"4752_CR27","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding, 2018. arXiv preprint arXiv:1810.04805."},{"key":"4752_CR28","doi-asserted-by":"crossref","unstructured":"Dey A, Shrivastava JN. Quantum inspired zeroshot classification for adverse drug reactions detection from social media reviews. In: 2024 international conference on artificial intelligence and emerging technology (Global AI Summit), IEEE. 2024;953\u2013958.","DOI":"10.1109\/GlobalAISummit62156.2024.10947885"},{"issue":"2","key":"4752_CR29","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1007\/s42001-024-00276-5","volume":"7","author":"A Dey","year":"2024","unstructured":"Dey A, Shrivastava JN, Kumar C. Classical-quantum hybrid transfer learning for adverse drug reaction detection from social media posts. J Comput Soc Sci. 2024;7(2):1433\u201350.","journal-title":"J Comput Soc Sci"},{"issue":"5","key":"4752_CR30","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s40264-020-00912-9","volume":"43","author":"J Dietrich","year":"2020","unstructured":"Dietrich J, Gattepaille LM, Grum BA, Jiri L, Lerch M, Sartori D, et al. Adverse events in twitter-development of a benchmark reference dataset: results from imi web-radr. Drug Saf. 2020;43(5):467\u201378.","journal-title":"Drug Saf"},{"key":"4752_CR31","doi-asserted-by":"crossref","unstructured":"Dima G-A, Cercel D-C, Dascalu M. Transformer-based multi-task learning for adverse effect mention analysis in tweets. In: Proceedings of the Sixth Social Media Mining for Health (# SMM4H) Workshop and Shared Task, 2021; p. 44\u201351.","DOI":"10.18653\/v1\/2021.smm4h-1.7"},{"key":"4752_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104228","volume":"139","author":"A Dirkson","year":"2023","unstructured":"Dirkson A, Verberne S, Van Oortmerssen G, Gelderblom H, Kraaij W. How do others cope? extracting coping strategies for adverse drug events from social media. J Biomed Inform. 2023;139:104228.","journal-title":"J Biomed Inform"},{"key":"4752_CR33","doi-asserted-by":"publisher","first-page":"1392180","DOI":"10.3389\/fpubh.2024.1392180","volume":"12","author":"F Dong","year":"2024","unstructured":"Dong F, Guo W, Liu J, Patterson TA, Hong H. Bert-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices. Front Public Health. 2024;12:1392180.","journal-title":"Front Public Health"},{"key":"4752_CR34","unstructured":"Duan Z, Wei K, Xue Z, Jin J, Yang S, Zhou J, Ma S, et al. Crowdsourcing-based knowledge graph construction for drug side effects using large language models with an application on semaglutide, 2025. arXiv preprint arXiv:2504.04346."},{"key":"4752_CR35","unstructured":"Duval FV, Silva FABd. Mining in twitter for adverse events from malaria drugs: the case of doxycycline. Cadernos de saude publica, 35;2019"},{"key":"4752_CR36","doi-asserted-by":"crossref","unstructured":"El-karef M, Hassan L. A joint training approach to tweet classification and adverse effect extraction and normalization for smm4h 2021. In: Proceedings of the Sixth Social Media Mining for Health (# SMM4H) Workshop and Shared Task. 2021; p. 91\u201394","DOI":"10.18653\/v1\/2021.smm4h-1.16"},{"key":"4752_CR37","doi-asserted-by":"crossref","unstructured":"Elbiach O, Grissette H, et al. Adverse drug reactions detection from social media: an empirical evaluation of machine learning techniques. In: 2023 14th international conference on intelligent systems: theories and applications (SITA), IEEE. 2023; p. 1\u20137.","DOI":"10.1109\/SITA60746.2023.10373604"},{"key":"4752_CR38","doi-asserted-by":"crossref","unstructured":"Fuentes-Carbajal JA, Montes-y G\u00f3mez M, Villase\u00f1or-Pineda L. Does this tweet report an adverse drug reaction? an enhanced bert-based method to identify drugs side effects in twitter. In: Mexican conference on pattern recognition, Springer. 2022; p. 235\u2013244.","DOI":"10.1007\/978-3-031-07750-0_22"},{"issue":"8","key":"4752_CR39","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s40264-020-00942-3","volume":"43","author":"LM Gattepaille","year":"2020","unstructured":"Gattepaille LM, Hedfors Vidlin S, Bergvall T, Pierce CE, Ellenius J. Prospective evaluation of adverse event recognition systems in twitter: results from the web-radr project. Drug Saf. 2020;43(8):797\u2013808.","journal-title":"Drug Saf"},{"key":"4752_CR40","unstructured":"Ginn R, Pimpalkhute P, Nikfarjam A, Patki A, O\u2019Connor K, Sarker A, Smith K, Gonzalez G. Mining twitter for adverse drug reaction mentions: a corpus and classification benchmark. In: Proceedings of the fourth workshop on building and evaluating resources for health and biomedical text processing, 2014; p. 1\u20138."},{"key":"4752_CR41","doi-asserted-by":"publisher","DOI":"10.2196\/59167","volume":"10","author":"S Golder","year":"2024","unstructured":"Golder S, O\u2019Connor K, Wang Y, Klein A, Gonzalez Hernandez G. The value of social media analysis for adverse events detection and pharmacovigilance: Scoping review. JMIR Public Health Surveill. 2024;10:e59167.","journal-title":"JMIR Public Health Surveill"},{"issue":"8","key":"4752_CR42","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7081","volume":"21","author":"S Golder","year":"2019","unstructured":"Golder S, Scantlebury A, Christmas H, et al. Understanding public attitudes toward researchers using social media for detecting and monitoring adverse events data: multi methods study. J Med Internet Res. 2019;21(8):e7081.","journal-title":"J Med Internet Res"},{"issue":"2","key":"4752_CR43","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1007\/s40264-020-00998-1","volume":"44","author":"S Golder","year":"2021","unstructured":"Golder S, Smith K, O\u2019Connor K, Gross R, Hennessy S, Gonzalez-Hernandez G. A comparative view of reported adverse effects of statins in social media, regulatory data, drug information databases and systematic reviews. Drug Saf. 2021;44(2):167\u201379.","journal-title":"Drug Saf"},{"issue":"4","key":"4752_CR44","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s40264-024-01505-6","volume":"48","author":"S Golder","year":"2025","unstructured":"Golder S, Xu D, O\u2019Connor K, Wang Y, Batra M, Hernandez GG. Leveraging natural language processing and machine learning methods for adverse drug event detection in electronic health\/medical records: a scoping review. Drug Saf. 2025;48(4):321\u201337.","journal-title":"Drug Saf"},{"key":"4752_CR45","doi-asserted-by":"crossref","unstructured":"Guo Y, Ge Y, Al-Garadi MA, Sarker A. Pre-trained transformer-based classification and span detection models for social media health applications. In: Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 52\u201357.","DOI":"10.18653\/v1\/2021.smm4h-1.8"},{"key":"4752_CR46","doi-asserted-by":"crossref","unstructured":"Gupta A, Karpinska M, Zhao W, Krishna K, Merullo J, Yeh L, Iyyer M, O\u2019Connor B. ezcoref: Towards unifying annotation guidelines for coreference resolution. 2022 arXiv preprint arXiv:2210.07188.","DOI":"10.18653\/v1\/2023.findings-eacl.24"},{"key":"4752_CR47","unstructured":"Habibabadi SK, Haghighi PD, Burstein F, Buttery J. Mining vaccine adverse events mentions from social media using twitter as a source. JMIR Med Inform. 2021"},{"issue":"6","key":"4752_CR48","doi-asserted-by":"publisher","DOI":"10.2196\/34305","volume":"10","author":"SK Habibabadi","year":"2022","unstructured":"Habibabadi SK, Haghighi PD, Burstein F, Buttery J, et al. Vaccine adverse event mining of twitter conversations: 2-phase classification study. JMIR Med Inform. 2022;10(6):e34305.","journal-title":"JMIR Med Inform"},{"key":"4752_CR49","unstructured":"He P, Liu X, Gao J, Chen W. Deberta: Decoding-enhanced bert with disentangled attention, 2020. arXiv preprint arXiv:2006.03654."},{"key":"4752_CR50","doi-asserted-by":"crossref","unstructured":"Ho TK. Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, volume\u00a01, IEEE. 1995;278\u2013282.","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"4752_CR51","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.ijmedinf.2018.10.003","volume":"120","author":"T Hoang","year":"2018","unstructured":"Hoang T, Liu J, Pratt N, Zheng VW, Chang KC, Roughead E, et al. Authenticity and credibility aware detection of adverse drug events from social media. Int J Med Inform. 2018;120:157\u201371.","journal-title":"Int J Med Inform"},{"issue":"8","key":"4752_CR52","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"issue":"8","key":"4752_CR53","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci. 1982;79(8):2554\u20138.","journal-title":"Proc Natl Acad Sci"},{"key":"4752_CR54","doi-asserted-by":"crossref","unstructured":"Hsu D, Moh M, Moh T-S, Moh D. Drug side effect frequency mining over a large twitter dataset using apache spark. In: Handbook of artificial intelligence in biomedical engineering. Apple Academic Press; 2021. p. 233\u201359.","DOI":"10.1201\/9781003045564-11"},{"key":"4752_CR55","unstructured":"Indani A, Goulikar D, Nair A, Potare P, More S. Reporting social media-based adverse events with artificial intelligence: elaborating the challenges -mitigating with innovation. 2020."},{"key":"4752_CR56","doi-asserted-by":"crossref","unstructured":"Islam MA, Mukta MSH, Olivier P, Rahman MM. Comprehensive guidelines for emotion annotation. In: Proceedings of the 22nd ACM international conference on intelligent virtual agents, 2022; p. 1\u20138.","DOI":"10.1145\/3514197.3549640"},{"issue":"1","key":"4752_CR57","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s40264-018-0762-z","volume":"42","author":"A Jagannatha","year":"2019","unstructured":"Jagannatha A, Liu F, Liu W, Yu H. Overview of the first natural language processing challenge for extracting medication, indication, and adverse drug events from electronic health record notes (made 1.0). Drug Saf. 2019;42(1):99\u2013111.","journal-title":"Drug Saf"},{"issue":"7","key":"4752_CR58","doi-asserted-by":"publisher","first-page":"85","DOI":"10.15503\/emet2020.85.92","volume":"7","author":"A Jarynowski","year":"2020","unstructured":"Jarynowski A, Kaczmar K, Madej M. Listening to twitter about adverse events of the comirnaty covid-19 vaccine during first weeks of immunisation in poland. E-methodology. 2020;7(7):85\u201392.","journal-title":"E-methodology"},{"key":"4752_CR59","doi-asserted-by":"crossref","unstructured":"Ji Z, Xia T, Han M. Paii-nlp at smm4h 2021: Joint extraction and normalization of adverse drug effect mentions in tweets. In: Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 126\u2013127.","DOI":"10.18653\/v1\/2021.smm4h-1.26"},{"key":"4752_CR60","first-page":"273","volume":"251","author":"K Jiang","year":"2018","unstructured":"Jiang K, Tingyu C, Ricardo A, Bernard GR. Identifying consumer health terms of side effects in twitter posts. Stud Health Technol Inform. 2018;251:273.","journal-title":"Stud Health Technol Inform"},{"key":"4752_CR61","doi-asserted-by":"publisher","DOI":"10.2196\/65031","volume":"27","author":"A Joshi","year":"2025","unstructured":"Joshi A, Kaune DF, Leff P, Fraser E, Lee S, Harrison M, et al. Self-reported side effects associated with selective androgen receptor modulators: social media data analysis. J Med Internet Res. 2025;27:e65031.","journal-title":"J Med Internet Res"},{"key":"4752_CR62","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jbi.2015.03.010","volume":"55","author":"S Karimi","year":"2015","unstructured":"Karimi S, Metke-Jimenez A, Kemp M, Wang C. Cadec: A corpus of adverse drug event annotations. J Biomed Inform. 2015;55:73\u201381.","journal-title":"J Biomed Inform"},{"key":"4752_CR63","doi-asserted-by":"crossref","unstructured":"Kayastha T, Gupta P, Bhattacharyya P. Bert based adverse drug effect tweet classification. In Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 88\u201390.","DOI":"10.18653\/v1\/2021.smm4h-1.15"},{"issue":"1","key":"4752_CR64","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s13278-024-01239-4","volume":"14","author":"MR Keyvanpour","year":"2024","unstructured":"Keyvanpour MR, Pourebrahim B, Mehrmolaei S. Eadr: an ensemble learning method for detecting adverse drug reactions from twitter. Soc Netw Anal Min. 2024;14(1):83.","journal-title":"Soc Netw Anal Min"},{"issue":"2","key":"4752_CR65","doi-asserted-by":"publisher","DOI":"10.2196\/16466","volume":"22","author":"MG Kim","year":"2020","unstructured":"Kim MG, Kim J, Kim SC, Jeong J. Twitter analysis of the nonmedical use and side effects of methylphenidate: machine learning study. J Med Internet Res. 2020;22(2):e16466.","journal-title":"J Med Internet Res"},{"key":"4752_CR66","doi-asserted-by":"crossref","unstructured":"Kim Y. Convolutional neural networks for sentence classification. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Doha, Qatar: Association for Computational Linguisticsp; 2014, p. 1746\u20131751.","DOI":"10.3115\/v1\/D14-1181"},{"key":"4752_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2023.107734","volume":"144","author":"S Kwon","year":"2023","unstructured":"Kwon S, Park A. Examining thematic and emotional differences across twitter, reddit, and youtube: the case of covid-19 vaccine side effects. Comput Hum Behav. 2023;144:107734.","journal-title":"Comput Hum Behav"},{"key":"4752_CR68","doi-asserted-by":"crossref","unstructured":"Laksito AD, Sismoro H, Rahmawati F, Yusa M, et\u00a0al. A comparison study of search strategy on collecting twitter data for drug adverse reaction. In: 2018 international seminar on application for technology of information and communication, IEEE. 2018; p. 356\u2013360.","DOI":"10.1109\/ISEMANTIC.2018.8549746"},{"key":"4752_CR69","unstructured":"Lample G, Conneau A. Cross-lingual language model pretraining. Adv Neural Inf Process Syst(NeurIPS). 2019."},{"key":"4752_CR70","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R 2019 Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942."},{"key":"4752_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103307","volume":"99","author":"A Lavertu","year":"2019","unstructured":"Lavertu A, Altman RB. Redmed: Extending drug lexicons for social media applications. J Biomed Inform. 2019;99:103307.","journal-title":"J Biomed Inform"},{"issue":"10","key":"4752_CR72","doi-asserted-by":"publisher","DOI":"10.2196\/27714","volume":"23","author":"A Lavertu","year":"2021","unstructured":"Lavertu A, Hamamsy T, Altman RB, et al. Quantifying the severity of adverse drug reactions using social media: Network analysis. J Med Internet Res. 2021;23(10):e27714.","journal-title":"J Med Internet Res"},{"key":"4752_CR73","unstructured":"Leaman R, Miller C, Gonzalez G. Enabling recognition of diseases in biomedical text with machine learning: corpus and benchmark. In: Proceedings of the 2009 symposium on languages in biology and medicine. 2009;82, p. 82\u201389."},{"issue":"4","key":"4752_CR74","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234\u201340.","journal-title":"Bioinformatics"},{"issue":"1","key":"4752_CR75","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.vaccine.2021.11.052","volume":"40","author":"M-P Lentzen","year":"2022","unstructured":"Lentzen M-P, Huebenthal V, Kaiser R, Kreppel M, Zoeller JE, Zirk M. A retrospective analysis of social media posts pertaining to covid-19 vaccination side effects. Vaccine. 2022;40(1):43\u201351.","journal-title":"Vaccine"},{"key":"4752_CR76","doi-asserted-by":"publisher","DOI":"10.2196\/63755","volume":"27","author":"W Li","year":"2025","unstructured":"Li W, Hua Y, Zhou P, Zhou L, Xu X, Yang J. Characterizing public sentiments and drug interactions in the covid-19 pandemic using social media: Natural language processing and network analysis. J Med Internet Res. 2025;27:e63755.","journal-title":"J Med Internet Res"},{"issue":"9","key":"4752_CR77","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s40264-020-00943-2","volume":"43","author":"Y Li","year":"2020","unstructured":"Li Y, Jimeno Yepes A, Xiao C. Combining social media and fda adverse event reporting system to detect adverse drug reactions. Drug Saf. 2020;43(9):893\u2013903.","journal-title":"Drug Saf"},{"issue":"1","key":"4752_CR78","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3390\/vaccines10010103","volume":"10","author":"AT Lian","year":"2022","unstructured":"Lian AT, Du J, Tang L. Using a machine learning approach to monitor covid-19 vaccine adverse events (vae) from twitter data. Vaccines. 2022;10(1):103.","journal-title":"Vaccines"},{"key":"4752_CR79","unstructured":"Liu H-C, Nataraj V, Tsai C-T, Liao W-H, Liu T-Y, Jian, MT-J, Day M-Y. Imntpu at the ntcir-17 real-mednlp task: multi-model approach to adverse drug event detection from social media. 2023."},{"issue":"2","key":"4752_CR80","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/MIS.2019.2893158","volume":"34","author":"J Liu","year":"2019","unstructured":"Liu J, Wang G, Chen G. Identifying adverse drug events from social media using an improved semisupervised method. IEEE Intell Syst. 2019;34(2):66\u201374.","journal-title":"IEEE Intell Syst"},{"key":"4752_CR81","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.artmed.2017.10.003","volume":"84","author":"J Liu","year":"2018","unstructured":"Liu J, Zhao S, Wang G. Ssel-ade: a semi-supervised ensemble learning framework for extracting adverse drug events from social media. Artif Intell Med. 2018;84:34\u201349.","journal-title":"Artif Intell Med"},{"key":"4752_CR82","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. Roberta: A robustly optimized bert pretraining approach, 2019. arXiv preprint arXiv:1907.11692."},{"issue":"1","key":"4752_CR83","doi-asserted-by":"publisher","first-page":"4157","DOI":"10.1038\/s41598-025-87724-y","volume":"15","author":"J-H Luo","year":"2025","unstructured":"Luo J-H, Yang A-H. Exploiting question-answer framework with multi-gru to detect adverse drug reaction on social media. Sci Rep. 2025;15(1):4157.","journal-title":"Sci Rep"},{"key":"4752_CR84","doi-asserted-by":"crossref","unstructured":"Lyu T, Eidson A, Jun J, Zhou X, Cui X, Liang C. Data veracity of patients and health consumers reported adverse drug reactions on twitter: key linguistic features, twitter variables, and association rules. medRxiv. 2020.","DOI":"10.1101\/2020.11.03.20225532"},{"key":"4752_CR85","doi-asserted-by":"crossref","unstructured":"MacPhail D, Harbecke D, Raithel L, M\u00f6ller S. Evaluating the robustness of adverse drug event classification models using templates, 2024. arXiv preprint arXiv:2407.02432.","DOI":"10.18653\/v1\/2024.bionlp-1.3"},{"issue":"10","key":"4752_CR86","doi-asserted-by":"publisher","first-page":"2184","DOI":"10.1093\/jamia\/ocab114","volume":"28","author":"A Magge","year":"2021","unstructured":"Magge A, Tutubalina E, Miftahutdinov Z, Alimova I, Dirkson A, Verberne S, et al. Deepademiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on twitter. J Am Med Inform Assoc. 2021;28(10):2184\u201392.","journal-title":"J Am Med Inform Assoc"},{"key":"4752_CR87","doi-asserted-by":"crossref","unstructured":"Mane PS, Patwardhan MS, Divekar AV. Medicinal side-effect analysis using twitter feed. In Progress in intelligent computing techniques: theory, practice, and applications, Springer. 2018; p. 59\u201369.","DOI":"10.1007\/978-981-10-3376-6_7"},{"key":"4752_CR88","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez MJ, Schiaffino SN, Godoy DL, Ponzoni I, Soto AJ. Can pharmacovigilance be performed on social media? mining adverse vaccine reactions from twitter. In: 2024 L Latin American Computer Conference (CLEI), IEEE. 2024; p. 1\u20134.","DOI":"10.1109\/CLEI64178.2024.10700271"},{"key":"4752_CR89","first-page":"41","volume-title":"AAAI-98 workshop on learning for text categorization","author":"A McCallum","year":"1998","unstructured":"McCallum A, Nigam K, et al. A comparison of event models for naive bayes text classification. In: AAAI-98 workshop on learning for text categorization, vol. 752. WI: Madison; 1998. p. 41\u20138."},{"issue":"2","key":"4752_CR90","first-page":"1","volume":"21","author":"MSH Mukta","year":"2021","unstructured":"Mukta MSH, Islam MA, Khan FA, Hossain A, Razik S, Hossain S, et al. A comprehensive guideline for Bengali sentiment annotation. Trans Asian Low-Resour Lang Inf Process. 2021;21(2):1\u201319.","journal-title":"Trans Asian Low-Resour Lang Inf Process"},{"issue":"6","key":"4752_CR91","first-page":"275","volume":"18","author":"AJ Myles","year":"2004","unstructured":"Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. An introduction to decision tree modeling. J Chemom A J Chemom Soc. 2004;18(6):275\u201385.","journal-title":"J Chemom A J Chemom Soc"},{"key":"4752_CR92","doi-asserted-by":"crossref","unstructured":"Nawar N, El-Gayar O, Ambati LS, Bojja GR Social media for exploring adverse drug events associated with multiple sclerosis. In: Proceedings of the 55th Hawaii international conference on system sciences. 2022.","DOI":"10.24251\/HICSS.2022.515"},{"key":"4752_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123572","volume":"249","author":"S Ngamwal","year":"2024","unstructured":"Ngamwal S, Pal V, et al. Sequence labelling with 2 level segregation (sl2ls): a framework to extract covid-19 vaccine adverse drug reactions from twitter data. Expert Syst Appl. 2024;249:123572.","journal-title":"Expert Syst Appl"},{"key":"4752_CR94","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Vu T, Nguyen AT Bertweet: A pre-trained language model for english tweets, 2020. arXiv preprint arXiv:2005.10200.","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"4752_CR95","unstructured":"O\u2019Shea K, Nash R. An introduction to convolutional neural networks, 2015. arXiv preprint arXiv:1511.08458."},{"issue":"1","key":"4752_CR96","doi-asserted-by":"publisher","first-page":"146045822211367","DOI":"10.1177\/14604582221136712","volume":"29","author":"O Oyebode","year":"2023","unstructured":"Oyebode O, Orji R. Identifying adverse drug reactions from patient reviews on social media using natural language processing. Health Inform J. 2023;29(1):14604582221136712.","journal-title":"Health Inform J"},{"issue":"1","key":"4752_CR97","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1162\/0891201053630264","volume":"31","author":"M Palmer","year":"2005","unstructured":"Palmer M, Gildea D, Kingsbury P. The proposition bank: an annotated corpus of semantic roles. Comput Linguist. 2005;31(1):71\u2013106.","journal-title":"Comput Linguist"},{"key":"4752_CR98","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014; p. 1532\u20131543.","DOI":"10.3115\/v1\/D14-1162"},{"key":"4752_CR99","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L. Deep contextualized word representations. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, Volume 1 (Long Papers). New Orleans, Louisiana. Association for Computational Linguistics, 2018; p. 2227\u20132237.","DOI":"10.18653\/v1\/N18-1202"},{"key":"4752_CR100","doi-asserted-by":"crossref","unstructured":"Pimpalkhute V, Nakhate P, Diwan T. Iiitn nlp at smm4h 2021 tasks: Transformer models for classification on health-related imbalanced twitter datasets. In: Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 118\u2013122.","DOI":"10.18653\/v1\/2021.smm4h-1.24"},{"key":"4752_CR101","doi-asserted-by":"crossref","unstructured":"Kanakarajan KR, Kundumani B, Sankarasubbu M. Bioelectra: pretrained biomedical text encoder using discriminators. In: Proceedings of the 20th workshop on biomedical language processing, 2021; p. 143\u2013154.","DOI":"10.18653\/v1\/2021.bionlp-1.16"},{"key":"4752_CR102","doi-asserted-by":"crossref","unstructured":"Rakhsha M, Keyvanpour MR, Shojaedini SV. Detecting adverse drug reactions from social media based on multichannel convolutional neural networks modified by support vector machine. In: 2021 7th international conference on web research (ICWR), IEEE. 2021; p. 48\u201352.","DOI":"10.1109\/ICWR51868.2021.9443128"},{"key":"4752_CR103","doi-asserted-by":"crossref","unstructured":"Ramesh S, Tiwari A, Choubey P, Kashyap S, Khose S, Lakara K, Singh N, Verma U. Bert based transformers lead the way in extraction of health information from social media. In: Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 33\u201338.","DOI":"10.18653\/v1\/2021.smm4h-1.5"},{"key":"4752_CR104","doi-asserted-by":"crossref","unstructured":"Remy F, Scaboro S, Portelli B. Boosting adverse drug event normalization on social media: general-purpose model initialization and biomedical semantic text similarity benefit zero-shot linking in informal contexts, 2023. arXiv preprint arXiv:2308.00157.","DOI":"10.18653\/v1\/2023.socialnlp-1.6"},{"key":"4752_CR105","doi-asserted-by":"crossref","unstructured":"Ribeiro LA, Cinalli D, Garcia ACB. Discovering adverse drug reactions from twitter: a sentiment analysis perspective. In: 2021 IEEE 24th international conference on computer supported cooperative work in design (CSCWD), IEEE, 2021; p. 1172\u20131177.","DOI":"10.1109\/CSCWD49262.2021.9437783"},{"key":"4752_CR106","unstructured":"Ruder S. An overview of gradient descent optimization algorithms, 2016 . arXiv preprint arXiv:1609.04747."},{"issue":"3","key":"4752_CR107","doi-asserted-by":"publisher","DOI":"10.2196\/26589","volume":"8","author":"K Saha","year":"2021","unstructured":"Saha K, Torous J, Kiciman E, De Choudhury M, et al. Understanding side effects of antidepressants: large-scale longitudinal study on social media data. JMIR Mental Health. 2021;8(3):e26589.","journal-title":"JMIR Mental Health"},{"key":"4752_CR108","doi-asserted-by":"crossref","unstructured":"Sahoo P, Singh AK, Saha S, Chadha A, Mondal S. Enhancing adverse drug event detection with multimodal dataset: Corpus creation and model development, 2024. arXiv preprint arXiv:2405.15766.","DOI":"10.18653\/v1\/2024.findings-acl.667"},{"key":"4752_CR109","doi-asserted-by":"crossref","unstructured":"Sakhovskiy A, Miftahutdinov Z, Tutubalina, E Kfu nlp team at smm4h 2021 tasks: Cross-lingual and cross-modal bert-based models for adverse drug effects. In: Proceedings of the sixth social media mining for health (# SMM4H) workshop and shared task, 2021; p. 39\u201343.","DOI":"10.18653\/v1\/2021.smm4h-1.6"},{"issue":"5","key":"4752_CR110","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s40290-022-00441-z","volume":"36","author":"M Salas","year":"2022","unstructured":"Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature. Pharm Med. 2022;36(5):295\u2013306.","journal-title":"Pharm Med"},{"key":"4752_CR111","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T, Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2019. arXiv preprint arXiv:1910.01108."},{"issue":"Suppl 4","key":"4752_CR112","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1186\/s12911-023-02374-2","volume":"23","author":"M Sankaranarayanapillai","year":"2024","unstructured":"Sankaranarayanapillai M, Wang S, Ji H, Song H-Y, Tao C. Lessons learned from annotation of vaers reports on adverse events following influenza vaccination and related to guillain-barr\u00e9 syndrome. BMC Med Inform Decis Mak. 2024;23(Suppl 4):298.","journal-title":"BMC Med Inform Decis Mak"},{"key":"4752_CR113","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107324","volume":"106","author":"C Shen","year":"2021","unstructured":"Shen C, Li Z, Chu Y, Zhao Z. Gar: Graph adversarial representation for adverse drug event detection on twitter. Appl Soft Comput. 2021;106:107324.","journal-title":"Appl Soft Comput"},{"issue":"9","key":"4752_CR114","doi-asserted-by":"publisher","first-page":"4799","DOI":"10.1007\/s00521-018-3722-8","volume":"31","author":"C Shen","year":"2019","unstructured":"Shen C, Lin H, Guo K, Xu K, Yang Z, Wang J. Detecting adverse drug reactions from social media based on multi-channel convolutional neural networks. Neural Comput Appl. 2019;31(9):4799\u2013808.","journal-title":"Neural Comput Appl"},{"key":"4752_CR115","doi-asserted-by":"crossref","unstructured":"Shen C, Lin H, Li Z, Chu Y, Li Z, Yang Z. A graph-boosted framework for adverse drug event detection on twitter. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, 2020; p. 1129\u20131131.","DOI":"10.1109\/BIBM49941.2020.9313352"},{"issue":"11","key":"4752_CR116","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1002\/jac5.2035","volume":"7","author":"J Singleton","year":"2024","unstructured":"Singleton J, Wantuch GA. Evaluation of tiktok social media posts on side effect information for popular weight loss medications. J Am Coll Clin Pharm. 2024;7(11):1077\u201383.","journal-title":"J Am Coll Clin Pharm"},{"issue":"12","key":"4752_CR117","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1007\/s40264-018-0707-6","volume":"41","author":"K Smith","year":"2018","unstructured":"Smith K, Golder S, Sarker A, Loke Y, O\u2019Connor K, Gonzalez-Hernandez G. Methods to compare adverse events in twitter to faers, drug information databases, and systematic reviews: proof of concept with adalimumab. Drug Saf. 2018;41(12):1397\u2013410.","journal-title":"Drug Saf"},{"issue":"26","key":"4752_CR118","doi-asserted-by":"publisher","first-page":"67779","DOI":"10.1007\/s11042-024-18144-9","volume":"83","author":"S Spandana","year":"2024","unstructured":"Spandana S, Prakash RV. Multiple features-based adverse drug reaction detection from social media using deep convolutional neural networks (dcnn). Multimed Tools Appl. 2024;83(26):67779\u201393.","journal-title":"Multimed Tools Appl"},{"key":"4752_CR119","unstructured":"Suadaa LH, Wahyuddin EP, Ridho F. Stis at the ntcir-17 mednlp-sc task: Incorporating sentiment to transformer architecture for adverse drug event detection on social media. 2023."},{"issue":"40","key":"4752_CR120","doi-asserted-by":"publisher","first-page":"5949","DOI":"10.1016\/j.vaccine.2018.08.064","volume":"36","author":"TA Suragh","year":"2018","unstructured":"Suragh TA, Lamprianou S, MacDonald NE, Loharikar AR, Balakrishnan MR, Benes O, et al. Cluster anxiety-related adverse events following immunization (aefi): an assessment of reports detected in social media and those identified using an online search engine. Vaccine. 2018;36(40):5949\u201354.","journal-title":"Vaccine"},{"key":"4752_CR121","doi-asserted-by":"publisher","first-page":"2379208","DOI":"10.1155\/2018\/2379208","volume":"2018","author":"B Tang","year":"2018","unstructured":"Tang B, Hu J, Wang X, Chen Q. Recognizing continuous and discontinuous adverse drug reaction mentions from social media using lstm-crf. Wirel Commun Mobile Comput. 2018;2018:2379208.","journal-title":"Wirel Commun Mobile Comput"},{"issue":"1","key":"4752_CR122","first-page":"42","volume":"10","author":"K Tonev","year":"2018","unstructured":"Tonev K, Grigorov E, Belcheva V, Getov I, et al. The social media and discussion forums as a source of information on adverse drug reactions. Bulg J Public Health. 2018;10(1):42\u201352.","journal-title":"Bulg J Public Health"},{"key":"4752_CR123","doi-asserted-by":"crossref","unstructured":"van Hunsel F, Younus MM, Cox AR. Patient and public involvement in pharmacovigilance. In: Principles and practice of pharmacovigilance and drug safety. Springer. 2024; p. 273\u2013293.","DOI":"10.1007\/978-3-031-51089-2_12"},{"key":"4752_CR124","doi-asserted-by":"crossref","unstructured":"Wahbeh A, Nasralah T, El-Gayar O, Al-Ramahi MA, El\u00a0Noshokaty A. Adverse health effects of kratom: an analysis of social media data. 2021.","DOI":"10.24251\/HICSS.2021.477"},{"issue":"1","key":"4752_CR125","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13326-018-0184-y","volume":"9","author":"J Wang","year":"2018","unstructured":"Wang J, Zhao L, Ye Y, Zhang Y. Adverse event detection by integrating twitter data and vaers. J Biomed Seman. 2018;9(1):1\u201310.","journal-title":"J Biomed Seman"},{"key":"4752_CR126","doi-asserted-by":"crossref","unstructured":"Wang X, Wang X, Zhang S. Adverse reaction detection from social media based on quantum bi-lstm with attention. IEEE Access. 2022.","DOI":"10.1109\/ACCESS.2022.3151900"},{"issue":"4","key":"4752_CR127","doi-asserted-by":"publisher","first-page":"ooab081","DOI":"10.1093\/jamiaopen\/ooab081","volume":"4","author":"Y Wang","year":"2021","unstructured":"Wang Y, Zhao Y, Schutte D, Bian J, Zhang R. Deep learning models in detection of dietary supplement adverse event signals from twitter. JAMIA Open. 2021;4(4):ooab081.","journal-title":"JAMIA Open"},{"issue":"3","key":"4752_CR128","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000468","volume":"4","author":"P Wegner","year":"2025","unstructured":"Wegner P, Fr\u00f6hlich H, Madan S. Evaluating knowledge fusion models on detecting adverse drug events in text. PLOS Digit Health. 2025;4(3):e0000468.","journal-title":"PLOS Digit Health"},{"key":"4752_CR129","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, et\u00a0al. Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, 2020; p. 38\u201345.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"issue":"6","key":"4752_CR130","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s40264-025-01520-1","volume":"48","author":"L Wu","year":"2025","unstructured":"Wu L, Fang H, Qu Y, Xu J, Tong W. Leveraging fda labeling documents and large language model to enhance annotation, profiling, and classification of drug adverse events with askfdalabel. Drug Saf. 2025;48(6):655.","journal-title":"Drug Saf"},{"issue":"1","key":"4752_CR131","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1093\/jamia\/ocx045","volume":"25","author":"J Xie","year":"2018","unstructured":"Xie J, Liu X, Dajun Zeng D. Mining e-cigarette adverse events in social media using bi-lstm recurrent neural network with word embedding representation. J Am Med Inform Assoc. 2018;25(1):72\u201380.","journal-title":"J Am Med Inform Assoc"},{"issue":"11","key":"4752_CR132","first-page":"279","volume":"11","author":"S Yadav","year":"2021","unstructured":"Yadav S, Alam A, Patnaik R. Identification and assessment of adverse events using smart social media platforms. Rev Int Geogr Educ Online. 2021;11(11):279\u201392.","journal-title":"Rev Int Geogr Educ Online"},{"key":"4752_CR133","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV. Xlnet: Generalized autoregressive pretraining for language understanding. Adv Neural Inf Process Syst. 2019;32."},{"key":"4752_CR134","doi-asserted-by":"crossref","unstructured":"Yaseen U, Langer S. Neural text classification and stacked heterogeneous embeddings for named entity recognition in smm4h, 2021. arXiv preprint arXiv:2106.05823.","DOI":"10.18653\/v1\/2021.smm4h-1.14"},{"key":"4752_CR135","doi-asserted-by":"crossref","unstructured":"Yazdani A, Rouhizadeh H, Bornet A, Teodoro D. Conorm: Context-aware entity normalization for adverse drug event detection. medRxiv, 2023;2023\u201309.","DOI":"10.1101\/2023.09.26.23296150"},{"issue":"5","key":"4752_CR136","doi-asserted-by":"publisher","first-page":"6487","DOI":"10.1109\/TCSS.2024.3392341","volume":"11","author":"S Yun","year":"2024","unstructured":"Yun S, Jeong J, Kim J. Covid-19 vaccine side effect analysis by leveraging social media: Focusing on connectivity and cluster characteristics of vaccine side effects. IEEE Trans Comput Soc Syst. 2024;11(5):6487\u2013500.","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"4","key":"4752_CR137","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1007\/s10618-018-00610-2","volume":"33","author":"M Zhang","year":"2019","unstructured":"Zhang M, Zhang M, Ge C, Liu Q, Wang J, Wei J, et al. Automatic discovery of adverse reactions through chinese social media. Data Min Knowl Disc. 2019;33(4):848\u201370.","journal-title":"Data Min Knowl Disc"},{"key":"4752_CR138","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.neucom.2021.05.007","volume":"453","author":"T Zhang","year":"2021","unstructured":"Zhang T, Lin H, Ren Y, Yang Z, Wang J, Duan X, et al. Identifying adverse drug reaction entities from social media with adversarial transfer learning model. Neurocomputing. 2021;453:254\u201362.","journal-title":"Neurocomputing"},{"key":"4752_CR139","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103437","volume":"106","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Cui S, Gao H. Adverse drug reaction detection on social media with deep linguistic features. J Biomed Inform. 2020;106:103437.","journal-title":"J Biomed Inform"},{"key":"4752_CR140","doi-asserted-by":"crossref","unstructured":"Zheng Y, Gong J, Ren S, Simancek D, Vydiswaran VV. Lhs712_adenotgood at# smm4h 2024 task 1: Deep-llmademiner: A deep learning and llm pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on twitter. In: Proceedings of the 9th social media mining for health research and applications (SMM4H 2024) workshop and shared tasks, 2024; p. 130\u2013132.","DOI":"10.18653\/v1\/2024.smm4h-1.30"},{"key":"4752_CR141","doi-asserted-by":"crossref","unstructured":"Zhou T, Li Z, Gan Z, Zhang B, Chen Y, Niu K, Wan J, Liu K, Zhao J, Shi Y, et\u00a0al. Classification, extraction, and normalization: Casia_unisound team at the social media mining for health 2021 shared tasks. In: Proceedings of the sixth social media mining for health (# SMM4H) Workshop and Shared Task, 2021; p. 77\u201382.","DOI":"10.18653\/v1\/2021.smm4h-1.13"},{"issue":"3","key":"4752_CR142","doi-asserted-by":"publisher","DOI":"10.2196\/19266","volume":"6","author":"Z Zhou","year":"2020","unstructured":"Zhou Z, Hultgren KE, et al. Complementing the us food and drug administration adverse event reporting system with adverse drug reaction reporting from social media: Comparative analysis. JMIR Public Health Surveill. 2020;6(3):e19266.","journal-title":"JMIR Public Health Surveill"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04752-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-026-04752-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04752-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T18:06:28Z","timestamp":1781633188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-026-04752-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,11]]},"references-count":142,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["4752"],"URL":"https:\/\/doi.org\/10.1007\/s42979-026-04752-9","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,11]]},"assertion":[{"value":"7 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"N\/A","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}},{"value":"N\/A","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"199"}}