{"doi":"10.1101/2024.06.11.24308776","title":"LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, out-performing the benchmark classifier by 1.18%.</jats:p>","journal":null,"year":null,"id":633713,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1643179,"name":"Thushari Atapattu","orcid":null,"position":1,"is_corresponding":false},{"id":1643181,"name":"Menasha Thilakaratne","orcid":null,"position":2,"is_corresponding":false},{"id":1643182,"name":"Katrina Falkner","orcid":null,"position":3,"is_corresponding":false},{"id":1643177,"name":"Dasun Athukoralage","orcid":"0009-0008-3950-6398","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"LT4SG@SMM4H’24: Tweets Classification for Digital Epidemiology of Childhood Health Outcomes Using Pre-Trained Language Models","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, out-performing the benchmark classifier by 1.18%.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4399576982","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"cc-by-nc","oa_locations":[{"url":"https://www.medrxiv.org/content/medrxiv/early/2024/06/12/2024.06.11.24308776.full.pdf","host_type":"repository"},{"url":"https://www.medrxiv.org/content/medrxiv/early/2024/06/12/2024.06.11.24308776.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2024.06.11.24308776","host_type":"publisher"},{"url":"http://dx.doi.org/10.1101/2024.06.11.24308776","host_type":"repository"},{"url":"https://doi.org/10.1101/2024.06.11.24308776","host_type":"Unpaywall"}],"fields_of_study":["Health Literacy and Information Accessibility","Child and Adolescent Health","Digital Mental Health Interventions"],"mesh_terms":[],"keywords":["Epidemiology","Computer science","Natural language processing","Artificial intelligence","Data science","Medicine","Pathology"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T12:32:26.868416Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}