{"doi":"10.1037/abn0000855","title":"Capturing mood dynamics through adolescent smartphone social communication.","abstract":"= 16.49, 73.5% female) with a wide range of depression severity. Participants completed daily mood ratings across a 90-day period, during which 354,278 messages were passively collected from social communication apps. Greater positive sentiment (i.e., more positive weighted composite valence score and a greater proportion of words expressing positive sentiment) predicted more positive next-day mood, controlling for previous-day mood. Moreover, greater proportions of positive and negative sentiment were, respectively, associated with lower anhedonia and greater dysphoria symptoms measured at baseline. Exploratory analyses of nonaffective linguistic features showed that greater use of social engagement words (e.g., friends and affiliation) and emojis (primarily consisting of hearts) predicted more positive changes in mood. Collectively, findings suggest that language from smartphone social communication can detect mood fluctuations in adolescents, laying the foundation for language-based tools to identify periods of heightened depression risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).","journal":"Journal of Psychopathology and Clinical Science","year":2023,"id":341410,"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":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6879,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1049569,"name":"Esha Trivedi","orcid":"0009-0003-5658-5546","position":1,"is_corresponding":false},{"id":1050146,"name":"Fiona Helgren","orcid":null,"position":2,"is_corresponding":false},{"id":720410,"name":"Grace O. Allison","orcid":"0000-0003-3796-9352","position":3,"is_corresponding":false},{"id":106492,"name":"Emily Zhang","orcid":"0000-0002-8809-8473","position":4,"is_corresponding":false},{"id":1050150,"name":"Savannah N. Buchanan","orcid":null,"position":5,"is_corresponding":false},{"id":421973,"name":"David Pagliaccio","orcid":"0000-0002-1214-1965","position":6,"is_corresponding":false},{"id":368088,"name":"Katherine Durham","orcid":null,"position":7,"is_corresponding":false},{"id":393230,"name":"Nicholas B. Allen","orcid":"0000-0002-1086-6639","position":8,"is_corresponding":false},{"id":367040,"name":"Randy P. Auerbach","orcid":"0000-0003-2319-4744","position":9,"is_corresponding":false},{"id":246812,"name":"Stewart A. Shankman","orcid":"0000-0002-8097-4483","position":10,"is_corresponding":false},{"id":1032015,"name":"Lilian Y. Li","orcid":"0000-0001-7507-3632","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T01:11:03.393978Z","pmid":"37498714","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":[]}