{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:50:05Z","timestamp":1783075805700,"version":"3.54.6"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>The growth of Internet of Things (IoT) devices and social media platforms generates large volumes of multimodal behavioral data that present both great opportunities and challenges to cross-domain behavior prediction. The current models do not deal with time misalignment, distributional drift, and personalization when only a limited amount of data is available. To address these issues, Adaptive Cross-Modal Behavioral Prediction (ACMBP), introduced in this study, is a new framework that merges the streams of IoT activity and social media characteristics via adaptive graph networks. ACMBP combines temporal-semantic offset attention to align activities by the IoT with social actions, drift-conscious dynamic graph rewiring to adapt to changing user relations, hierarchical cross-domain transfer to adapt across different platforms, and few-shot personalization with meta-learning and behavioral prototypes. ACMBP possibility in large-scale heterogeneous digital ecosystems is compared to a fused IoT social dataset (wearable sensors + Twitter) and two public multimodal benchmarks against 10 strong baselines (LSTM, GRU, BERT, GCN, GAT, HAN, GraphSAGE, DCRNN, DeepFM, Multimodal Transformer). ACMBP has been tested on a custom IoT\u2013Social Fusion dataset (1,200 users, 90-day collection, 2.3M sensor records, 850K Twitter posts) and two public benchmarks (PAMAP2, USC-HAD). The results of all improvements are statistically significant (Wilcoxon signed-rank test, p &lt; 0.001). ACMBP attains Behavioral Transition Accuracy (BTA) of 91.4%, Temporal Offset Prediction Error (TOPE) of 1.2 hours, Drift Detection Precision (DDP) of 84.7%, Cross-Platform Transfer Efficiency (CPTE) of 82.3%, and Few-Shot Adaptation only with five samples, which are improvements by 7.3%, 32%, 11.8%, 13.4%, and 50% respectively over the best baselines. Each module\u2019s contribution is supported by ablation studies, and the results of scalability experiments indicate stable performance in large-scale heterogeneous IoT-social environments.<\/jats:p>","DOI":"10.31449\/inf.v50i9.11508","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:15Z","timestamp":1773354075000},"source":"Crossref","is-referenced-by-count":1,"title":["ACMBP: An Adaptive Graph-Based Meta-Learning Framework for Cross-Modal Behavior Prediction from IoT and Social Media Streams"],"prefix":"10.31449","volume":"50","author":[{"given":"Xinzhu","family":"Pu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqi","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,12]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11508\/6545","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11508\/6545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:15Z","timestamp":1773354075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i9.11508","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}