{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T12:17:00Z","timestamp":1768306620014,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372418"],"award-info":[{"award-number":["62372418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"University Students\u2019 Science and Technology Association Support Program Project","award":["ZG25004"],"award-info":[{"award-number":["ZG25004"]}]},{"name":"the State Key Laboratory of Media Convergence and Communication, Communication University of China"},{"name":"the Fundamental Research Funds for the Central Universities","award":["CUCZD2503"],"award-info":[{"award-number":["CUCZD2503"]}]},{"name":"Liaoning Collaboration Innovation Center For CSLE"},{"name":"The High-quality and Cutting-edge Disciplines Construction Project for Universities in Beijing"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The containment of misinformation diffusion on social media is a critical challenge in computational social science. However, prevailing intervention strategies predominantly rely on static topological metrics or time-agnostic learning models, thereby overlooking the profound impact of temporal\u2013demographic heterogeneity. This oversight frequently results in a \u201cspatio-temporal mismatch\u201d, where limited intervention resources are misallocated to structurally central but temporarily inactive nodes, particularly during non-stationary propagation bursts driven by exogenous triggers. To bridge this gap, we propose a Spatio-Temporal Deep Reinforcement Learning (ST-DRL) framework for proactive misinformation defense. By seamlessly integrating continuous trigonometric time encoding with demographic-aware Graph Attention Networks, our model explicitly captures the coupling dynamics between group-specific circadian rhythms and event-driven transmission surges. Extensive simulations on heterogeneous networks demonstrate that ST-DRL achieves a Peak Prevalence Reduction of 93.2%, significantly outperforming static heuristics and approaching the theoretical upper bound of oracle-assisted baselines. Crucially, interpretability analysis reveals that the agent autonomously evolves a \u201cPreemptive Strike\u201d strategy\u2014prioritizing the sanitization of high-risk bridge nodes, such as bots, prior to event onsets\u2014thus establishing a new paradigm for predictive rather than reactive network governance.<\/jats:p>","DOI":"10.3390\/info17010067","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:13:01Z","timestamp":1768209181000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Breaking the Spatio-Temporal Mismatch: A Preemptive Deep Reinforcement Learning Framework for Misinformation Defense"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9981-0331","authenticated-orcid":false,"given":"Fulian","family":"Yin","sequence":"first","affiliation":[{"name":"State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China"},{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7250-6667","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8239-4404","authenticated-orcid":false,"given":"Zhenyu","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4745-4312","authenticated-orcid":false,"given":"Chang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1276-5350","authenticated-orcid":false,"given":"Junyi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6678-8377","authenticated-orcid":false,"given":"Yuewei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e57455","DOI":"10.2196\/57455","article-title":"Infodemic Versus Viral Information Spread: Key Differences and Open Challenges","volume":"5","author":"Cinelli","year":"2025","journal-title":"JMIR Infodemiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.bushor.2021.07.012","article-title":"Misinformation, disinformation, and fake news: Cyber risks to business","volume":"64","author":"Petratos","year":"2021","journal-title":"Bus. Horiz."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104375","DOI":"10.1016\/j.ijmedinf.2021.104375","article-title":"Healthcare professionals\u2019 acts of correcting health misinformation on social media","volume":"148","author":"Bautista","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1177\/1065912920938143","article-title":"Disinformation as a threat to deliberative democracy","volume":"74","author":"McKay","year":"2021","journal-title":"Political Res. Q."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"832","DOI":"10.26599\/TST.2021.9010062","article-title":"Efficient algorithms for maximizing group influence in social networks","volume":"27","author":"Huang","year":"2022","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kempe, D., Kleinberg, J., and Tardos, \u00c9. (2003, January 24\u201327). Maximizing the spread of influence through a social network. Proceedings of the KDD03: The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/956750.956769"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"123429","DOI":"10.1016\/j.eswa.2024.123429","article-title":"A survey on influence maximization models","volume":"248","author":"Jaouadi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_8","unstructured":"Plepi, J., Sakketou, F., Geiss, H.-J., and Flek, L. (2022, January 16). Temporal graph analysis of misinformation spreaders in social media. Proceedings of the TextGraphs-16: Graph-Based Methods for Natural Language Processing, Gyeongju, Republic of Korea."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1038\/s41467-018-06930-7","article-title":"The spread of low-credibility content by social bots","volume":"9","author":"Shao","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/s41562-023-01564-2","article-title":"Exposure to untrustworthy websites in the 2020 US election","volume":"7","author":"Moore","year":"2023","journal-title":"Nat. Hum. Behav."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2409329121","DOI":"10.1073\/pnas.2409329121","article-title":"Susceptibility to online misinformation: A systematic meta-analysis of demographic and psychological factors","volume":"121","author":"Sultan","year":"2024","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","first-page":"20000","article-title":"Gcomb: Learning budget-constrained combinatorial algorithms over billion-sized graphs","volume":"33","author":"Manchanda","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_13","unstructured":"Chen, H., Qiu, W., Ou, H.C., An, B., and Tambe, M. (2021). Contingency-aware influence maximization: A reinforcement learning approach. Uncertainty in Artificial Intelligence, PMLR."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110595","DOI":"10.1016\/j.patcog.2024.110595","article-title":"Influence maximization for heterogeneous networks based on self-supervised clustered heterogeneous graph transformer","volume":"154","author":"Li","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e40031","DOI":"10.1016\/j.heliyon.2024.e40031","article-title":"Budget-aware local influence iterative algorithm for efficient influence maximization in social networks","volume":"10","author":"Li","year":"2024","journal-title":"Heliyon"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dai, C., Tang, Q., and Ding, H. (2024, January 17\u201319). TGAT: Temporal graph attention network for blockchain phishing scams detection. Proceedings of the 2024 International Conference on Computer, Information and Telecommunication Systems (CITS), Girona, Spain.","DOI":"10.1109\/CITS61189.2024.10608015"},{"key":"ref_17","first-page":"32257","article-title":"Provably expressive temporal graph networks","volume":"35","author":"Souza","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"143925","DOI":"10.1109\/ACCESS.2023.3343843","article-title":"Topological and sequential neural network model for detecting fake news","volume":"11","author":"Jung","year":"2023","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chang, Y.-T., Hu, Z., Li, X., Yang, S., Jiang, J., and Sun, N. (2024, January 21\u201325). Dihan: A novel dynamic hierarchical graph attention network for fake news detection. Proceedings of the CIKM \u201924: The 33rd ACM International Conference on Information and Knowledge Management, Boise, ID, USA.","DOI":"10.1145\/3627673.3679675"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s00607-024-01395-7","article-title":"Bi-directional temporal graph attention networks for rumor detection in online social networks","volume":"107","author":"Zhou","year":"2025","journal-title":"Computing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s41019-025-00304-y","article-title":"Proactive Rumor Control Using Graph Neural Networks and Evolutionary Optimization","volume":"10","author":"Xu","year":"2025","journal-title":"Data Sci. Eng."},{"key":"ref_22","first-page":"1","article-title":"A survey on influence maximization: From an ml-based combinatorial optimization","volume":"17","author":"Li","year":"2023","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1109\/TCSS.2023.3272331","article-title":"ToupleGDD: A fine-designed solution of influence maximization by deep reinforcement learning","volume":"11","author":"Chen","year":"2023","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, K., Constantinides, M., Quercia, D., and \u0160\u0107epanovi\u0107, S. (2023, January 5\u20138). How circadian rhythms extracted from social media relate to physical activity and sleep. Proceedings of the Seventeenth International AAAI Conference on Web and Social Media, Limassol, Cyprus.","DOI":"10.1609\/icwsm.v17i1.22202"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","author":"Vosoughi","year":"2018","journal-title":"Science"},{"key":"ref_26","first-page":"1","article-title":"Influence maximization with limit cost in social network","volume":"56","author":"Wang","year":"2013","journal-title":"Sci. China Inf. Sci."},{"key":"ref_27","first-page":"8","article-title":"Budgeted reinforcement learning in continuous state space","volume":"32","author":"Carrara","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"955","DOI":"10.5302\/J.ICROS.2020.20.0125","article-title":"Autonomous flying of drone based on PPO reinforcement learning algorithm","volume":"26","author":"Park","year":"2020","journal-title":"J. Inst. Control. Robot. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"114068","DOI":"10.1016\/j.rser.2023.114068","article-title":"Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning","volume":"191","author":"Pang","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hoagland, A. (2025). An ounce of prevention or a pound of cure? The value of health risk information. Rev. Econ. Stat., 1\u201345.","DOI":"10.1162\/rest.a.276"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6080","DOI":"10.1109\/TNNLS.2024.3394161","article-title":"Scalable and effective temporal graph representation learning with hyperbolic geometry","volume":"36","author":"Xu","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_32","first-page":"1","article-title":"A survey of trustworthy representation learning across domains","volume":"18","author":"Zhu","year":"2024","journal-title":"ACM Trans. Knowl. Discov. Data"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/1\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:13:47Z","timestamp":1768281227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/1\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,11]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["info17010067"],"URL":"https:\/\/doi.org\/10.3390\/info17010067","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,11]]}}}