{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:50:27Z","timestamp":1760784627893,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T00:00:00Z","timestamp":1551657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The rapid development of online social networks has allowed users to obtain information, communicate with each other and express different opinions. Generally, in the same social network, users tend to be influenced by each other and have similar views. However, on another social network, users may have opposite views on the same event. Therefore, research undertaken on a single social network is unable to meet the needs of research on hot topic community discovery. \u201cCross social network\u201d refers to multiple social networks. The integration of information from multiple social network platforms forms a new unified dataset. In the dataset, information from different platforms for the same event may contain similar or unique topics. This paper proposes a hot topic discovery method on cross social networks. Firstly, text data from different social networks are fused to build a unified model. Then, we obtain latent topic distributions from the unified model using the Labeled Biterm Latent Dirichlet Allocation (LB-LDA) model. Based on the distributions, similar topics are clustered to form several topic communities. Finally, we choose hot topic communities based on their scores. Experiment result on data from three social networks prove that our model is effective and has certain application value.<\/jats:p>","DOI":"10.3390\/fi11030060","type":"journal-article","created":{"date-parts":[[2019,3,4]],"date-time":"2019-03-04T05:22:26Z","timestamp":1551676946000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hot Topic Community Discovery on Cross Social Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Xuan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Bofeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"}]},{"given":"Furong","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"School of Computer Science and Technology, Kashgar University, Kashgar 844006, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Skeels, M.M., and Grudin, J. (2009, January 10\u201313). When social networks cross boundaries: A case study of workplace use of facebook and linkedin. Proceedings of the ACM 2009 International Conference on Supporting Group Work, Sanibel Island, FL, USA.","DOI":"10.1145\/1531674.1531689"},{"key":"ref_2","first-page":"23","article-title":"A Comparison of Information Seeking Using Search Engines and Social Networks","volume":"10","author":"Morris","year":"2010","journal-title":"ICWSM"},{"key":"ref_3","unstructured":"Dale, S., and Brown, N. (2013). Cross Social Network Data Aggregation. (8,429,277), US Patent."},{"key":"ref_4","unstructured":"Farseev, A., Kotkov, D., Semenov, A., Veijalainen, J., and Chua, T.S. (July, January 28). Cross-social network collaborative recommendation. Proceedings of the ACM Web Science Conference, Oxford, UK."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tian, Y., Yuan, J., and Yu, S. (2016, January 17\u201319). SBPA: Social behavior based cross Social Network phishing attacks. Proceedings of the 2016 IEEE Conference on Communications and Network Security (CNS), Philadelphia, PA, USA.","DOI":"10.1109\/CNS.2016.7860514"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shu, K., Wang, S., Tang, J., Wang, Y., and Liu, H. (2018, January 5\u20139). Crossfire: Cross media joint friend and item recommendations. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Los Angeles, CA, USA.","DOI":"10.1145\/3159652.3159692"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9","article-title":"Indexing by latent semantic analysis","volume":"41","author":"Deerwester","year":"1990","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hofmann, T. (1999). Probabilistic latent semantic analysis. Artificial Intelligence, Proceedings of the Fifteenth conference on Uncertainty, Stockholm, Sweden, 30 July\u20131 August 1999, Morgan Kaufmann Publishers Inc.","DOI":"10.1145\/312624.312649"},{"key":"ref_9","first-page":"993","article-title":"Latent dirichlet allocation","volume":"138","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ramage, D., Hall, D., Nallapati, R., and Manning, C.D. (2009, January 6\u20137). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore.","DOI":"10.3115\/1699510.1699543"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Titov, I., and McDonald, R. (2008, January 21\u201325). Modeling online reviews with multi-grain topic models. Proceedings of the 17th International Conference on World Wide Web, Beijing, China.","DOI":"10.1145\/1367497.1367513"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, H., Yin, H., Li, X., Wang, M., Chen, W., and Chen, T. (2017, January 3\u20137). People opinion topic model: Opinion based user clustering in social networks. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3051159"},{"key":"ref_13","first-page":"1427","article-title":"Topic Tracking Model for Analyzing Consumer Purchase Behavior","volume":"9","author":"Iwata","year":"2009","journal-title":"IJCAI"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kurashima, T., Iwata, T., Hoshide, T., Takaya, N., and Fujimura, K. (2013, January 4\u20138). Geo topic model: Joint modeling of user\u2019s activity area and interests for location recommendation. Proceedings of the Sixth ACM international Conference on Web Search and Data Mining, New York City, NY, USA.","DOI":"10.1145\/2433396.2433444"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chemudugunta, C., Smyth, P., and Steyvers, M. (2007). Modeling general and specific aspects of documents with a probabilistic topic model. Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 4\u20137 December 2006, The MIT Press.","DOI":"10.7551\/mitpress\/7503.003.0035"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lin, C., and He, Y. (2009, January 2\u20136). Joint sentiment\/topicmodel for sentiment analysis. Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong, China.","DOI":"10.1145\/1645953.1646003"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, S., Chen, Z., and Liu, B. (2016, January 11\u201315). Mining aspect-specific opinion using a holistic lifelong topic model. Proceedings of the 25th International Conference on World Wide Web, Montreal, QC, Canada.","DOI":"10.1145\/2872427.2883086"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2928","DOI":"10.1109\/TKDE.2014.2313872","article-title":"Btm: Topic modeling over short texts","volume":"26","author":"Cheng","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, X., McCallum, A., and Wei, X. (2007, January 28\u201331). Topical n-grams: Phrase and topic discovery, with an application to information retrieval. Proceedings of the Seventh IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, USA.","DOI":"10.1109\/ICDM.2007.86"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vaca, C.K., Mantrach, A., Jaimes, A., and Saerens, M. (2014, January 7\u201311). A time-based collective factorization for topic discovery and monitoring in news. Proceedings of the 23rd international conference on World Wide Web, Seoul, Korea.","DOI":"10.1145\/2566486.2568041"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, J., and Ma, X. (2018). Research on hot news discovery model based on user interest and topic discovery. Cluster Computing, Springer.","DOI":"10.1007\/s10586-018-1880-1"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Z.H., Hu, G.L., Zhou, T.H., and Wang, L. (2018, January 26\u201328). TDT_CC: A Hot Topic Detection and Tracking Algorithm Based on Chain of Causes. Proceedings of the International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Sendai, Japan.","DOI":"10.1007\/978-3-030-03745-1_4"},{"key":"ref_23","unstructured":"Torgerson, W.S. (1958). Theory and Methods of Scaling, Wiley."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, H., Zhang, Z., Sun, A., and Ma, Z. (2016, January 17\u201321). Topic modeling for short texts with auxiliary word embeddings. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa, Italy.","DOI":"10.1145\/2911451.2911499"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1162\/tacl_a_00140","article-title":"Improving topic models with latent feature word representations","volume":"3","author":"Nguyen","year":"2015","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, T., Kang, K., Choo, J., and Reddy, C.K. (2018, January 23\u201327). Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations. Proceedings of the 2018 World Wide Web Conference on World Wide Web, Lyon, France.","DOI":"10.1145\/3178876.3186009"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:36:04Z","timestamp":1760186164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,4]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["fi11030060"],"URL":"https:\/\/doi.org\/10.3390\/fi11030060","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2019,3,4]]}}}