{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T13:46:24Z","timestamp":1774014384588,"version":"3.50.1"},"reference-count":41,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.<\/jats:p>","DOI":"10.4018\/ijkdb.2018010102","type":"journal-article","created":{"date-parts":[[2018,3,14]],"date-time":"2018-03-14T13:42:39Z","timestamp":1521034959000},"page":"12-26","source":"Crossref","is-referenced-by-count":50,"title":["Spam Detection on Social Media Using Semantic Convolutional Neural Network"],"prefix":"10.4018","volume":"8","author":[{"given":"Gauri","family":"Jain","sequence":"first","affiliation":[{"name":"Banasthali University, Banasthali, India"}]},{"given":"Manisha","family":"Sharma","sequence":"additional","affiliation":[{"name":"Banasthali University, Banasthali, India"}]},{"given":"Basant","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Swami Keshvanand Institute of Technology (SKIT), Jaipur, India"}]}],"member":"2432","reference":[{"key":"IJKDB.2018010102-0","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2014.977830"},{"key":"IJKDB.2018010102-1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25343-5_5"},{"key":"IJKDB.2018010102-2","first-page":"30","article-title":"Sentiment analysis using common-sense and context information.","author":"B.Agarwal","year":"2015","journal-title":"Computational Intelligence and Neuroscience"},{"key":"IJKDB.2018010102-3","doi-asserted-by":"crossref","unstructured":"Almeida, T. A., & Yamakami, A. (2010, July). Content-based spam filtering. In Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN). IEEE.","DOI":"10.1109\/IJCNN.2010.5596569"},{"key":"IJKDB.2018010102-4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCE.2012.6271302"},{"key":"IJKDB.2018010102-5","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"IJKDB.2018010102-6","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"IJKDB.2018010102-7","doi-asserted-by":"publisher","DOI":"10.1145\/280324.280336"},{"key":"IJKDB.2018010102-8","unstructured":"Dos Santos, C. N., & Gatti, M. (2014, August). Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics (pp. 69-78)."},{"issue":"Jul","key":"IJKDB.2018010102-9","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization.","volume":"12","author":"J.Duchi","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"IJKDB.2018010102-10","unstructured":"Goldberg, Y., & Levy, O. (2014). word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv:1402.3722"},{"key":"IJKDB.2018010102-11","unstructured":"Havasi, C., Speer, R., & Alonso, J. (2007, September). ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In Recent advances in natural language processing (pp. 27-29)."},{"key":"IJKDB.2018010102-12","first-page":"257","article-title":"An assessment of case base reasoning for short text message classification.","author":"M.Healy","year":"2005","journal-title":"Proceedings of 16th Irish Conference on Artificial Intelligence and Cognitive Science, (AICS-05)"},{"key":"IJKDB.2018010102-13","doi-asserted-by":"publisher","DOI":"10.1109\/2.485891"},{"key":"IJKDB.2018010102-14","doi-asserted-by":"crossref","unstructured":"Jain, G., & Sharma, M. (2016a). Social Media: A Review. In Information Systems Design and Intelligent Applications (pp. 387-395). Springer India.","DOI":"10.1007\/978-81-322-2755-7_41"},{"issue":"10","key":"IJKDB.2018010102-15","first-page":"126","article-title":"An Overview of RNN and CNN Techniques for Spam Detection in Social Media","volume":"6","author":"G.Jain","year":"2016","journal-title":"IJARCSSE"},{"key":"IJKDB.2018010102-16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17569-5_56"},{"key":"IJKDB.2018010102-17","doi-asserted-by":"crossref","unstructured":"Kalchbrennern, N., Grefenstette, E., & Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv:1404.2188","DOI":"10.3115\/v1\/P14-1062"},{"key":"IJKDB.2018010102-18","article-title":"Improving static SMS spam detection by using new content-based features.","author":"A.Karami","year":"2014","journal-title":"Proceedings of the 20th Americas Conference on Information systems (AMCIS)"},{"key":"IJKDB.2018010102-19","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"key":"IJKDB.2018010102-20","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks.","author":"A.Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"IJKDB.2018010102-21","doi-asserted-by":"publisher","DOI":"10.1109\/72.554195"},{"key":"IJKDB.2018010102-22","doi-asserted-by":"crossref","unstructured":"Liu, H. & Singh, P. (2004). ConceptNet\u2014a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4), 211-226.","DOI":"10.1023\/B:BTTJ.0000047600.45421.6d"},{"key":"IJKDB.2018010102-23","first-page":"168","article-title":"A New Spam Short Message Classification.","volume":"Vol. 2","author":"D.Longzhen","year":"2009","journal-title":"Proceedings of the First International Workshop on Education Technology and Computer Science"},{"key":"IJKDB.2018010102-24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23496-5_13"},{"key":"IJKDB.2018010102-25","unstructured":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"IJKDB.2018010102-26","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"IJKDB.2018010102-27","doi-asserted-by":"publisher","DOI":"10.1109\/4236.815847"},{"key":"IJKDB.2018010102-28","unstructured":"Rong, X. (2014). word2vec parameter learning explained. arXiv:1411.2738"},{"key":"IJKDB.2018010102-29","unstructured":"Sermanet, P., Chintala, S., & LeCun, Y. (2012, November). Convolutional neural networks applied to house numbers digit classification. In Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR) (pp. 3288-3291). IEEE."},{"key":"IJKDB.2018010102-30","unstructured":"Silva, R. M., Almeida, T. A., & Yamakami, A. (2012). January. Artificial neural networks for content-based web spam detection. In Proceedings on the International Conference on Artificial Intelligence (ICAI). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)."},{"key":"IJKDB.2018010102-31","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"IJKDB.2018010102-32","doi-asserted-by":"crossref","unstructured":"Su, Z., Xu, H., Zhang, D., & Xu, Y. (2014, September). Chinese sentiment classification using a neural network tool\u2014Word2vec. In Proceedings of the 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). IEEE.","DOI":"10.1109\/MFI.2014.6997687"},{"key":"IJKDB.2018010102-33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25207-0_9"},{"key":"IJKDB.2018010102-34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1167"},{"key":"IJKDB.2018010102-35","first-page":"226","article-title":"December. An extensible classifier for semi-structured documents.","author":"M.Tresch","year":"1995","journal-title":"Proceedings of the fourth international conference on Information and knowledge management"},{"key":"IJKDB.2018010102-36","unstructured":"van Kleef, J. (2016). Towards Human-like Performance Face Detection: A Convolutional Neural Network Approach."},{"key":"IJKDB.2018010102-37","unstructured":"Wang, A. H. (2010, July). Don't follow me: Spam detection in twitter. In Proceedings of the 2010 International Conference onSecurity and Cryptography (SECRYPT)."},{"key":"IJKDB.2018010102-38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.09.096"},{"key":"IJKDB.2018010102-39","doi-asserted-by":"publisher","DOI":"10.1145\/1039621.1039625"},{"key":"IJKDB.2018010102-40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.03.015"}],"container-title":["International Journal of Knowledge Discovery in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=202361","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:02:48Z","timestamp":1651795368000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJKDB.2018010102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":41,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.4018\/ijkdb.2018010102","relation":{},"ISSN":["1947-9115","1947-9123"],"issn-type":[{"value":"1947-9115","type":"print"},{"value":"1947-9123","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1]]}}}