Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.

With the age of incoming of self-media, everyone can be the author of the content in the media age of big data. This has caused a mass of fake news appearing in the network. Authors of these fake news will mislead the public by spreading and it will bring economic and social benefits. Existing work focuses on using the various types of features of the article in the hope that a way to accurately identify fake news can be found, but this undermines their universality. In this paper, we propose a pipeline that combines preprocessing, feature extraction and model fusion for a more accurate and automated prediction. Specially we fusion of latent semantic analysis (LSA) and ensemble learning model results using stacking. Experimental analysis of real-world data demonstrates that our pipeline achieves higher accuracy than existing approaches.