An Improved Model for Spam User Identification
Aiming at the problem of spam user identification in microblog, this paper proposes an improved model based on representation learning and C4.5 decision tree. The spam user identification rule has been decided at first. Then, the ten fold cross validation and training set are combined to train the model. The experimental results show that the improved model proposed in this paper is superior to the traditional model not only in accuracy but also universality. Results of this paper may provide a guidance to the improvement of framework of a certain social network platform. Introduction Nowadays, the rapid development of internet opens a new world for the users to make social contact online. However, popularity of microblog is accompanied by the problem of spam users. The existence of them does harm to the public network environment greatly. Therefore, how to effectively identify and detect spam users has become a research focus [1] Lots of effort has been devoted to overcoming the problem. An anti spam filter for E mail spam detection was developed in [2] to solve the problem in predicting the labels of emails in a personalized mailbox. In [3] a semi supervised spam detection framework for spam detection was proposed. It can adaptively learns patterns of new spam activities and maintain good accuracy for spam detection at the same time. However, they take a lot of time and labor cost. In this work, an improved model is proposed to solve the problem mentioned above. The representation learning has been used to replace the artificial feature selection in traditional model to enhance the efficiency and reduce the subjective factor. Together with the C4.5 algorithm [4] for spam user identification, the initial sample is trained based on the ten fold cross validation. Finally, the comparison between traditional model and the improved model in this work is performed. Proposed Methodology Details of the Modeling Method The improved model proposed in this paper is based on representation learning and C4.5 algorithm. The representation learning algorithm [5] is added to replace the artificial feature selection in traditional model to overcome the subjective factor. Also, the time consuming problem can be solved. The learning phase is divided into two steps. In the first step, the features are drawn from the document using the bigram, POS tagging, and TF_IDF methods. In the second step, the principal component analysis is employed to reduction of the dimensions of the sample data. Spam User Identification Rules Because this work is aimed at solving the problem in spam user identification, it is necessary to create specific rules for the identification. The rules include: 1. Users whose microbial content contains keywords such as "activity" "special offer" etc. 2. Users whose microbial content has over 80% recurrence rates; 3. Users whose microbes have followers 10 times more than fans. Users who meet the above behavior are indicated as 1, while the remaining users are marked as 1 in the labeling 91 process. Besides, some blank users whose microbes don't contain any information, are marked as 1 too. Evaluation of the Proposed Model Experimental Environment and Data Set The computer processor used in the experiment is Core i5 2450M, dual core, 2.6GHz, 4GB DDR3 memory. The system used by the computer is windows7. The language used for the algorithm is Python. The version of Python is 2.7. The dataset for investigation in this work is from the microblog in 2016, which consists of user's profile and content of microblog. User's profile includes ID, name and number of follower together with fans. The content of microblog contains users' daily life. Experimental Procedure and Evaluation Criterion This paper utilizes the 10 fold cross validation method to train the model, and the initial sample is randomly divided into 10 independent sample subsets, which can guarantee that distribution of each category of user is uniform in each subset. Then, 9 subsets of the entire sample were selected as the training set to establish the classification detection model. The remaining 1 sample subset was used as the test set. Based on the classified data set, the 10 cross validations were performed. In this paper, the average of these 10 classification evaluation indicators is selected as the predictive classification performance evaluation index of the final model. In order to evaluate the performance of the proposed model, three indexes including accuracy (p) recall rate (r) and F value (f) were utilized in this work. Among them, F value can be calculated by accuracy and recall rate. The expressions of them are presented as follows: