# Detecting Spam Bots in Online Social Networking Sites: A Machine Learning Approach

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7 Detecting Spam Bots in Online Social Networking Sites 7 followers of the user. The result shows that there is around 1% spam account in the data set. The study [12] shows that there is probably 3% spam on Twitter. To simulate the reality and avoid the bias in my crawling method, I add more spam data to the data set. As mentioned in Section 1, Twitter provides several method for users to report spam, which includes sending Twitter a direct message and clicking on the report for spam link. The most simple and public available method is to post a tweet format is to mention the spam account. I to collect additional spam data set. Surprisedly I found that this service is abused by hoaxes and spam. Only a small percentage tweets is reporting spam. I clean the query results by manually evaluating each spam report. Finally the data set is mixed of containing around 3% spam. The evaluation of the overall process is based on a set of measures commonly used in Machine Learning and Information Retrieval. Given a classification algorithm C, I consider its confusion matrix: Prediction Spam Not Spam True Spam a b Not Spam c d Three measures are considered in the evaluation experiements: precision, recall, and F-measure. The precision is P = a/(a + c) and the recall is R = a/(a + b). The F-measure is defined as F = 2P R/(P + R). For evaluating the classification algorithms, I focus on the F-measure F as it is a standard way of summarizing both precision and recall. All the predictions reported in the paper are computed using 10-fold cross validation. For each classifier, the precision, recall, and F-measure are reported. Each classifier is trained 10 times, each time using the 9 out of the 10 partitions as training data and computing the confusion matrix using the tenth partition as test data. The evaluation metrics are estimated on the average confusion matrix. The evaluation results are shown in Table 1. The naïve Bayesian classifier has the best overall performance compared with other algorithms. Table 1. Classification Evaluation Classifier Precision Recall F-measure Decision Tree Neural Networks Support Vector Machines Naïve Bayesian

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