Online social networks are an important part of people’s life and also become the platform where spammers use suspicious accounts to spread malicious URLs. In order to detect suspicious accounts in online social networks, researchers make a lot of efforts. Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. Besides, such methods are unable to cope with the variable and unknown features. The works based on graph mainly use the location and social relationship of spammers, and they need to build a huge social graph, which leads to much computing cost. Thus, it is necessary to propose a lightweight algorithm which is hard to be evaded. In this article, we propose a lightweight algorithm GroupFound, which focuses on the structure of the local graph. As the bi-followers come from different social communities, we divide all accounts into different groups and compute the average number of accounts for these groups. We evaluate GroupFound on Sina Weibo dataset and find an appropriate threshold to identify suspicious accounts. Experimental results have demonstrated that our algorithm can accomplish a high detection rate of 86.27% at a low false positive rate of 8.54%.
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