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Many threats in the real world can be related to activities of persons on the internet. Internet
surveillance aims to detect threats in an early stage and to assist in finding suspects based on
information from the web. However, the amount of data on the internet rapidly increases and it is time
consuming to monitor many open sources. In this paper, we present a method to find abnormal
behavior on the internet and give an early warning for threats. The system was tested on Twitter data.
The results show that it can successfully analyze the content of messages and recognize abnormal
changes in sentiment and threatening content.