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In this study a novel approach that uses location based social networks for next location prediction in the field of technical surveillance and digital forensics is proposed. With the help of proposed methodology, search area for the potential criminals will be narrowed so that the spent time, money and effort by the law enforcement officers will be minimized. After collecting enough past location information for Foursquare users, the whole data is trained by means of Artificial Neural Networks. After training process, predicting the next location of the wantedperson is carried out. Prediction process is made region-based, so it is tried to predict the region of the potential criminals' next geographical location. The experimental results have shown that the proposed approach and developed system might achieve the prediction goal with only 3% error rate, and proposed methodology can be used by law enforcement officers for forensic surveillance and similar criminal acts.

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In this study an approach that uses social networking data for developing sentiment analysis system is proposed. With the help of developed software, it is tried to find out whether there is any relation between universities' academic success and sentiment of the public about them in social media. After collecting enough text based data from Twitter, preprocessing of data is carried out and final data is trained by means of Naïve Bayes Classifier. After testing process, experimental results have shown that developed sentiment analysis system can classify the tweets about top 10 universities according to URAP rankings in terms of their sentiment with the 72.33% success rate, and proposed methodology can be used by universities for understanding sentiment of the public about them in social media.