Analyzing social media data and predict personal and business information from it
Sosyal medya verilerini analiz etmek ve bitten kişisel ve iş bilgilerini tahmin etmek
- Tez No: 796199
- Danışmanlar: Assist. Prof. Dr. MESUT ÇEVİK
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: Troll Detection, Deep-Learning, Artificial Intelligence, Social Media, Bots
- Yıl: 2022
- Dil: İngilizce
- Üniversite: Altınbaş Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Elektrik ve Bilgisayar Mühendisliği Bilim Dalı
- Sayfa Sayısı: 79
Özet
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Özet (Çeviri)
Social networks become an essential part of our daily life it has been used to share personal thoughts, emotions, and life events and it doesn't ends here new trends and challenges emerges daily shaping new generations thoughts, values and even the way of living, all of these social network features makes it a powerful tool that can be used by organizations, government, and individual politicians to drive the society in certain direction using what known as social trolls or bots which are an automated accounts that belongs to certain parties. Efforts have been made to emphasis the effect of these trolls on the society not no serious actions have been made to eliminate their effects. In this thesis we utilized artificial intelligent to detect social networks trolls by analysis tweets textual contents. three deep learning models (CNN Convolutional neural network, FNN Feed forward neural network, RNN recurrent neural network) has been used in order to detect social trolls. A Comprehensive evaluation has been made in order to assess the deep learning models using two data type (tokenized text sequences and PCA data) the evaluation metrices shows promising results the CNN model achieved the highest accuracy of ( 98.33 %), followed by RNN model with accuracy of (97.245 %), and lastly came the FNN model with accuracy (97.533 %) and that was before utilizing the PCA technique which also have been evaluated but it's performance was low with the following accuracies (82.5 %) for the RNN model followed by (82.4%) for the FNN model and lastly (81.6%) for the CNN model. many evaluation matrices have been discussed in this works and presented the ups and down of each used technique.
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