Twitter hesap içeriğinden ve yapısından kullanıcı türlerinin belirlenmesi

People are using social media platforms more and more every day; hence, they are becoming suitable for research studies by their rich content. Twitter is one of the biggest and most widely used social media platforms, and many studies focus on Twitter for social media research. In this thesis, we propose methodologies for determining user types of Twitter accounts by their metadata, content, and structure. Our first problem is classifying organization vs. individual account types using only metadata. After we give details about the data collection and analysis process, we clean text features and normalize number features. Proposed model contains LSTM for textual features and MLP for numeric features. Language independent word embeddings make the proposed model useful for accounts in different languages and multi-language accounts. Experiments show that our model requires less data and performs better results than previous studies about account type classification. The second problem is to personality type prediction of individual accounts. Ground truth data comes from Big Five Personality tests and Twitter accounts of a group of people. This time model also contains tweets and graph features of the account. Personalities are classified in terms of their Big Five Test scores, and for each OCEAN feature, we train different models. Experiment results are promising even though we study with a small set of users.