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Netw. Anal. Min."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one\u2019s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either \u2018human\u2019 or \u2018bot.\u2019 We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework \u2018DeeProBot,\u2019 which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.<\/jats:p>","DOI":"10.1007\/s13278-022-00869-w","type":"journal-article","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T11:02:40Z","timestamp":1647082960000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8092-4590","authenticated-orcid":false,"given":"Kadhim","family":"Hayawi","sequence":"first","affiliation":[]},{"given":"Sujith","family":"Mathew","sequence":"additional","affiliation":[]},{"given":"Neethu","family":"Venugopal","sequence":"additional","affiliation":[]},{"given":"Mohammad M.","family":"Masud","sequence":"additional","affiliation":[]},{"given":"Pin-Han","family":"Ho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"869_CR1","doi-asserted-by":"crossref","unstructured":"Abu-El-Rub N, Mueen A (2019) Botcamp: bot-driven interactions in social campaigns. 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