Document Type
Article
Source of Publication
Social Network Analysis and Mining
Publication Date
3-12-2022
Abstract
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s 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 ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ 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.
DOI Link
Publisher
Springer Science and Business Media LLC
Volume
12
Issue
1
Disciplines
Computer Sciences
Keywords
Deep learning, GLoVe embedding, LSTM, Social bot detection, Twitter, User profile metadata
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hayawi, Kadhim; Mathew, Sujith; Venugopal, Neethu; Masud, Mohammad M.; and Ho, Pin Han, "DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data" (2022). All Works. 4929.
https://zuscholars.zu.ac.ae/works/4929
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series