.png){kind=icon}
.png){kind=icon}
Return
Detecting malicious activity in Twitter using deep learning techniques
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2021.107360.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Undoubtedly, social media, such as Facebook and Twitter, constitute a major part of our everyday life due to the incredible possibilities they offer to their users. However, Twitter and generally online social networks (OSNs) are increasingly used by automated accounts, widely known as bots, due to their immense popularity across a wide range of user categories. Their main purpose is the dissemination of fake news, the promotion of specific ideas and products, the manipulation of the stock market and even the diffusion of sexually explicit material. Therefore, the early detection of bots in social media is quite essential. In this paper, two methods are introduced targeting this that are mainly based on Natural Language Processing (NLP) to distinguish legitimate users from bots. In the first method, a feature extraction approach is proposed for identifying accounts posting automated messages. After applying feature selection techniques and dealing with imbalanced datasets, the subset of features selected is fed in machine learning algorithms. In the second method, a deep learning architecture is proposed to identify whether tweets have been posted by real users or generated by bots. To the best of the authors' knowledge, there is no prior work on using an attention mechanism for identifying bots. The introduced approaches have been evaluated over a series of experiments using two large real Twitter datasets and demonstrate valuable advantages over other existing techniques targeting the identification of malicious users in social media. (C) 2021 The Authors. Published by Elsevier B.V.
Keywords:
Social media
Twitter
Bot detection
Feature extraction
Feature selection
Machine learning
Deep learning
Attention mechanism
Natural Language Processing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
No organization information available