Applying Social Network Embedding and Word Embedding for Socialbots Detection
I‐Hsien Ting, Kazunori Minetaki, Mei-Yun Hsu, Chia-Sung Yen · 2023
With the growth of social networking website, social media has become a major platform for marketing, such as social business, political manipulation, influence and brand management, etc. However, social media marketing is very different to traditional marketing. Social media marketing needs to face large number of users and need to repeat same process frequently. It is therefore a very human power consuming task. Under this situation, it is the reason why Robotic Process Automation and Social-bots is now very popular in many social networking websites. However, there are many negative effects when applying social-bots for social marketing. Therefore, more and more researchers are devoting on propose efficient ways to detect social-bots. In this paper, we proposed an approach to detect social-bots by considering the content that users posted as well as the behavior and features when using social networking website. In this approach, we adopt the concept of word embedding and social network embedding. Convolutional neural network is used as the main techniques to train the model for social-bots detection. The experimental results show that the proposed combination approach has better detection accuracy than only social network embedding or word embedding approach as well as it reaches 92% detection accuracy by using our dataset.