A Content Filtering from Spam Posts on Social Media using Weighted Multimodal Approach

Abstract

The system for filtering spam posts on social media is preferred to obtain the relevant content and expected by users. The previous works on spam detection have been done to filter irrelevant content on email and social media based on text or image separately. Due to the social media posts are commonly in the form of image, text, or both, the multimodal data is preferred to improve the capability of system in handling filtering content on social media. In addition, a spam post containing multimodal data sometimes does not indicate spam in both data but only one. To improve the performance of system, we propose a weighted multimodal approach for filtering content from spam posts in social media using Convolutional Neural Network (CNN). The mechanism of weighted multimodal is by weighting of spam prediction results from image and text data. We also investigate the performance of CNN architectures for spam post detection that are 3-layer, 5-layer, AlexNet and VGG16. The performance of each architectures is evaluated by 8000 Indonesian posts in the form of image and text taken from Instagram posts. The results show that the highest accuracy achieves 0.9850 based on the combination of image and text by using a 5-layer architecture. The average accuracy of all CNN architectures using multimodal data is higher than only using image and text data separately.