Aggressive comments containing offensive images and inappropriate gesture signs together with textual comments have grown exponentially in the recent past on social media. These aggressive contents on social media are affecting the victims negatively causing fear, stress, sleeping problems and even suicide in some cases. Since social media contents are unmoderated, a technical solution with the characteristic of having automatic flagging of these contents considering the text and images together is highly needed. This article presents a deep learning and binary firefly-based optimization-based model to classify the social media posts into high-aggressive, medium-aggressive, and non-aggressive classes. The proposed model considers both text and images together to evaluate the aggression level of a post. In this model, the image features of the posts are extracted using pre-trained VGG-16 model, whereas the textual features are extracted using a three-layered convolutional neural network in parallel. The image and text features are then combined to get a hybrid feature set which is further optimized using a binary firefly optimization algorithm. Our proposed model improves the results by 11% in terms of the weighted F1-score with optimized features by binary firefly algorithm over non-optimized features.

最近，在社交媒体上，包含令人反感的图像和不适当的手势标志的激进评论以及文字评论呈指数增长。这些社交媒体上的侵略性内容正在对受害者造成负面影响，从而在某些情况下引起恐惧，压力，睡眠问题甚至自杀。由于社交媒体内容不受限制，因此迫切需要一种具有自动标记这些内容并同时考虑文本和图像的特征的技术解决方案。本文提出了一种深度学习和基于二元萤火虫的基于优化的模型，用于将社交媒体帖子分类为高攻击性，中攻击性和非攻击性类别。所提出的模型同时考虑了文本和图像，以评估帖子的攻击程度。在这个模型中 帖子的图像特征是使用预训练的VGG-16模型提取的，而文本特征则是使用三层卷积神经网络并行提取的。然后将图像和文本特征进行组合以获得混合特征集，使用二进制萤火虫优化算法对混合特征集进行进一步优化。我们提出的模型将加权F1分数与具有非优化特征的二进制萤火虫算法相比具有优化特征的F1分数提高了11％。