Data available online is growing day by day that leads to a tough competition among data publishers to attract the largest possible audience. Obtaining reliable prediction related to future popularity of the online content becomes a concern to the publishers. In this paper, a novel nature-inspired metaheuristic framework is proposed that shortlists the prominent features of the video to participate in making predictions. Fitness of the set of selected features is calculated using mean square error of the deviation between predicted and actual popularity. The exhaustive examinations on standard benchmark parameters is performed on the datasets, i.e., Facebook 2015, Top and random datasets of YouTube. The performance of the proposed algorithms, namely particle swarm optimization with support vector regression (PSO-SVR), bat algorithm with support vector regression (BA-SVR), dragonfly algorithm with support vector regression (DA-SVR) and existing prediction method support vector regression (SVR), is compared. DA-SVR outperforms SVR, PSO-SVR and BA-SVR in terms of coefficient of regularization (R²) score and number of features selected.

在线可用数据日益增长，这导致数据发布者之间为了吸引尽可能多的受众而展开激烈竞争。获得与在线内容未来流行度相关的可靠预测成为发布者关心的问题。在本文中，提出了一种新颖的受自然启发的元启发式框架，该框架将视频的突出特征列入候选名单以参与预测。使用预测流行度和实际流行度之间偏差的均方误差计算所选特征集的适应度。对标准基准参数的详尽检查是在数据集上进行的，即 Facebook 2015、YouTube 的 Top 和随机数据集。所提出算法的性能，即支持向量回归的粒子群优化（PSO-SVR），蝙蝠算法支持向量回归（BA-SVR），蜻蜓算法支持向量回归（DA-SVR）和现有的预测方法支持向量回归（SVR），比较。DA-SVR 在正则化系数 (R) 方面优于 SVR、PSO-SVR 和 BA-SVR²）得分和选择的特征数量。