With 1.3 billion users, Instagram (IG) has also become a business tool. IG influencer marketing, expected to generate $33.25 billion in 2022, encourages companies and influencers to create trending content. Various methods have been proposed for predicting a post's popularity, i.e., how much engagement (e.g., Likes) it will generate. However, these methods are limited: first, they focus on forecasting the likes, ignoring the number of comments, which became crucial in 2021. Secondly, studies often use biased or limited data. Third, researchers focused on Deep Learning models to increase predictive performance, which are difficult to interpret. As a result, end-users can only estimate engagement after a post is created, which is inefficient and expensive. A better approach is to generate a post based on what people and IG like, e.g., by following guidelines. In this work, we uncover part of the underlying mechanisms driving IG engagement. To achieve this goal, we rely on statistical analysis and interpretable models rather than Deep Learning (black-box) approaches. We conduct extensive experiments using a worldwide dataset of 10 million posts created by 34K global influencers in nine different categories. With our simple yet powerful algorithms, we can predict engagement up to 94% of F1-Score, making us comparable and even superior to Deep Learning-based method. Furthermore, we propose a novel unsupervised algorithm for finding highly engaging topics on IG. Thanks to our interpretable approaches, we conclude by outlining guidelines for creating successful posts.
翻译:使用13亿用户,Instagram (IG), Instagram (IG) 也已成为一个商业工具。 IG 影响力营销,预期在2022年产生332.5亿美元,鼓励公司和影响力营销创造趋势内容。提出了预测一个职位的受欢迎程度的各种方法,即,它会产生多少参与(如喜欢),然而,这些方法是有限的:首先,它们侧重于预测类似情况,忽视了2021年变得至关重要的评论数量。第二,研究经常使用偏差或有限的数据。第三,研究人员侧重于深层学习模型,以提高预测性能,这很难解释。结果,最终用户只能在创建一个职位后估计参与程度低廉和昂贵的内容。一个更好的办法是根据人们和IG喜欢的内容(例如喜欢)来生成一个职位。在这项工作中,我们发现驱动IG参与的部分基本机制。为了实现这一目标,我们常常依靠统计分析和可解释的模型,而不是深层次学习(黑盒)方法。我们用一个简单的、具有可比性的FK 3400万个全球数据序列来进行广泛的实验。我们用一个简单的、有1400万个高超级的、有弹性的G级的数据序列来预测。