Abstract
Currently, many professional users tend to promote their websites and brands via multiple online social networks. During activities of information dissemination, the users are confronted with the problem of platform selection. For a post, its platform selection should be based on platform preference, which refers to the platform in which the post can obtain more engagement. In this paper, we focus on this problem by proposing a model to predict platform preference. Specifically, we build a content similarity-based Multi-Task Learning model to predict platform preference of posts. This model takes user specific characters into account and incorporates the regularization term under our validated hypothesis about content similarity. Based on data from Twitter and Facebook, the experiments reveal this model significantly outperforms a number of the baselines. The prediction of platform preference can provide insight for users conducting platform selection to obtain more engagement.
Highlights
Online social media networks have become one of the most popular services on the internet, attracting billions of subscribers and millions of daily active users
Based on the data from Twitter and Facebook, we conduct extensive experiments to compare our method with other classification algorithms, and the results suggest that our model can significantly improve the predictive accuracies
EXPERIMENTS Here, we evaluate the effectiveness of the Multi-Task Learning (MTL) incorporating the regularization term of content similarity and the collective efficacy of the features via their performance on a binary classification task: will a given post obtain more engagement on Twitter than Facebook
Summary
Online social media networks have become one of the most popular services on the internet, attracting billions of subscribers and millions of daily active users. This tremendous success has created a very profitable market, and there exist multiple online social media sites which are very popular and provide different services, such as information-gathering and information propagation [1]. As the widespread information sharing activities in multiple social media networks, organizations and companies are confronted with the problem about how to gain the full benefit these platforms provide [4], [5]. Some present detailed skills to help users gain more engagement via social media, such as how to control the number of repeated
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