InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
This addresses the challenge for companies in efficiently selecting influencers among millions of users, though it is incremental as it builds on existing graph and neural network techniques.
The paper tackles the problem of identifying effective influencers for social media marketing by proposing InfluencerRank, a method that ranks influencers based on their posting behaviors and social relations over time, and it outperforms existing baselines on an Instagram dataset of 18,397 influencers.
As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.