Organized Event Participant Prediction Enhanced by Social Media Retweeting Data

TL;DR

This paper introduces a novel approach to predict event participants by leveraging social media retweeting data through a joint knowledge graph and a specialized learning model, improving accuracy especially with limited data.

Contribution

It proposes a new method that integrates social media activity into event participant prediction, addressing data scarcity issues with a joint knowledge graph and retweet-based learning model.

Findings

Our approach outperforms baseline models in real-world scenarios.

It is especially effective with limited training data.

The method shows strong results in warm test cases.

Abstract

Nowadays, many platforms on the Web offer organized events, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate model. In this paper, we propose to utilize social media retweeting activity data to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilizes retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models, especially with the warm test cases, and when target domain data is limited.