PopALM: Popularity-Aligned Language Models for Social Media Trendy
  Response Prediction
Erxin Yu Jing Li Chunpu Xu
Social media platforms are daily exhibiting millions of events. To
preliminarily predict the mainstream public reaction to these events, we study
trendy response prediction to automatically generate top-liked user replies to
social media events. While previous works focus on generating responses without
factoring in popularity, we propose Popularity-Aligned Language Models (PopALM)
to distinguish responses liked by a larger audience through reinforcement
learning. Recognizing the noisy labels from user "likes", we tailor-make
curriculum learning in proximal policy optimization (PPO) to help models
capture the essential samples for easy-to-hard training. In experiments, we
build a large-scale Weibo dataset for trendy response prediction, and its
results show that PopALM can help boost the performance of advanced language
models.