The code and datasets for "PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction."
Social media platforms are daily exhibiting millions of events. To better track public responses to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous work 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.
Code would be released soon.