PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction

arXiv:2402.18950v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.cl daily events focus generate language language models media platforms prediction public responses social social media social media platforms study type