Generator-Guided Crowd Reaction Assessment
WWW 2024(2024)
摘要
In the realm of social media, understanding and predicting post reach is a
significant challenge. This paper presents a Crowd Reaction AssessMent (CReAM)
task designed to estimate if a given social media post will receive more
reaction than another, a particularly essential task for digital marketers and
content writers. We introduce the Crowd Reaction Estimation Dataset (CRED),
consisting of pairs of tweets from The White House with comparative measures of
retweet count. The proposed Generator-Guided Estimation Approach (GGEA)
leverages generative Large Language Models (LLMs), such as ChatGPT, FLAN-UL2,
and Claude, to guide classification models for making better predictions. Our
results reveal that a fine-tuned FLANG-RoBERTa model, utilizing a cross-encoder
architecture with tweet content and responses generated by Claude, performs
optimally. We further use a T5-based paraphraser to generate paraphrases of a
given post and demonstrate GGEA's ability to predict which post will elicit the
most reactions. We believe this novel application of LLMs provides a
significant advancement in predicting social media post reach.
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