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sohomghosh/Generator-Guided-Crowd-Reaction-Assessment

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The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-sa-4.0, from the tagEvidence: README.md at e80a078
Licence files: No licence file at the top level of the repositoryEvidence: file list at e80a078
Declared sources: None listed in the card metadata
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Tasks

text-classification

Citation text from the card

@inproceedings{10.1145/3589335.3651512,
author = {Ghosh, Sohom and Chen, Chung-Chi and Naskar, Sudip Kumar},
title = {Generator-Guided Crowd Reaction Assessment},
year = {2024},
isbn = {9798400701726},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589335.3651512},
doi = {10.1145/3589335.3651512},
abstract = {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 medi

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dataset_licence({ "id": "sohomghosh/Generator-Guided-Crowd-Reaction-Assessment" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0