Generator-Guided Crowd Reaction Assessment

• URL: http://arxiv.org/abs/2403.09702v1
• Date: Fri, 8 Mar 2024 13:05:44 GMT
• Title: Generator-Guided Crowd Reaction Assessment
• Authors: Sohom Ghosh, Chung-Chi Chen, Sudip Kumar Naskar,
• Abstract summary: This paper presents a Crowd Reaction AssessMent task designed to estimate if a given social media post will receive more reaction than another. We introduce the Crowd Reaction Estimation dataset (CRED), consisting of pairs of tweets from The White House with comparative measures of retweet count. 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.
• Score: 4.1756520114950035
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 media post reach.

Related papers

• MetaKP: On-Demand Keyphrase Generation [52.48698290354449]
  We introduce on-demand keyphrase generation, a novel paradigm that requires keyphrases that conform to specific high-level goals or intents. We present MetaKP, a large-scale benchmark comprising four datasets, 7500 documents, and 3760 goals across news and biomedical domains with human-annotated keyphrases. We demonstrate the potential of our method to serve as a general NLP infrastructure, exemplified by its application in epidemic event detection from social media.
  arXiv  Detail & Related papers  (2024-06-28T19:02:59Z)
• Rumour Evaluation with Very Large Language Models [2.6861033447765217]
  This work proposes to leverage the advancement of prompting-dependent large language models to combat misinformation. We employ two prompting-based LLM variants to extend the two RumourEval subtasks. For veracity prediction, three classifications schemes are experimented per GPT variant. Each scheme is tested in zero-, one- and few-shot settings. For stance classification, prompting-based-approaches show comparable performance to prior results, with no improvement over finetuning methods.
  arXiv  Detail & Related papers  (2024-04-11T19:38:22Z)
• Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs [49.547988001231424]
  We propose the one-shot-subgraph link prediction to achieve efficient and adaptive prediction. Design principle is that, instead of directly acting on the whole KG, the prediction procedure is decoupled into two steps. We achieve promoted efficiency and leading performances on five large-scale benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T12:00:12Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scaling of Texts with Large Language Models [3.9940425551415597]
  Existing text scaling methods often require a large corpus, struggle with short texts, or require labeled data. We develop a text scaling method that leverages the pattern recognition capabilities of generative large language models. We demonstrate how combining substantive knowledge with LLMs can create state-of-the-art measures of abstract concepts.
  arXiv  Detail & Related papers  (2023-10-18T15:34:37Z)
• Context-Based Tweet Engagement Prediction [0.0]
  This thesis investigates how well context alone may be used to predict tweet engagement likelihood. We employed the Spark engine on TU Wien's Little Big Data Cluster to create scalable data preprocessing, feature engineering, feature selection, and machine learning pipelines. We also found that factors such as the prediction algorithm, training dataset size, training dataset sampling method, and feature selection significantly affect the results.
  arXiv  Detail & Related papers  (2023-09-28T08:36:57Z)
• DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation [80.45816053153722]
  DialogVED introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation.
  arXiv  Detail & Related papers  (2022-04-27T16:18:15Z)
• Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application [63.10266319378212]
  We propose a method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT) We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers.
  arXiv  Detail & Related papers  (2020-09-22T02:15:05Z)
• Dialogue Response Ranking Training with Large-Scale Human Feedback Data [52.12342165926226]
  We leverage social media feedback data to build a large-scale training dataset for feedback prediction. We trained DialogRPT, a set of GPT-2 based models on 133M pairs of human feedback data. Our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
  arXiv  Detail & Related papers  (2020-09-15T10:50:05Z)
• Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media [21.619614611039257]
  We propose interpretive Multi-Modal Depression Detection with Hierarchical Attention Network MDHAN. Our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.
  arXiv  Detail & Related papers  (2020-07-03T12:11:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.