mlsanityData to 14 Oct 2026

Measuring the Effect of Influential Messages on Varying Personas

arXiv 2305.16470 ↗First posted 25 May 2023cs.CL cs.LGcited by 8

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Abstract (arXiv)

Predicting how a user responds to news events enables important applications such as allowing intelligent agents or content producers to estimate the effect on different communities and revise unreleased messages to prevent unexpected bad outcomes such as social conflict and moral injury. We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona (characterizing an individual or a group) might have upon seeing a news message. Compared to the previous efforts which only predict generic comments to news, the proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response. This enables more accurate and comprehensive inference on the mental state of the persona. Meanwhile, the generated sentiment dimensions make the evaluation and application more reliable. We create the first benchmark dataset, which consists of 13,357 responses to 3,847 news headlines from Twitter. We further evaluate the SOTA neural language models with our dataset. The empirical results suggest that the included persona attributes are helpful for the performance of all response dimensions. Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

9 of 29 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
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  8. 2019Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generationcited by 12
  9. 2022Leveraging Users' Social Network Embeddings for Fake News Detection on Twittercited by 1

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 3.

8 papers.

  1. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  2. 2024Aligning LLMs with Individual Preferences via Interactioncited by 63
  3. 2024Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinementcited by 49
  4. 2023Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecastingcited by 11
  5. 2024PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intentcited by 9
  6. 2025Beyond Reactive Safety: Risk-Aware LLM Alignment via Long-Horizon Simulationcited by 2
  7. 2025User Behavior Prediction as a Generic, Robust, Scalable, and Low-Cost Evaluation Strategy for Estimating Generalization in LLMscited by 2
  8. 2025Predicting Social Media User Actions: A Hybrid Approach for Common and Rare Behavior Prediction on Blueskycited by 0