Predicting Social Media Engagement from Emotional and Temporal Features

• URL: http://arxiv.org/abs/2508.21650v1
• Date: Fri, 29 Aug 2025 14:14:17 GMT
• Title: Predicting Social Media Engagement from Emotional and Temporal Features
• Abstract summary: We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features.<n>The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics.<n>A multi target regression model based on HistGradientBoostingRegressor is trained on log transformed engagement ratios to address skewed targets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a machine learning approach for predicting social media engagement (comments and likes) from emotional and temporal features. The dataset contains 600 songs with annotations for valence, arousal, and related sentiment metrics. A multi target regression model based on HistGradientBoostingRegressor is trained on log transformed engagement ratios to address skewed targets. Performance is evaluated with both a custom order of magnitude accuracy and standard regression metrics, including the coefficient of determination (R^2). Results show that emotional and temporal metadata, together with existing view counts, predict future engagement effectively. The model attains R^2 = 0.98 for likes but only R^2 = 0.41 for comments. This gap indicates that likes are largely driven by readily captured affective and exposure signals, whereas comments depend on additional factors not represented in the current feature set.

Related papers

• RSRank: Learning Relevance from Representational Shifts [2.996596877662786]
  We introduce a lightweight training framework that learns projections RS to calibrated relevance scores.<n>Our training objectives naturally filter irrelevant content at a zero threshold, reducing dependence tuning.
  arXiv  Detail & Related papers  (2026-06-16T03:29:23Z)
• Leveraging Large Language Models for Predictive Analysis of Human Misery [1.2458057399345226]
  This study investigates the use of Large Language Models (LLMs) for predicting human-perceived misery scores.<n>We evaluate multiple prompting strategies, including zero-shot, fixed-context few-shot, and retrieval-based prompting.<n>To move beyond static evaluation, we introduce the "Misery Game Show", a novel gamified framework inspired by a television format.
  arXiv  Detail & Related papers  (2025-08-18T07:02:59Z)
• Temporal Interest Network for User Response Prediction [33.95334897033641]
  We propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. TIN has been successfully deployed in production since October 2023, serving the WeChat Moments traffic.
  arXiv  Detail & Related papers  (2023-08-15T05:48:44Z)
• Gender Biases in Automatic Evaluation Metrics for Image Captioning [87.15170977240643]
  We conduct a systematic study of gender biases in model-based evaluation metrics for image captioning tasks. We demonstrate the negative consequences of using these biased metrics, including the inability to differentiate between biased and unbiased generations. We present a simple and effective way to mitigate the metric bias without hurting the correlations with human judgments.
  arXiv  Detail & Related papers  (2023-05-24T04:27:40Z)
• Measuring Fairness of Text Classifiers via Prediction Sensitivity [63.56554964580627]
  ACCUMULATED PREDICTION SENSITIVITY measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features. We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.
  arXiv  Detail & Related papers  (2022-03-16T15:00:33Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• An Estimation of Online Video User Engagement from Features of Continuous Emotions [13.244780102036069]
  Portraying emotion and trustworthiness is known to increase the appeal of video content. We investigate features extracted from these signals against various user engagement indicators. Our results demonstrate that smaller boundary ranges and fluctuations for arousal lead to an increase in user engagement. An effective combination of features is outlined for approaches aiming to automatically predict several user engagement indicators.
  arXiv  Detail & Related papers  (2021-05-04T17:18:47Z)
• A framework for predicting, interpreting, and improving Learning Outcomes [0.0]
  We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student. ESQ can be used to predict the future scoring potential of a student as well as offer personalized learning nudges.
  arXiv  Detail & Related papers  (2020-10-06T11:22:27Z)
• 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)
• Value-driven Hindsight Modelling [68.658900923595]
  Value estimation is a critical component of the reinforcement learning (RL) paradigm. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. We develop an approach for representation learning in RL that sits in between these two extremes. This provides tractable prediction targets that are directly relevant for a task, and can thus accelerate learning the value function.
  arXiv  Detail & Related papers  (2020-02-19T18:10:20Z)

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.