When or What? Understanding Consumer Engagement on Digital Platforms

• URL: http://arxiv.org/abs/2510.10474v1
• Date: Sun, 12 Oct 2025 06:53:57 GMT
• Title: When or What? Understanding Consumer Engagement on Digital Platforms
• Authors: Jingyi Wu, Junying Liang,
• Abstract summary: This study applies Latent Dirichlet Allocation modeling to a large corpus of TED Talks.<n>By comparing the thematic supply of creators with the demand expressed in audience engagement, we identify persistent mismatches between producer offerings and consumer preferences.
• Score: 1.593326304030926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding what drives popularity is critical in today's digital service economy, where content creators compete for consumer attention. Prior studies have primarily emphasized the role of content features, yet creators often misjudge what audiences actually value. This study applies Latent Dirichlet Allocation (LDA) modeling to a large corpus of TED Talks, treating the platform as a case of digital service provision in which creators (speakers) and consumers (audiences) interact. By comparing the thematic supply of creators with the demand expressed in audience engagement, we identify persistent mismatches between producer offerings and consumer preferences. Our longitudinal analysis further reveals that temporal dynamics exert a stronger influence on consumer engagement than thematic content, suggesting that when content is delivered may matter more than what is delivered. These findings challenge the dominant assumption that content features are the primary drivers of popularity and highlight the importance of timing and contextual factors in shaping consumer responses. The results provide new insights into consumer attention dynamics on digital platforms and carry practical implications for marketers, platform managers, and content creators seeking to optimize audience engagement strategies.

Related papers

• An attention economy model of co-evolution between content quality and audience selectivity [0.050745801979964804]
  Human attention has become a scarce and strategically contested resource in digital environments.<n>We develop a minimal mathematical framework to explain how content quality and audience attention coevolve under limited attention capacity.
  arXiv  Detail & Related papers  (2026-02-06T07:07:57Z)
• MMPersuade: A Dataset and Evaluation Framework for Multimodal Persuasion [73.99171322670772]
  Large Vision-Language Models (LVLMs) are increasingly deployed in domains such as shopping, health, and news.<n> MMPersuade provides a unified framework for systematically studying multimodal persuasion dynamics in LVLMs.
  arXiv  Detail & Related papers  (2025-10-26T17:39:21Z)
• Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms [68.51708490104687]
  We show that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on platforms.
  arXiv  Detail & Related papers  (2024-10-31T07:19:22Z)
• Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective [0.3277163122167434]
  This study focuses on the consumer reviews of Indian hotels to extract aspects important for final ratings. The study involves gathering data using web scraping methods, analyzing the texts using Latent Dirichlet Allocation and sentiment analysis.
  arXiv  Detail & Related papers  (2024-08-08T10:58:33Z)
• Symmetries, Scaling Laws and Phase Transitions in Consumer Advertising Response [0.0]
  This research introduces a new modeling approach based on the concepts of symmetries and scaling laws in physics.<n>We propose a model that accounts for a key aspect: the saturation effect.<n>We introduce new key parameters like Marketing Sensitivity, Response Sensitivity, and Behavioral Sensitivit, that offer additional insights into the drivers of audience engagement and advertising performance.
  arXiv  Detail & Related papers  (2024-04-01T11:23:31Z)
• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
  We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
  arXiv  Detail & Related papers  (2022-07-25T14:37:18Z)
• Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies [24.246450472404614]
  This work empirically examines the causal effects of user-generated online reviews on a granular level. We consider multiple aspects, e.g., the Food and Service of a restaurant.
  arXiv  Detail & Related papers  (2021-12-19T22:29:02Z)
• Feedback Shaping: A Modeling Approach to Nurture Content Creation [10.31854532203776]
  We propose a modeling approach to predict how feedback from content consumers incentivizes creators. We then leverage this model to optimize the newsfeed experience for content creators by reshaping the feedback distribution. We present a deployed use case on the LinkedIn newsfeed, where we used this approach to improve content creation significantly without compromising the consumers' experience.
  arXiv  Detail & Related papers  (2021-06-21T22:53:16Z)
• SoMin.ai: Personality-Driven Content Generation Platform [60.49416044866648]
  We showcase the World's first personality-driven marketing content generation platform, called SoMin.ai. The platform combines deep multi-view personality profiling framework and style generative adversarial networks. It can be used for the enhancement of the social networking user experience as well as for content marketing routines.
  arXiv  Detail & Related papers  (2020-11-30T08:33:39Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)

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.