Phrasing for UX: Enhancing Information Engagement through Computational Linguistics and Creative Analytics

• URL: http://arxiv.org/abs/2409.00064v1
• Date: Fri, 23 Aug 2024 00:33:47 GMT
• Title: Phrasing for UX: Enhancing Information Engagement through Computational Linguistics and Creative Analytics
• Authors: Nimrod Dvir,
• Abstract summary: This study explores the relationship between textual features and Information Engagement (IE) on digital platforms. It highlights the impact of computational linguistics and analytics on user interaction. The READ model is introduced to quantify key predictors like representativeness, ease of use, affect, and distribution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study explores the relationship between textual features and Information Engagement (IE) on digital platforms. It highlights the impact of computational linguistics and analytics on user interaction. The READ model is introduced to quantify key predictors like representativeness, ease of use, affect, and distribution, which forecast engagement levels. The model's effectiveness is validated through AB testing and randomized trials, showing strong predictive performance in participation (accuracy: 0.94), perception (accuracy: 0.85), perseverance (accuracy: 0.81), and overall IE (accuracy: 0.97). While participation metrics are strong, perception and perseverance show slightly lower recall and F1-scores, indicating some challenges. The study demonstrates that modifying text based on the READ model's insights leads to significant improvements. For example, increasing representativeness and positive affect boosts selection rates by 11 percent, raises evaluation averages from 3.98 to 4.46, and improves retention rates by 11 percent. These findings highlight the importance of linguistic factors in IE, providing a framework for enhancing digital text engagement. The research offers practical strategies applicable to fields like education, health, and media.

Related papers

• The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks [17.5336703613751]
  This study benchmarks leading large language models and vision language models against human performance on the Wechsler Adult Intelligence Scale (WAIS-IV) Most models demonstrated exceptional capabilities in the storage, retrieval, and manipulation of tokens such as arbitrary sequences of letters and numbers. Despite these broad strengths, we observed consistently poor performance on the Perceptual Reasoning Index (PRI) from multimodal models.
  arXiv  Detail & Related papers  (2024-10-09T19:22:26Z)
• Optimizing Transformer based on high-performance optimizer for predicting employment sentiment in American social media content [9.49688045612671]
  This article improves the Transformer model based on swarm intelligence optimization algorithm, aiming to predict the emotions of employment related text content on American social media. During the training process, the accuracy of the model gradually increased from 49.27% to 82.83%, while the loss value decreased from 0.67 to 0.35. The improved model proposed in this article not only improves the accuracy of sentiment recognition in employment related texts on social media, but also has important practical significance.
  arXiv  Detail & Related papers  (2024-10-09T03:14:05Z)
• Evaluating Large Language Models Using Contrast Sets: An Experimental Approach [0.0]
  We introduce an innovative technique for generating a contrast set for the Stanford Natural Language Inference dataset. Our strategy involves the automated substitution of verbs, adverbs, and adjectives with their synonyms to preserve the original meaning of sentences. This method aims to assess whether a model's performance is based on genuine language comprehension or simply on pattern recognition.
  arXiv  Detail & Related papers  (2024-04-02T02:03:28Z)
• Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language Inference [3.154631846975021]
  We focus on investigating dataset artifacts and developing strategies to address these issues. We propose several automatic data augmentation strategies spanning character to word levels. Experiments demonstrate that the proposed approaches effectively enhance model accuracy and reduce biases by up to 0.66% and 1.14%, respectively.
  arXiv  Detail & Related papers  (2023-12-14T08:46:26Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing [3.09766013093045]
  This study introduces and empirically tests a novel predictive model for digital information engagement (IE) The READ model integrates key cognitive biases with computational linguistics and natural language processing to develop a multidimensional perspective on information engagement. The READ model's potential extends across various domains, including business, education, government, and healthcare.
  arXiv  Detail & Related papers  (2023-07-26T20:58:47Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Retrieval-based Disentangled Representation Learning with Natural Language Supervision [61.75109410513864]
  We present Vocabulary Disentangled Retrieval (VDR), a retrieval-based framework that harnesses natural language as proxies of the underlying data variation to drive disentangled representation learning. Our approach employ a bi-encoder model to represent both data and natural language in a vocabulary space, enabling the model to distinguish intrinsic dimensions that capture characteristics within data through its natural language counterpart, thus disentanglement.
  arXiv  Detail & Related papers  (2022-12-15T10:20:42Z)
• A Multi-Level Attention Model for Evidence-Based Fact Checking [58.95413968110558]
  We present a simple model that can be trained on sequence structures. Results on a large-scale dataset for Fact Extraction and VERification show that our model outperforms the graph-based approaches.
  arXiv  Detail & Related papers  (2021-06-02T05:40:12Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)
• Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions [55.660255727031725]
  Influence functions explain the decisions of a model by identifying influential training examples. We conduct a comparison between influence functions and common word-saliency methods on representative tasks. We develop a new measure based on influence functions that can reveal artifacts in training data.
  arXiv  Detail & Related papers  (2020-05-14T00:45:23Z)

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.