---
title: "A Predictive Model of Digital Information Engagement:"
description: "Drawing on Cumulative Prospect Theory and the heuristics-and-biases tradition, the authors propose… 37 references, cited by 1 paper here."
authors: ["Nimrod Dvir", "Elaine Friedman", "Suraj Commuri", "Fan yang", "Jennifer Romano"]
year: 2023
venue: "arXiv:2307.14500"
source: "https://arxiv.org/abs/2307.14500"
doi: "10.48550/arxiv.2307.14500"
arxiv: "2307.14500"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dvir-2023-predictive-model-digital-information-engagement/"
---
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# A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing

**Authors:** Nimrod Dvir, Elaine Friedman, Suraj Commuri, [Fan yang](https://md.research.elamur.ai/free/en/authors/fan-yang/) and Jennifer Romano · **Year:** 2023 · **Venue:** arXiv:2307.14500 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2307.14500) · [PDF](https://arxiv.org/pdf/2307.14500) · [DOI 10.48550/arxiv.2307.14500](https://doi.org/10.48550/arxiv.2307.14500) · [arXiv 2307.14500](https://arxiv.org/abs/2307.14500) · [OpenAlex](https://openalex.org/W4385373816)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether engagement with digital text can be forecast from the wording itself, starting with the smallest unit: a single English word. Drawing on Cumulative Prospect Theory and the heuristics-and-biases tradition, the authors propose the READ model. It scores a word on four attributes. Representativeness covers semantic relations such as equivalence, hierarchy and association. Ease-of-use covers length, syllable count and readability in the Flesch–Kincaid style. Affect is sentiment polarity, and Distribution is frequency and familiarity. The authors sampled 50 synonym pairs (100 words) from WordNet and gathered human engagement ratings through a large online survey. A Python program computed the READ features for each word. Predicted engagement tracked the survey scores closely. Comparing the feature profiles of two synonyms picked the more engaging word in roughly 82–84% of pairs; the abstract and the discussion report different figures. The work matters because it turns a vague UX notion into measurable lexical signals that could guide word choice and generative text systems.

## Key points
- The test set is 50 synonym pairs taken from WordNet and identified by synset (results tables mention items like wiccan.n.01 and well.r.11), giving 100 words in total.
- The abstract reports n = 80,500 survey responses. The limitations section says participants were undergraduates at one university, recruited through snowball sampling.
- Each READ attribute is tied to a known bias:
  - Representativeness to the representativeness heuristic.
  - Ease-of-use to processing fluency.
  - Affect to the affect heuristic.
  - Distribution to availability and recognition.
- Accuracy at picking the more engaging synonym is 84% in the abstract and 82% in the discussion.
- In the discussion, engagement rose with emotional intensity of either polarity and with word frequency. The paper contrasts its approach with the self-report User Engagement Scale (UES), whose four dimensions capture how visually attractive something is, how much it absorbs the user's attention, how usable it seems, and how rewarding the experience feels.

## Interesting ideas
- Synonym pairs hold meaning fixed and vary only the surface form, so any measured difference comes from wording rather than content.
- Each abstract psychological construct is mapped to an explicit, inspectable feature (frequency, syllables, polarity, semantic relations) rather than a learned embedding.
- Evaluating by pairwise preference ("which of two is better") is easier for judges and more stable than assigning absolute scores.
- Emotional intensity is treated separately from emotional direction, since strong words of either sign drew engagement.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (back): when generation picks a lexicalization from a synset, pairwise synonym tests can show whether the chosen form is the more natural one while the meaning graph stays unchanged.
- **I2** (world): the compiled lexical knowledge could store such per-lemma attributes as named, explainable values that downstream steps can cite.
- **I3** (learn): candidate repair rules or competing parses could be compared in pairs by witness parsers or an LLM teacher, instead of each being scored on its own scale.
- **I4** (frames): irony and speech-act detection could keep affect magnitude and affect polarity as two separate signals, so mismatches between them become a visible cue.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Google](https://md.research.elamur.ai/free/en/companies/google/) — Company, Mountain View, United States · 430 works · 424,363 citations
- [State University of New York](https://md.research.elamur.ai/free/en/universities/state-university-of-new-york/) — University, Albany, United States · 2 works · 1,026 citations
- [University at Albany, State University of New York](https://md.research.elamur.ai/free/en/universities/university-at-albany-state-university-of-new-york/) — University, Albany, United States · 5 works · 83 citations

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## Cite

Cite as: Nimrod Dvir, Elaine Friedman, Suraj Commuri, Fan yang and Jennifer Romano. 2023. A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing. arXiv:2307.14500.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{dvir2023predictive,
  title = {{A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing}},
  author = {Nimrod Dvir, Elaine Friedman, Suraj Commuri, Fan yang and Jennifer Romano},
  year = {2023},
  doi = {10.48550/arxiv.2307.14500},
  eprint = {2307.14500},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.14500}
}
```

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37 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*