# Lumen: A Machine Learning Framework to Expose Influence Cues in Text

arXiv: https://arxiv.org/abs/2107.10655 · HTML: https://mlsanity.com/paper/2107.10655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 82 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Cognitive Triaging of Phishing Attacks](https://mlsanity.com/paper/1905.02162/) (2019)
- [A new evaluation framework for topic modeling algorithms based on synthetic corpora](https://mlsanity.com/paper/1901.09848/) (2019)

## Built on by (most cited first, 1 of 1)

- [AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders](https://mlsanity.com/paper/2607.13123/) (2026)

## Abstract (arXiv)

Phishing and disinformation are popular social engineering attacks with attackers invariably applying influence cues in texts to make them more appealing to users. We introduce Lumen, a learning-based framework that exposes influence cues in text: (i) persuasion, (ii) framing, (iii) emotion, (iv) objectivity/subjectivity, (v) guilt/blame, and (vi) use of emphasis. Lumen was trained with a newly developed dataset of 3K texts comprised of disinformation, phishing, hyperpartisan news, and mainstream news. Evaluation of Lumen in comparison to other learning models showed that Lumen and LSTM presented the best F1-micro score, but Lumen yielded better interpretability. Our results highlight the promise of ML to expose influence cues in text, towards the goal of application in automatic labeling tools to improve the accuracy of human-based detection and reduce the likelihood of users falling for deceptive online content.