Measuring and Fostering Peace through Machine Learning and Artificial Intelligence
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914250393387008 |
|---|---|
| author | Gilda, P. Dungarwal, P. Thongkham, A. Ajayi, E. T. Choudhary, S. Terol, T. M. Lam, C. Araujo, J. P. McFadyen-Mungalln, M. Liebovitch, L. S. Coleman, P. T. West, H. Sieck, K. Carter, S. |
| author_facet | Gilda, P. Dungarwal, P. Thongkham, A. Ajayi, E. T. Choudhary, S. Terol, T. M. Lam, C. Araujo, J. P. McFadyen-Mungalln, M. Liebovitch, L. S. Coleman, P. T. West, H. Sieck, K. Carter, S. |
| contents | We used machine learning and artificial intelligence: 1) to measure levels of peace in countries from news and social media and 2) to develop on-line tools that promote peace by helping users better understand their own media diet. For news media, we used neural networks to measure levels of peace from text embeddings of on-line news sources. The model, trained on one news media dataset also showed high accuracy when used to analyze a different news dataset. For social media, such as YouTube, we developed other models to measure levels of social dimensions important in peace using word level (GoEmotions) and context level (Large Language Model) methods. To promote peace, we note that 71% of people 20-40 years old daily view most of their news through short videos on social media. Content creators of these videos are biased towards creating videos with emotional activation, making you angry to engage you, to increase clicks. We developed and tested a Chrome extension, MirrorMirror, which provides real-time feedback to YouTube viewers about the peacefulness of the media they are watching. Our long term goal is for MirrorMirror to evolve into an open-source tool for content creators, journalists, researchers, platforms, and individual users to better understand the tone of their media creation and consumption and its effects on viewers. Moving beyond simple engagement metrics, we hope to encourage more respectful, nuanced, and informative communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05232 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Measuring and Fostering Peace through Machine Learning and Artificial Intelligence Gilda, P. Dungarwal, P. Thongkham, A. Ajayi, E. T. Choudhary, S. Terol, T. M. Lam, C. Araujo, J. P. McFadyen-Mungalln, M. Liebovitch, L. S. Coleman, P. T. West, H. Sieck, K. Carter, S. Computation and Language Computers and Society Machine Learning I.2.6 We used machine learning and artificial intelligence: 1) to measure levels of peace in countries from news and social media and 2) to develop on-line tools that promote peace by helping users better understand their own media diet. For news media, we used neural networks to measure levels of peace from text embeddings of on-line news sources. The model, trained on one news media dataset also showed high accuracy when used to analyze a different news dataset. For social media, such as YouTube, we developed other models to measure levels of social dimensions important in peace using word level (GoEmotions) and context level (Large Language Model) methods. To promote peace, we note that 71% of people 20-40 years old daily view most of their news through short videos on social media. Content creators of these videos are biased towards creating videos with emotional activation, making you angry to engage you, to increase clicks. We developed and tested a Chrome extension, MirrorMirror, which provides real-time feedback to YouTube viewers about the peacefulness of the media they are watching. Our long term goal is for MirrorMirror to evolve into an open-source tool for content creators, journalists, researchers, platforms, and individual users to better understand the tone of their media creation and consumption and its effects on viewers. Moving beyond simple engagement metrics, we hope to encourage more respectful, nuanced, and informative communication. |
| title | Measuring and Fostering Peace through Machine Learning and Artificial Intelligence |
| topic | Computation and Language Computers and Society Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2601.05232 |