# Instagram.com in the Machine Relations Index

Canonical URL: https://machinerelations.ai/index/domains/instagram.com
Canonical domain: instagram.com
Source role: Other observed source
Release mri_score_v2.0+2026-09-18+8fa38e54dd0a; methodology mri_score_v2.0; generated 2026-09-18; window 2026-05-10 to 2026-09-18; artifact 8fa38e54dd0a.

instagram.com appeared as a cited source in 114 of 15,782 monitored answer runs (0.72%) from 2026-05-10 to 2026-09-18.

## Overall Evidence

- Citation rate: 0.72%
- Cited runs: 114 of 15,782
- Days cited: 43 of 125
- Engine breadth: 3 observed engines (google_ai_mode, google_ai_overviews, perplexity)
- Confidence: Confidence B
- Standing: #56 of 22,179 observed domains

## Observed Segments

| Category | Question shape | State | Cited runs | Observed runs | Run dates | Rate | Confidence | Standing |
|---|---|---|---:|---:|---:|---:|---|---|
| [AI Infrastructure](https://machinerelations.ai/index/categories/ai-infrastructure) | [How buyers choose](https://machinerelations.ai/index/categories/ai-infrastructure/how_choose) | published | 1 | 106 | 7 | 0.94% | Unavailable | #178 of 258 |
| [AI Infrastructure](https://machinerelations.ai/index/categories/ai-infrastructure) | [Problem-first research](https://machinerelations.ai/index/categories/ai-infrastructure/problem_first) | published | 4 | 106 | 7 | 3.77% | Unavailable | #24 of 212 |
| [AI Infrastructure](https://machinerelations.ai/index/categories/ai-infrastructure) | [Comparisons](https://machinerelations.ai/index/categories/ai-infrastructure/x_vs_y) | published | 1 | 108 | 7 | 0.93% | Unavailable | #159 of 231 |
| [AI Visibility & GEO](https://machinerelations.ai/index/categories/ai-visibility-geo) | [Best tools](https://machinerelations.ai/index/categories/ai-visibility-geo/best_x) | published | 1 | 113 | 7 | 0.88% | Unavailable | #250 of 351 |
| [AI Visibility & GEO](https://machinerelations.ai/index/categories/ai-visibility-geo) | [How buyers choose](https://machinerelations.ai/index/categories/ai-visibility-geo/how_choose) | published | 1 | 113 | 7 | 0.88% | Unavailable | #274 of 448 |
| [AI Visibility & GEO](https://machinerelations.ai/index/categories/ai-visibility-geo) | [Is it worth it](https://machinerelations.ai/index/categories/ai-visibility-geo/is_x_worth) | published | 2 | 137 | 7 | 1.46% | Unavailable | #160 of 467 |
| [AI Visibility & GEO](https://machinerelations.ai/index/categories/ai-visibility-geo) | [Problem-first research](https://machinerelations.ai/index/categories/ai-visibility-geo/problem_first) | published | 6 | 132 | 7 | 4.55% | Unavailable | #18 of 420 |
| [AI Visibility & GEO](https://machinerelations.ai/index/categories/ai-visibility-geo) | [Comparisons](https://machinerelations.ai/index/categories/ai-visibility-geo/x_vs_y) | published | 4 | 132 | 7 | 3.03% | Unavailable | #50 of 417 |
| [Consumer Finance](https://machinerelations.ai/index/categories/consumer-finance) | [Best tools](https://machinerelations.ai/index/categories/consumer-finance/best_x) | published | 2 | 132 | 7 | 1.52% | Unavailable | #108 of 236 |
| [Consumer Finance](https://machinerelations.ai/index/categories/consumer-finance) | [How buyers choose](https://machinerelations.ai/index/categories/consumer-finance/how_choose) | published | 3 | 132 | 7 | 2.27% | Unavailable | #62 of 191 |
| [Consumer Finance](https://machinerelations.ai/index/categories/consumer-finance) | [Is it worth it](https://machinerelations.ai/index/categories/consumer-finance/is_x_worth) | published | 4 | 132 | 7 | 3.03% | Unavailable | #51 of 262 |
| [Consumer Finance](https://machinerelations.ai/index/categories/consumer-finance) | [Problem-first research](https://machinerelations.ai/index/categories/consumer-finance/problem_first) | published | 9 | 132 | 7 | 6.82% | Unavailable | #16 of 226 |
| [Consumer Finance](https://machinerelations.ai/index/categories/consumer-finance) | [Comparisons](https://machinerelations.ai/index/categories/consumer-finance/x_vs_y) | published | 3 | 138 | 7 | 2.17% | Unavailable | #57 of 170 |
| [Consumer Health](https://machinerelations.ai/index/categories/consumer-health) | [Best tools](https://machinerelations.ai/index/categories/consumer-health/best_x) | published | 3 | 132 | 7 | 2.27% | Unavailable | #77 of 298 |
| [Consumer Health](https://machinerelations.ai/index/categories/consumer-health) | [How buyers choose](https://machinerelations.ai/index/categories/consumer-health/how_choose) | published | 8 | 131 | 7 | 6.11% | Unavailable | #23 of 339 |
| [Consumer Health](https://machinerelations.ai/index/categories/consumer-health) | [Is it worth it](https://machinerelations.ai/index/categories/consumer-health/is_x_worth) | published | 4 | 130 | 7 | 3.08% | Unavailable | #64 of 213 |
| [Consumer Health](https://machinerelations.ai/index/categories/consumer-health) | [Problem-first research](https://machinerelations.ai/index/categories/consumer-health/problem_first) | published | 4 | 131 | 7 | 3.05% | Unavailable | #48 of 227 |
| [Consumer Health](https://machinerelations.ai/index/categories/consumer-health) | [Top lists](https://machinerelations.ai/index/categories/consumer-health/top_list) | published | 3 | 131 | 7 | 2.29% | Unavailable | #113 of 338 |
| [Consumer Health](https://machinerelations.ai/index/categories/consumer-health) | [Comparisons](https://machinerelations.ai/index/categories/consumer-health/x_vs_y) | published | 8 | 131 | 7 | 6.11% | Unavailable | #39 of 217 |
| [Consumer Products](https://machinerelations.ai/index/categories/consumer-products) | [Best tools](https://machinerelations.ai/index/categories/consumer-products/best_x) | published | 2 | 131 | 7 | 1.53% | Unavailable | #130 of 318 |
| [Consumer Products](https://machinerelations.ai/index/categories/consumer-products) | [How buyers choose](https://machinerelations.ai/index/categories/consumer-products/how_choose) | published | 8 | 137 | 7 | 5.84% | Unavailable | #18 of 427 |
| [Consumer Products](https://machinerelations.ai/index/categories/consumer-products) | [Is it worth it](https://machinerelations.ai/index/categories/consumer-products/is_x_worth) | published | 5 | 136 | 7 | 3.68% | Unavailable | #49 of 228 |
| [Consumer Products](https://machinerelations.ai/index/categories/consumer-products) | [Problem-first research](https://machinerelations.ai/index/categories/consumer-products/problem_first) | published | 2 | 132 | 7 | 1.52% | Unavailable | #80 of 216 |
| [Consumer Products](https://machinerelations.ai/index/categories/consumer-products) | [Comparisons](https://machinerelations.ai/index/categories/consumer-products/x_vs_y) | published | 2 | 131 | 7 | 1.53% | Unavailable | #125 of 268 |
| [Cybersecurity](https://machinerelations.ai/index/categories/cybersecurity) | [News-driven citations](https://machinerelations.ai/index/categories/cybersecurity/news_topic) | published | 2 | 623 | 54 | 0.32% | Unavailable | #494 of 1,340 |
| [Emergent Prosumer](https://machinerelations.ai/index/categories/emergent-prosumer) | [Is it worth it](https://machinerelations.ai/index/categories/emergent-prosumer/is_x_worth) | published | 1 | 100 | 7 | 1.00% | Unavailable | #101 of 136 |
| [Emergent Prosumer](https://machinerelations.ai/index/categories/emergent-prosumer) | [Comparisons](https://machinerelations.ai/index/categories/emergent-prosumer/x_vs_y) | published | 6 | 95 | 7 | 6.32% | Unavailable | #21 of 118 |
| [Enterprise Software](https://machinerelations.ai/index/categories/enterprise-software) | [News-driven citations](https://machinerelations.ai/index/categories/enterprise-software/news_topic) | published | 1 | 596 | 53 | 0.17% | Unavailable | #937 of 1,375 |
| [Family Software](https://machinerelations.ai/index/categories/family-software) | [Top lists](https://machinerelations.ai/index/categories/family-software/top_list) | published | 1 | 108 | 7 | 0.93% | Unavailable | #158 of 195 |
| [Fintech](https://machinerelations.ai/index/categories/fintech) | [News-driven citations](https://machinerelations.ai/index/categories/fintech/news_topic) | published | 1 | 616 | 53 | 0.16% | Unavailable | #1,047 of 1,531 |
| [Legacy News Topics](https://machinerelations.ai/index/categories/legacy-unmapped) | [News-driven citations](https://machinerelations.ai/index/categories/legacy-unmapped/news_topic) | published | 12 | 2,138 | 54 | 0.56% | Unavailable | #283 of 4,025 |

## Citation

Instagram.com (instagram.com), 0.72% citation rate, 114 of 15,782 monitored answer runs, 2026-05-10 to 2026-09-18, mri_score_v2.0, mri_score_v2.0+2026-09-18+8fa38e54dd0a, https://machinerelations.ai/index/domains/instagram.com.

## Badge

Badge image: https://machinerelations.ai/index/domains/instagram.com/badge.svg

[![Instagram.com (instagram.com), 0.72% citation rate, 114 of 15,782 monitored answer runs, 2026-05-10 to 2026-09-18, mri_score_v2.0, mri_score_v2.0+2026-09-18+8fa38e54dd0a, https://machinerelations.ai/index/domains/instagram.com.](https://machinerelations.ai/index/domains/instagram.com/badge.svg)](https://machinerelations.ai/index/domains/instagram.com)

The badge is live: it shows the current release, so it changes when the release changes, and it returns 404 if this domain leaves the released population. The link goes to this profile and the alt text is the citation above.

## Public Boundary

The public dataset reports citation rates, rankings, and evidence counts. It excludes internal query identifiers, raw cited URLs, and answer-engine provider payloads. MRI measures observed root-domain citations in monitored prompts; it does not measure all AI answers, recommendation quality, website traffic, or commercial performance. A citation does not prove source support, and MRI confidence is not an Answer-Source Fidelity grade.

## Related Sources

- [Machine Relations Index](https://machinerelations.ai/index)
- [Public JSON artifact](https://machinerelations.ai/data/machine-relations-index.json)
- [Release manifest](https://machinerelations.ai/data/mri-release-manifest.json)
- [AuthorityTech publication intelligence projection](https://authoritytech.io/publications.md) — Practitioner projection derived from the neutral Machine Relations Index. It is not source evidence, citation provenance, or sameAs identity for the neutral dataset.