Pith Number

pith:N6EZ6AJS

pith:2026:N6EZ6AJSJIPNRRUHTAH4BSXANG

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Topical Shifts in the Dark Web: A Longitudinal Analysis of Content from the Cybercrime Ecosystem

Irdin Pekaric, Luca Allodi, Maximilian Schafer, Philipp Zech, Raffaela Groner, Roy Ricaldi

Dark web cybercrime discussions concentrate 75% of their volume in a small set of persistent core topics that last a median of 75 months.

arxiv:2605.15345 v1 · 2026-05-14 · cs.CR

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\pithnumber{N6EZ6AJSJIPNRRUHTAH4BSXANG}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

approximately 75% of total discussion volume is concentrated in a small set of persistent core topics, while short-lived themes account for approximately 3% of activity. The median topic lifespan is 75 months, indicating gradual thematic evolution rather than abrupt replacement.

C2weakest assumption

The longitudinal topic-modeling framework that combines domain-specific embeddings, density-based clustering and temporal aggregation correctly identifies thematic clusters and measures their prevalence and lifespan at the website level without major distortion from snapshot collection biases or hyperparameter choices.

C3one line summary

Longitudinal topic modeling on a large dark web dataset finds 75% of discussion volume in persistent core topics with a median lifespan of 75 months and only 3% in short-lived themes.

References

67 extracted · 67 resolved · 2 Pith anchors

[1] Relevance of the deep web to academic research, 2020

[2] “Tor metrics,” 2025. [Online]. Available: https:// metrics.torproject.org/ 2025

[3] A review of dark web: Trends and future directions, 2022

[4] The Dark Side of the Web: Towards Understanding Various Data Sources in Cyber Threat Intelligence , 2025

[5] J. Robertson, A. Diab, E. Marin, E. Nunes, V . Paliath, J. Shakar- ian, and P. Shakarian,Darkweb cyber threat intelligence mining. Cambridge University Press, 2017 2017

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:53.616821Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6f899f01324a1ed8c687980fc0cae069b71a49d814411a825d1cbc9d825eaf37

Aliases

arxiv: 2605.15345 · arxiv_version: 2605.15345v1 · doi: 10.48550/arxiv.2605.15345 · pith_short_12: N6EZ6AJSJIPN · pith_short_16: N6EZ6AJSJIPNRRUH · pith_short_8: N6EZ6AJS

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/N6EZ6AJSJIPNRRUHTAH4BSXANG \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6f899f01324a1ed8c687980fc0cae069b71a49d814411a825d1cbc9d825eaf37

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "2ec8eeba59bf08dfee022acefd617dd72f373e6816ea11d7989261cb13633fe6",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-14T19:14:53Z",
    "title_canon_sha256": "c5c1fd099ef8c604eb8d938da6dc661dc44d9d044e2578fcf87caee96a9c8aae"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15345",
    "kind": "arxiv",
    "version": 1
  }
}