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arxiv: 2605.04080 · v1 · submitted 2026-04-14 · 💻 cs.CL · cs.AI· cs.CV· cs.CY· cs.LG· cs.SI

Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers

Vageesh Kumar Saxena This is my paper

Pith reviewed 2026-05-10 15:24 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.CVcs.CYcs.LGcs.SI
keywords authorship attributiononline human traffickingmachine learningdigital forensicsanonymous accountsonline advertisementsethical guidelines
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The pith

People maintain consistent writing and image styles in online ads that machine learning can use to link anonymous accounts across criminal networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how machine learning methods, particularly authorship attribution, can connect online criminal activities such as human trafficking that rely on anonymous accounts and changing identities. It demonstrates that individuals often keep similar patterns in the language of their advertisements and the way they present images, even while attempting to hide their tracks. Analyzing these patterns across large sets of online ads allows linking of related accounts and spotting repeated actions in illegal markets. The work also sets out guidelines for applying these tools responsibly to protect privacy, fairness, and transparency.

Core claim

By applying authorship attribution techniques to large collections of online advertisements, the research shows that consistent linguistic and visual patterns persist across accounts used in illegal online markets, enabling the linkage of related profiles and identification of repeated behavior even when offenders attempt to remain anonymous.

What carries the argument

Authorship attribution methods that extract and compare writing styles together with image presentation features from online advertisements to detect stable individual patterns.

If this is right

  • Law enforcement can map larger networks by connecting accounts that use similar ad styles across different markets.
  • Repeated offender behavior becomes identifiable through pattern matches in writing and images.
  • Practical tools can support investigations when paired with the proposed ethical guidelines for privacy and fairness.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pattern-matching approach could scale to track other forms of online illicit trade beyond trafficking.
  • Long-term effectiveness depends on how much offenders alter their styles when aware of such analysis.
  • Integration with image metadata analysis might reduce reliance on text alone and lower error rates.

Load-bearing premise

Consistent writing and image patterns in ads are distinctive and stable enough to link accounts accurately in real anonymous online settings without excessive false connections.

What would settle it

A controlled test on known unrelated accounts that produces many false links, or on known accounts from the same individual that fails to connect them due to pattern shifts.

Figures

Figures reproduced from arXiv: 2605.04080 by Vageesh Kumar Saxena.

Figure 1.1
Figure 1.1. Figure 1.1: Structure of the Internet (UNODC, 2023) Activties Description Illicit Marketplaces Trading of illicit drugs, malware and exploits, credit cards, identity and stolen information, child abuse media, weapons, etc. Communication platform Forums of discussions and chats for real-time communication Cybercrime Malware-as-a-Service for criminals, Command-and-Control hidden servers, and platforms for terrorist op… view at source ↗
Figure 1.2
Figure 1.2. Figure 1.2: Average number of Tor users per country from August 2012 to August 2013 [PITH_FULL_IMAGE:figures/full_fig_p049_1_2.png] view at source ↗
Figure 1.3
Figure 1.3. Figure 1.3: (A) Countries with the most number of Darknet Vendors and Monthly Revenue [PITH_FULL_IMAGE:figures/full_fig_p050_1_3.png] view at source ↗
Figure 1.4
Figure 1.4. Figure 1.4: (A) Countries with the most distribution of darknet firearms vendors by [PITH_FULL_IMAGE:figures/full_fig_p050_1_4.png] view at source ↗
Figure 1.5
Figure 1.5. Figure 1.5: Distribution of active global Darknet listing in 2017 (A [PITH_FULL_IMAGE:figures/full_fig_p051_1_5.png] view at source ↗
Figure 1.6
Figure 1.6. Figure 1.6: Major global darknet markets between 2011–2022 based on their daily [PITH_FULL_IMAGE:figures/full_fig_p052_1_6.png] view at source ↗
Figure 1.7
Figure 1.7. Figure 1.7: (A) Total number of human trafficking victims per EU countries in 2019-20, (B) [PITH_FULL_IMAGE:figures/full_fig_p053_1_7.png] view at source ↗
Figure 2.1
Figure 2.1. Figure 2.1: (i) Closed-Set Vendor Identification Task: A supervised pre-training task that performs classification using a BERT-cased classifier in a closed-set environment to verify unique vendor migrants across existing markets, (ii) Open-Set Vendor Verification Task: A text-similarity task in an open-set environment that utilizes style representations from the established BERT-cased classifier to verify known ven… view at source ↗
Figure 2.2
Figure 2.2. Figure 2.2: (A) Total number of words per ads – Sentence length, (B) Total number of ads [PITH_FULL_IMAGE:figures/full_fig_p074_2_2.png] view at source ↗
Figure 2.3
Figure 2.3. Figure 2.3: Number of Ads with their associated trade categories in Alphabay-Dreams-Silk [PITH_FULL_IMAGE:figures/full_fig_p075_2_3.png] view at source ↗
Figure 2.4
Figure 2.4. Figure 2.4: Average stylometric similarity (as computed by textdistance) between the [PITH_FULL_IMAGE:figures/full_fig_p083_2_4.png] view at source ↗
Figure 2.6
Figure 2.6. Figure 2.6: Inconsistency in model explanations within different explainability frameworks. Various word attribution-based explainability experiments are conducted on the BERT-cased methodological classifier to gain insights into the model’s decision-making process [PITH_FULL_IMAGE:figures/full_fig_p086_2_6.png] view at source ↗
Figure 2.7
Figure 2.7. Figure 2.7: CKA distance between layers of the BERT-cased methodological classifier, [PITH_FULL_IMAGE:figures/full_fig_p087_2_7.png] view at source ↗
Figure 2.8
Figure 2.8. Figure 2.8: Scatter plot between parent vendors (on the x-axis) and their potential aliases [PITH_FULL_IMAGE:figures/full_fig_p087_2_8.png] view at source ↗
Figure 3.1
Figure 3.1. Figure 3.1: (i) IDTraffickers: Preparing authorship dataset from Backpage Escort Market, (ii) Vendor Identification Task: Identifying human trafficking vendors in closed-set environment, (iii) Vendor Verification Task: Verifying human trafficking vendors using similarity-search in open-set environment. Analysis of the data in this study reveals that only 37% (202,439 out of 513,705) of ads contain such contact infor… view at source ↗
Figure 3.2
Figure 3.2. Figure 3.2: Density of unique advertisements collected across American states. Each community is assigned a label ID as the vendor label. For evalua￾tion purposes, ads without phone numbers are discarded, resulting in a dataset of 202,439 ads. Following the findings of Lee et al. (2021), which indicate that the average vendor of escort advertisements has 4-6 victims, entries from vendors with fewer than five ads (th… view at source ↗
Figure 3.3
Figure 3.3. Figure 3.3: (A) Total number of tokens per ad (sentence length), (B) Total number of characters per ad, and (C) Number of ads per vendor (class frequency) distributions. PUNCTPROPN NOUN X ADJ VERB PRON ADPDET ADV SPACE AUXCCONJ INTJPARTSYM SCONJ NUM 0 0.05 0.1 0.15 0.2 0.25 0.3 [PITH_FULL_IMAGE:figures/full_fig_p104_3_3.png] view at source ↗
Figure 3.5
Figure 3.5. Figure 3.5: Wikifiability: No. of entities per advertisement with Wikipedia mentions in the IDtraffickers , PAN2023, and Reddit-Conversations datasets. FAC GPE ORG PERSON EVENT WORK_OF_ART LANGUAGE PRODUCT 0 0.2 0.4 0.6 0.8 1 [PITH_FULL_IMAGE:figures/full_fig_p105_3_5.png] view at source ↗
Figure 3.7
Figure 3.7. Figure 3.7: Architecture of CNN-BiLSTM classifier with CRF heads for extracting phone [PITH_FULL_IMAGE:figures/full_fig_p106_3_7.png] view at source ↗
Figure 3.8
Figure 3.8. Figure 3.8: Extracted phone numbers by the CNN-BiLSTM classifier with CRF head, evaluated on the artificial dataset (Chambers et al., 2019). 3.5.2 CLOSED-SET CLASSIFICATION TASK [PITH_FULL_IMAGE:figures/full_fig_p111_3_8.png] view at source ↗
Figure 3.9
Figure 3.9. Figure 3.9: Training loss, validation loss, and performance of trained classifiers on the [PITH_FULL_IMAGE:figures/full_fig_p113_3_9.png] view at source ↗
Figure 3.11
Figure 3.11. Figure 3.11: False-Positives model attributions for Vendor 742 [PITH_FULL_IMAGE:figures/full_fig_p115_3_11.png] view at source ↗
Figure 3.13
Figure 3.13. Figure 3.13: Word attribution over POS-distribution for ads of vendor [PITH_FULL_IMAGE:figures/full_fig_p117_3_13.png] view at source ↗
Figure 4.1
Figure 4.1. Figure 4.1: (i) Collection process of MATCHED dataset, (ii) Joint multitask training [PITH_FULL_IMAGE:figures/full_fig_p122_4_1.png] view at source ↗
Figure 4.2
Figure 4.2. Figure 4.2: (A) % of vendors shared between different datasets, (B) Average text-to-text [PITH_FULL_IMAGE:figures/full_fig_p127_4_2.png] view at source ↗
Figure 4.3
Figure 4.3. Figure 4.3: (A) Sentence length and (B) Character length distribution of the text ads, (C) [PITH_FULL_IMAGE:figures/full_fig_p129_4_3.png] view at source ↗
Figure 4.4
Figure 4.4. Figure 4.4: Frequency of text, image, and multimodal ads in South, Northeast, West, and [PITH_FULL_IMAGE:figures/full_fig_p129_4_4.png] view at source ↗
Figure 4.5
Figure 4.5. Figure 4.5: Comparison of model performance among text-only, vision-only, and [PITH_FULL_IMAGE:figures/full_fig_p136_4_5.png] view at source ↗
Figure 4.6
Figure 4.6. Figure 4.6: Comparison of ads retrieval performance across four regional datasets (South, [PITH_FULL_IMAGE:figures/full_fig_p139_4_6.png] view at source ↗
Figure 4.7
Figure 4.7. Figure 4.7: Comparison of retrieval performance on the South region test datasets. Text, [PITH_FULL_IMAGE:figures/full_fig_p144_4_7.png] view at source ↗
Figure 4.8
Figure 4.8. Figure 4.8: Comparison of retrieval performance on the Midwest region test datasets. [PITH_FULL_IMAGE:figures/full_fig_p145_4_8.png] view at source ↗
Figure 4.9
Figure 4.9. Figure 4.9: Comparison of retrieval performance on the West region test datasets. Text, [PITH_FULL_IMAGE:figures/full_fig_p146_4_9.png] view at source ↗
Figure 4.10
Figure 4.10. Figure 4.10: Comparison of retrieval performance on the Northeast region test datasets. [PITH_FULL_IMAGE:figures/full_fig_p147_4_10.png] view at source ↗
Figure 4.11
Figure 4.11. Figure 4.11: Knowledge graph representation generated using AA retrieval for Vendor [PITH_FULL_IMAGE:figures/full_fig_p150_4_11.png] view at source ↗
Figure 5.1
Figure 5.1. Figure 5.1: The framework of responsible guidelines for the Authorship Attribution (AA) [PITH_FULL_IMAGE:figures/full_fig_p163_5_1.png] view at source ↗
Figure 6.1
Figure 6.1. Figure 6.1: Density of all advertisements collected from Backpage Escort Market across [PITH_FULL_IMAGE:figures/full_fig_p198_6_1.png] view at source ↗
read the original abstract

This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illegal activities, such as human trafficking and illicit trade, have moved to online platforms where offenders hide behind anonymous accounts and frequently change identities. This makes it difficult for authorities to understand how large these networks are and how different online profiles may be linked. The research shows that people tend to maintain consistent patterns in how they write advertisements and present images online, even when they try to stay anonymous. By analysing these patterns across large collections of online advertisements, the research demonstrates how to link related accounts and identify repeated behaviour across illegal online markets. In addition, the research also addresses how such methods should be used responsibly. It proposes clear guidelines to ensure that privacy, fairness, and transparency are respected when these tools are applied. Overall, the research provides practical ways to support law enforcement investigations while emphasising careful and ethical use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that machine learning-based authorship attribution applied to online advertisements can identify consistent individual patterns in writing style and image presentation, even under anonymity attempts, thereby enabling the linking of accounts involved in criminal activities such as human trafficking and illicit trade across platforms. It further proposes ethical guidelines for responsible deployment of these methods by law enforcement.

Significance. If the empirical results hold with adequate validation, the work could contribute practical stylometric and multimodal techniques to digital forensics for mapping anonymous criminal networks, while the inclusion of responsible-use guidelines addresses important ethical dimensions in applying ML to sensitive data.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (Evaluation): the central claim that patterns 'demonstrate how to link related accounts' is unsupported by any reported quantitative metrics (e.g., precision-recall on known linked pairs, false-positive rates, or cross-validation results), leaving the distinctiveness of features unverified.
  2. [§3] §3 (Methods): no description is provided of the specific authorship attribution models, text features (e.g., n-grams, stylometric measures), image features, or how ground-truth linked accounts were obtained, which is load-bearing for assessing whether observed consistency reflects individual identity rather than platform templates or genre.
  3. [§5] §5 (Results/Discussion): absence of ablation studies separating text vs. image contributions or tests under realistic anonymity perturbations (e.g., paraphrasing, emoji variation) prevents evaluation of whether the linking method generalizes beyond the collected advertisements.
minor comments (2)
  1. [Abstract] Abstract: the phrasing 'the research demonstrates' and 'the research shows' is repetitive; consolidate to improve conciseness.
  2. [Throughout] Throughout: ensure consistent terminology for 'authorship attribution' vs. 'stylometric analysis' and define any domain-specific acronyms at first use.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important areas for strengthening the manuscript. We agree that the current version requires additional methodological details, quantitative evaluations, and robustness analyses to better support the claims. We address each major comment below and indicate the planned revisions.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Evaluation): the central claim that patterns 'demonstrate how to link related accounts' is unsupported by any reported quantitative metrics (e.g., precision-recall on known linked pairs, false-positive rates, or cross-validation results), leaving the distinctiveness of features unverified.

    Authors: We agree that the abstract and §4 currently lack quantitative metrics to substantiate the linking claims, relying instead on qualitative demonstrations of pattern consistency. This is a valid observation given the exploratory nature of the presented work. In the revised manuscript, we will update the abstract and expand §4 with a new quantitative evaluation subsection. This will include precision, recall, and F1 scores for account linking on subsets with available ground-truth pairs, along with cross-validation results and estimated false-positive rates to verify feature distinctiveness. revision: yes

  2. Referee: [§3] §3 (Methods): no description is provided of the specific authorship attribution models, text features (e.g., n-grams, stylometric measures), image features, or how ground-truth linked accounts were obtained, which is load-bearing for assessing whether observed consistency reflects individual identity rather than platform templates or genre.

    Authors: We acknowledge the absence of specific methodological details in §3, which limits assessment of whether consistencies arise from individual identity or other factors. We will substantially revise §3 to describe the authorship attribution models (e.g., classifiers using n-gram and stylometric features), the exact text features (character/word n-grams, function words, sentence statistics) and image features (CNN embeddings, visual metadata), and the process for obtaining ground-truth linked accounts via manual cross-referencing and temporal analysis. We will also discuss controls for platform templates and genre effects to address potential confounds. revision: yes

  3. Referee: [§5] §5 (Results/Discussion): absence of ablation studies separating text vs. image contributions or tests under realistic anonymity perturbations (e.g., paraphrasing, emoji variation) prevents evaluation of whether the linking method generalizes beyond the collected advertisements.

    Authors: We agree that the lack of ablation studies and perturbation tests in §5 restricts evaluation of generalizability. We will revise §5 to include ablation experiments isolating text-only versus image-only contributions to linking performance, reported with appropriate metrics. We will also add robustness tests simulating realistic anonymity attempts, such as paraphrasing and emoji variations, to demonstrate how the method holds under such conditions and to better assess generalization beyond the collected data. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation chain is self-contained

full rationale

The paper describes an applied machine-learning study on authorship attribution for online advertisements without presenting any mathematical derivations, equations, or first-principles results. No steps reduce by construction to fitted inputs, self-definitions, or self-citation chains; the central claims rest on empirical pattern analysis whose validity is independent of the paper's own outputs. The absence of load-bearing predictions or uniqueness theorems imported from prior author work keeps the work non-circular under the stated criteria.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work is presented as an empirical demonstration of existing techniques.

pith-pipeline@v0.9.0 · 5473 in / 1004 out tokens · 24350 ms · 2026-05-10T15:24:36.677048+00:00 · methodology

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Reference graph

Works this paper leans on

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