Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches | Paper Citation Record

Observation 1891098c-3492-464d-8e4a-cba0eaf4b71d · outbound

This paper cites Table 5 shows the summary of the research using LLMs.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Table 5 shows the summary of the research using LLMs

Reference 1

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This paper cites There are significantly fewer AV-based research articles compared to AA.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches There are significantly fewer AV-based research articles compared to AA

Reference 2

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This paper cites Studies that focused solely on traditional ML techniques include Castillo et al.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Studies that focused solely on traditional ML techniques include Castillo et al

Reference 3

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This paper cites It includes the top 50 authors based on the total size of their articles, all of whom have written at least one article in the CCAT (corporate/industrial) category.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It includes the top 50 authors based on the total size of their articles, all of whom have written at least one article in the CCAT (corporate/industrial) category

Reference 4

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 5

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This paper cites For instance, Khan et al.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches For instance, Khan et al

Reference 6

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This paper cites Shao et al.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Shao et al

Reference 7

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This paper cites (2024) used LLMs for Latin AA and AV, focusing on historic language rather than modern language.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches (2024) used LLMs for Latin AA and AV, focusing on historic language rather than modern language

Reference 8

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 9

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This paper cites It has many datasets of different sub-tasks.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It has many datasets of different sub-tasks

Reference 10

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This paper cites Liu, 2006).

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Liu, 2006)

Reference 12

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This paper cites The DTs used are Essays, Emails, Text messages and Business memos.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches The DTs used are Essays, Emails, Text messages and Business memos

Reference 13

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This paper cites It was collected by the Federal Energy Regulatory Commission during their investigation into Enron’s collapse (Cohen, 2015).

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It was collected by the Federal Energy Regulatory Commission during their investigation into Enron’s collapse (Cohen, 2015)

Reference 14

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This paper cites It includes news articles from Reuters written between 1996 and.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It includes news articles from Reuters written between 1996 and

Reference 15

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This paper cites The dataset consists of articles obtained from Internet news portals, is compiled into a dataset in the Comma Separated Value (CSV) document format.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches The dataset consists of articles obtained from Internet news portals, is compiled into a dataset in the Comma Separated Value (CSV) document format

Reference 17

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This paper cites (2023) and contains over 2 million articles.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches (2023) and contains over 2 million articles

Reference 18

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This paper cites The dataset comprises a total of 10 authors and 582 samples.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches The dataset comprises a total of 10 authors and 582 samples

Reference 19

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 20

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This paper cites It was created by Khan et al.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It was created by Khan et al

Reference 21

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This paper cites From Figure 3a it is clear that from year 2017 to 2020, research predominantly utilized ML and DL techniques, with no reported use of LLMs.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches From Figure 3a it is clear that from year 2017 to 2020, research predominantly utilized ML and DL techniques, with no reported use of LLMs

Reference 23

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This paper cites Does a text retain its original authorship when it undergoes numerous paraphrasing iterations.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Does a text retain its original authorship when it undergoes numerous paraphrasing iterations

Reference 24

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This paper cites It operates by recursively dividing the dataset based on feature values, creating a tree-like structure.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It operates by recursively dividing the dataset based on feature values, creating a tree-like structure

Reference 25

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 26

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This paper cites It is capable of generating coherent text and performing tasks such as translation and summarization without task-specific training (Radford et al., 2019).

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches It is capable of generating coherent text and performing tasks such as translation and summarization without task-specific training (Radford et al., 2019)

Reference 27

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This paper cites The method used was K-means clustering.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches The method used was K-means clustering

Reference 29

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Reference 30

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This paper cites G., Bradley, H., OBrien, K., Hallahan, E., Khan, M.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches G., Bradley, H., OBrien, K., Hallahan, E., Khan, M

Reference 35

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Mixtral of Experts

Reference 37

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Reference 39

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 41

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This paper cites Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges

Reference 42

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches F., Anwar, W., Arshad, H., & Abbas, S

Reference 44

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Reference 47

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This paper cites CAVE: Controllable Authorship Verification Explanations.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches CAVE: Controllable Authorship Verification Explanations

Reference 50

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This paper cites Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin

Reference 52

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This paper cites On the Limitations of Large Language Models (LLMs): False Attribution.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches On the Limitations of Large Language Models (LLMs): False Attribution

Reference 93

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches T5 meets Tybalt: Author Attribution in Early Modern English Drama Using Large Language Models

Reference 131

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Reference 190

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Reference 297

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Reference 348

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Reference 435

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Gemini: A Family of Highly Capable Multimodal Models

Reference 838

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Reference 1787

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Reference 1997

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Reference 2004

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This paper cites • Gemini: Gemini, developed by Google DeepMind and launched on December 6, 2023, is a multi-modal large language model.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches • Gemini: Gemini, developed by Google DeepMind and launched on December 6, 2023, is a multi-modal large language model

Reference 2023

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This paper cites Due to inclusion and exclusion criteria mentioned in Section 2, no articles from 2015 and 2016 made it into the review as shown in Table C3 and Figure.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Due to inclusion and exclusion criteria mentioned in Section 2, no articles from 2015 and 2016 made it into the review as shown in Table C3 and Figure

Reference 2024

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches R., Cohen, T., & McGill, S

Reference 2207

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Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches GPT-4 Technical Report

Reference 2700

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Reference 4589

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d202c480-76e6-41ae-a510-e47b40ab2b79 · outbound

This paper cites an unresolved cited work.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 7255

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:32.785127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20cb3c31-0feb-4483-ad7d-8e3039fad52f · outbound

This paper cites an unresolved cited work.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 7518

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:20:30.836127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72aafb80-c4b5-48c9-bf13-2911e0317702 · outbound

This paper cites an unresolved cited work.

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches Unresolved cited work

Reference 9071

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T15:20:30.043829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers