Shared Tasks on Authorship Analysis at PAN 2020

Bevendorff, Janek; Ghanem, Bilal; Giachanou, Anastasia; Kestemont, Mike; Manjavacas, Enrique; Potthast, Martin; Rangel, Francisco; Rosso, Paolo; Specht, Günther; Stamatatos, Efstathios; Stein, Benno; Wiegmann, Matti; Zangerle, Eva

doi:10.1007/978-3-030-45442-5_66

Cited by 10 publications

(5 citation statements)

References 16 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This could suggest that the model is using features inherent to political speech to assign class labels, as opposed to some inherent writing style linked to political affiliation. The issue of generalisation has also been addressed in the PAN (a long-running series of tasks and events focusing on text exploration and classification) 2020 task, where fandoms of fan-fiction were varied in an cross-domain authorship verificationtask (Bevendorff et al, 2020).…”

Section: Generalisabilitymentioning

confidence: 99%

Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

Holmes

Cribbin

Ferenczi

2023

Computers in Human Behavior Reports

View full text Add to dashboard Cite

Section: Generalisabilitymentioning

confidence: 99%

Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

Holmes

Cribbin

Ferenczi

2023

Computers in Human Behavior Reports

View full text Add to dashboard Cite

“…These advancements have led to applications in style representational approaches (Hay et al, 2020;Zhu and Jurgens, 2021;Wegmann et al, 2022), which currently represent the state-of-the-art (SOTA) for authorship tasks. Consequently, several datasets (Conneau and Kiela, 2018;Andrews and Bishop, 2019;Bevendorff et al, 2020Bevendorff et al, , 2023 have been established to facilitate further research in this area.…”

Section: Authorship Attribution In Nlpmentioning

confidence: 99%

IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements

Saxena,

Ashpole,

van Dijck

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that many HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators 1 .

show abstract

“…Bevendorff et al [14] provided a brief discussion on the results of the four shared tasks arranged at the PAN 2020 [15] lab on digital text forensics and stylometry. This involved author profiling, cross-domain author verification, celebrity profiling, and style change detection.…”

Section: Related Workmentioning

confidence: 99%

Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

et al. 2022

View full text Add to dashboard Cite

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.

show abstract

Shared Tasks on Authorship Analysis at PAN 2020

Cited by 10 publications

References 16 publications

Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements

Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

Contact Info

Product

Resources

About