TripAdvisor AV Corpus

Summary:------------------- The "TripAdvisor AV Corpus" represents an authorship verification (AV) corpus derived from the "Webis-Tripad-13-Sentiment Dataset", released by Wachsmuth et al. ("A Review Corpus for Argumentation Analysis"). It was transformed into the same standardized format used by the PAN Authorship Verification corpora from 2013–2015. With only minor modifications, it is also compatible with the PAV AV corpora released between 2020 and 2022. This corpus aims to support the research community in Digital Text Forensics by providing a shared resource for benchmarking and comparing AV methods.

Structure: ------------------- The dataset comprises 600 AV cases in total, split into training and test partitions containing 120 and 480 cases, respectively. Both partitions are strictly balanced with respect to same-authorship and different-authorship instances. Each AV case includes two plain-text documents: a reference text produced by the known author and a questioned text attributed to an unknown author whose authorship is to be verified. The document lengths vary between approximately 0.2 KB and 8 KB.

Preprocessing: ------------------- Since the original dataset already consists of reviews of reasonably high quality, only limited preprocessing was applied. This included correcting spacing inconsistencies around punctuation marks (e.g., " (alight" → "(alight"), as well as normalizing excessive character repetitions to a maximum of three characters (e.g., "(!!!!!" → "!!!"). In addition, reviews containing an excessive number of named entities were removed, as well as authors for which only a single review was available. Subsequently, topic masking was applied to all texts using the POSNoise library in order to preserve stylistically relevant textual units, such as punctuation marks, function words and interjections, while replacing topic/content-related words with POS-tag-like placeholders (see Table 2 in the original POSNoise paper, "POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis"). Finally, the filenames of the underlying texts were anonymized to ensure compliance with GDPR requirements.

Paper: ------------------- The "TripAdvisor AV Corpus" is also described in the supplementary materials (see Section 1.7) of the paper: Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification

Citing the Corpus:------------------- If you use this corpus in your research, please cite the following paper:

Andrea Nini, Oren Halvani, Lukas Graner, Sophie Titze, Valerio Gherardi and Shunichi Ishihara. Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification. Humanities and Social Sciences Communications (Nature) 13, 455 (2026).

Bibtex:

@Article{NiniLambdaG:2026,    author = {Nini, Andrea and Halvani, Oren and Graner, Lukas and Titze, Sophie and Gherardi, Valerio and Ishihara, Shunichi},    title = {{Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification}},    journal = {Humanities and Social Sciences Communications},    year = {2026},    month = {Mar},    day = {03},    volume = {13},    number = {1},    pages = {455},    abstract = {Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were written by the same person. Numerous computational approaches have been proposed over the last two decades in an attempt to address this challenge. However, existing AV methods often suffer from high complexity, low explainability, and especially from a lack of clear scientific justification. We propose a simpler method based on modeling the grammar of an author following Cognitive Linguistics principles. These models are used to calculate $\lambda$G (LambdaG): the ratio of the likelihoods of a document given the candidate's grammar versus given a reference population's grammar. Our empirical evaluation, conducted on 12 datasets and compared against seven baseline methods, demonstrates that LambdaG achieves superior performance, including against several neural network-based AV methods. LambdaG is also robust to small variations in the composition of the reference population and provides interpretable visualizations, enhancing its explainability. We argue that its effectiveness is due to the method's compatibility with Cognitive Linguistics theories, predicting that a person's grammar is a behavioral biometric.},    issn = {2662-9992},    doi = {10.1057/s41599-025-06340-3},    url = {https://doi.org/10.1057/s41599-025-06340-3}}