Many approaches have been proposed recently to identify the author of a given document. Thereby, one fact is often silently assumed: i.e., that the given document is indeed written by only author. For a realistic author identification system it is therefore crucial to at first determine whether a document is single- or multiauthored.

To this end, previous PAN editions aimed to analyze multi-authored documents. As it has been shown that it is a hard problem to reliably identify individual authors and their contribution within a single document (Author Diarization, 2016; Style Breach Detection, 2017), last year's task substantially relaxed the problem by asking only for binary decision (single- or multi-authored). Considering the promising results achieved by the submitted approaches (see the overview paper for details), we continue last year's task and additionally ask participants to predict the number of involved authors.

Given a document, participants thus should apply intrinsic style analyses to hierarchically answer the following questions:

Is the document written by one or more authors, i.e., do style changes exist or not?

If it is multi-authored, how many authors have collaborated?

All documents are provided in English and may contain zero up to arbitrarily many style changes, resulting from arbitrarily many authors.

The training set: contains 50% of the whole dataset and includes solutions. Use this set to feed/train your models.

Like last year, the whole data set is based on user posts from various sites of the StackExchange network, covering different topics and containing approximately 300 to 2000 tokens per document.

For each problem instance X, two files are provided:

problem-X.txt contains the actual text

problem-X.truth contains the ground truth, i.e., the correct solution in JSON format:

{ "authors": number_of_authors, "structure": [author_segment_1, ..., author_segment_3], "switches": [ character_pos_switch_segment_1, ..., character_pos_switch_segment_n, ] }

An example for a multi-author document could look as follows:

{ "authors": 4, "structure": ["A1", "A2", "A4", "A2", "A4", "A2", "A3", "A2", "A4"], "switches": [805, 1552, 2827, 3584, 4340, 5489, 7564, 8714] }

whereas a single-author document would have exactly the following form:

{ "authors": 1, "structure": ["A1"], "switches": [] }

Note that authors within the structure correspond only to the respective document, i.e., they are not the same over the whole dataset. For example, author A1 in document 1 is most likely not the same author as A1 in document 2 (it could be, but as there are hundreds of authors the chances are very small that this is the case). Further, please consider that the structure and the switches are provided only as additional resources for the development of your algorithms, i.e., they are not expected to be predicted.

To tackle the problem, you can develop novel approaches, extend existing algorithms from last year's task or adapt approaches from related problems such as intrinsic plagiarism detection or text segmentation. You are also free to additionally evaluate your approaches on last year's training/validation/test dataset (for the number of authors use the corresponding meta data).

This is the dataset for the shared task on Multi-Author Writing Style Analysis PAN@CLEF2025. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.

Task

The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Hence, a fundamental question is the following: If multiple authors together have written a text, can we find evidence for this fact; do we have a means to detect variations in the writing style? Answering this question belongs to the most difficult and most interesting challenges in author identification: Style change detection is the only means to detect plagiarism in a document if no comparison texts are given; likewise, style change detection can help to uncover gift authorships, to verify a claimed authorship, or to develop new technology for writing support.

Previous editions of the multi-author writing style analysis task aim at e.g., detecting whether a document is single- or multi-authored (2018), the actual number of authors within a document (2019), whether there was a style change between two consecutive paragraphs (2020, 2021, 2022), and where the actual style changes were located (2021, 2022). In 2022, style changes also had to be detected on the sentence level. The previously used datasets exhibited high topic diversity, which allowed the participants to leverage topic information as a style change signal. In the 2023 and 2024 editions of the writing style analysis task, special attention is paid to this issue.

We ask participants to solve the following intrinsic style change detection task: for a given text, find all positions of writing style change on the sentence-level (i.e., for each pair of consecutive sentences, assess whether there was a style change). The simultaneous change of authorship and topic will be carefully controlled and we will provide participants with datasets of three difficulty levels:

1. Easy: The sentences of a document cover a variety of topics, allowing approaches to make use of topic information to detect authorship changes.
2. Medium: The topical variety in a document is small (though still present) forcing the approaches to focus more on style to effectively solve the detection task.
3. Hard: All sentences in a document are on the same topic.

All documents are provided in English and may contain an arbitrary number of style changes. However, style changes may only occur between sentences (i.e., a single sentence is always authored by a single author and contains no style changes).

Data

To develop and then test your algorithms, three datasets including ground truth information are provided (easy for the easy task, medium for the medium task, and hard for the hard task).

Each dataset is split into three parts:

1. training set: Contains 70% of the whole dataset and includes ground truth data. Use this set to develop and train your models.
2. validation set: Contains 15% of the whole dataset and includes ground truth data. Use this set to evaluate and optimize your models.
3. test set: Contains 15% of the whole dataset, no ground truth data is given. This set is used for evaluation.

You are free to use additional external data for training your models. However, we ask you to make the additional data utilized freely available under a suitable license.