Overview of PAN 2018 - Author Identification, Author Profiling, and Author Obfuscation

@inproceedings{Stamatatos2018OverviewOP,
  title={Overview of PAN 2018 - Author Identification, Author Profiling, and Author Obfuscation},
  author={Efstathios Stamatatos and Francisco Manuel Rangel Pardo and Michael Tschuggnall and Benno Stein and Mike Kestemont and Paolo Rosso and Martin Potthast},
  booktitle={CLEF},
  year={2018}
}
PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive approaches and advancing research in digital text forensics. [] Key Method In addition, a shared task in multimodal author profiling examines, for the first time, a combination of information from both texts and images posted by social media users to estimate their gender.

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References

SHOWING 1-10 OF 41 REFERENCES

Overview of the Author Identification Task at PAN 2013

TLDR
The author identification task at PAN-2014 focuses on author verification and adopts the c@1 measure, originally proposed for the question answering task, and continues the successful practice of the PAN labs to examine meta-models based on the combination of all submitted systems.

Blogs, Twitter Feeds, and Reddit Comments: Cross-domain Authorship Attribution

TLDR
It is determined that state-of-the-art methods in stylometry do not perform as well in cross- domain situations as they do in in-domain situations and methods are proposed that improve performance in the cross-domain setting with both feature and classification level techniques which can increase accuracy to up to 70%.

An Overview of the Traditional Authorship Attribution Subtask

TLDR
This paper describes the Traditional Authorship Attribution subtask of the PAN/CLEF 2012 workshop, and established a new corpus for analysis for 2012 (Rome), which consisted of eight problems, including three closed-class authorship attribution problems, three open-class (the set of correct answers included Ònone of the aboveÓ), and two clustering problems.

Overview of the Author Obfuscation Task at PAN 2017: Safety Evaluation Revisited

TLDR
There is still way to go to “perfect” automatic obfuscation that (1) tricks verification approaches, (2) keeps the meaning of the original, and (3) is, regarding its obfuscation, unsuspicious to a human eye.

Authorship Attribution Using Text Distortion

TLDR
A novel method is presented that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures to mask topic-specific information that is not related to the personal style of authors.

On the Robustness of Authorship Attribution Based on Character N -gram Features

TLDR
Comparative results with another competitive text representation approach based on very frequent words show that character n-grams are better able to capture stylistic properties of text when there are significant differences among the training and test corpora.

Cross-Genre Authorship Verification Using Unmasking

In this paper we will stress-test a recently proposed technique for computational authorship verification, ‘‘unmasking'', which has been well received in the literature. The technique envisages an

Not All Character N-grams Are Created Equal: A Study in Authorship Attribution

TLDR
It is demonstrated that characterngrams that capture information about affixes and punctuation account for almost all of the power of character n-grams as features.

Cross-Language Authorship Attribution

TLDR
A number of cross-language stylometric features, such as those based on sentiment and emotional markers, are proposed for the task of CLAA, and an approach based on machine translation (MT) with both lexical and cross- language features is explored.

Overview of the 3rd Author Profiling Task at PAN 2015

TLDR
The framework and the results for the Author Profiling Shared Task organised at PAN 2015 are overviewed, which aims at identifying age, gender, and personality traits of Twitter users.