1 research outputs found
POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis
Authorship verification (AV) is a fundamental research task in digital text
forensics, which addresses the problem of whether two texts were written by the
same person. In recent years, a variety of AV methods have been proposed that
focus on this problem and can be divided into two categories: The first
category refers to such methods that are based on explicitly defined features,
where one has full control over which features are considered and what they
actually represent. The second category, on the other hand, relates to such AV
methods that are based on implicitly defined features, where no control
mechanism is involved, so that any character sequence in a text can serve as a
potential feature. However, AV methods belonging to the second category bear
the risk that the topic of the texts may bias their classification predictions,
which in turn may lead to misleading conclusions regarding their results. To
tackle this problem, we propose a preprocessing technique called POSNoise,
which effectively masks topic-related content in a given text. In this way, AV
methods are forced to focus on such text units that are more related to the
writing style. Our empirical evaluation based on six AV methods (falling into
the second category) and seven corpora shows that POSNoise leads to better
results compared to a well-known topic masking approach in 34 out of 42 cases,
with an increase in accuracy of up to 10%.Comment: Paper has been accepted for publication in: The 16th International
Conference on Availability, Reliability and Security (ARES 2021