Scaling Up Authorship Attribution (2024)

Abstract: We describe our system for authorship attribution in the IARPA HIATUS program. We describe the model and compute infrastructure developed to satisfy the set of technical constraints imposed by IARPA, including runtime limits as well as other constraints related to the ultimate use case. One use-case constraint concerns the explainability of the features used in the system. For this reason, we integrate features from frame semantic parsing, as they are both interpretable and difficult for adversaries to evade. One trade-off with using such features, however, is that more sophisticated feature representations require more complicated architectures, which limit usefulness in time-sensitive and constrained compute environments. We propose an approach to increase the efficiency of frame semantic parsing through an analysis of parallelization and beam search sizes. Our approach results in a system that is approximately 8.37x faster than the base system with a minimal effect on accuracy.

## introduction authorship attribution aims to identify the correct author of a document. the iarpa human interpretable attribution of text using underlying structure (hiatus) program looks to develop novel methods to address several of the current limitations of authorship attribution, with specific consideration given to explainability, higher linguistic features, generalizability, and privacy preservation. in information warfare, operatives adopt local writing patterns in order to infiltrate and influence populations and manipulate legitimate political discourse. while they assume the local patois and customs of the target audiences they are trying to infiltrate, humans cannot completely erase all traces of higher-order authorship characteristics (e.g., socialization, education, culture, and characteristics of their native language). our system is designed to detect such infiltrators, but should also allow distribution of information without revealing the source. while this approach is tailored towards meeting the hiatus requirements, these requirements translate into realistic scenarios. for this reason, we assume that our solutions will be usable in real world applications, with additional consideration given to external factors (e.g. costs, time, deployability). thus, any lessons learned from its development are directly applicable to any related production-level system that uses the same features or focuses on similar tasks. an overall system should be composed of three interconnected modules: feature generation, authorship attribution, and privacy preservation, with the latter two possessing explainability requirements. an underlying challenge in our applications is that any software must be usable by the intelligence community, which entails strict technical restrictions. for this reason, hiatus systems are required to be fully contained and executable within a pre-defined set of environments as defined by iarpa. this requires a balance between delivering a highly effective solution and maintaining the ability to deploy our solution within the bounds of the technical constraints. here, we focus on authorship attribution. we evaluate the speed of the overall attribution system, and specifically optimize one of the models for creating features derived from a semantic parser, since the parser requires several passes over a sentence, among other limitations, and thus does not scale sufficiently. ## related work authorship analysis refers to a range of tasks that require modeling language use with the goal of grouping texts together that are written by the same author and discriminating among texts that are written by different authors @xcite . in order to facilitate authorship analysis, recent work has focused on extracting authorship embedding representations from documents, often leveraging large language models @xcite @xcite or other deep learning models @xcite @xcite to aggregate authorship information. while capturing sophisticated representations, the black-box nature of the models do not make them as inherently usable when explainability is required. this has resulted in work developing explainable authorship embeddings @xcite where each component corresponds to an interpretable feature. the two most common authorship analysis subtasks are authorship attribution and authorship verification. ## conclusion and future work we detailed our system for authorship attribution within the hiatus environment, which has an infrastructure that poses overhead limits to the compute and memory configurations of the specific task of creating authorship embeddings. given these constraints, we highlighted our computational efficiency modifications to the frame semantic parser in order to be able to integrate frame semantic linguistic features into our authorship attribution model. our architecture generates authorship embeddings that are successfully evaluated on the test & evaluation harness, while resulting in negligible performance reduction. the integrated modifications are directly applicable to any other resource constrained production system focused on authorship attribution or using similar features. future modifications will include more robust parallelization of the feature generation ability in addition to an optimization step that includes automating the docker image creation for the task and submission to the hiatus evaluation infrastructure.