Similarity Learning for Authorship Verification in Social Media

Boenninghoff, Benedikt; Nickel, Robert M.; Zeiler, Steffen; Kolossa, Dorothea

doi:10.1109/icassp.2019.8683405

Cited by 29 publications

(22 citation statements)

References 28 publications

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“…These three algorithms have been ranked first, second and third in a performance evaluation on a small-sized corpus of larger Amazon reviews conducted by [38]. In addition, we also considered our predecessor HRSN [27]. Table I summarizes the average verification error rates for the dataset described in Section III with a 5-fold cross-validation.…”

Section: B Baseline Methodsmentioning

confidence: 99%

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Explainable Authorship Verification in Social Media via Attention-based Similarity Learning

Boenninghoff

Hessler

Kolossa

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Authorship verification is the task of analyzing the linguistic patterns of two or more texts to determine whether they were written by the same author or not. The analysis is traditionally performed by experts who consider linguistic features, which include spelling mistakes, grammatical inconsistencies, and stylistics for example. Machine learning algorithms, on the other hand, can be trained to accomplish the same, but have traditionally relied on so-called stylometric features. The disadvantage of such features is that their reliability is greatly diminished for short and topically varied social media texts. In this interdisciplinary work, we propose a substantial extension of a recently published hierarchical Siamese neural network approach, with which it is feasible to learn neural features and to visualize the decision-making process. For this purpose, a new large-scale corpus of short Amazon reviews for text comparison research is compiled and we show that the Siamese network topologies outperform state-of-the-art approaches that were built up on stylometric features. Our linguistic analysis of the internal attention weights of the network shows that the proposed method is indeed able to latch on to some traditional linguistic categories.

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Section: B Baseline Methodsmentioning

confidence: 99%

“…II. ATTENTION-BASED SIAMESE NETWORK TOPOLOGY With our ADHOMINEM approach we propose a significant extension to our model introduced in [27]. Its Siamese topology consists of two identical neural networks that share the exact same set of parameters.…”

Section: Introductionmentioning

confidence: 99%

Explainable Authorship Verification in Social Media via Attention-based Similarity Learning

Boenninghoff

Hessler

Kolossa

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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“…Boenningho et al [10] proposed a new neural network topology to identify whether two documents with unknown authors were wri en by the same author.…”

Section: State-of-the-artmentioning

confidence: 99%

Comparison of Style Features for the Authorship Verification of Literary Texts

Lagutina

2021

Model. anal. inf. sist.

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The article compares character-level, word-level, and rhythm features for the authorship verification of literary texts of the 19th-21st centuries. Text corpora contains fragments of novels, each fragment has a size of about 50 000 characters. There are 40 fragments for each author. 20 authors who wrote in English, Russian, French, and 8 Spanish-language authors are considered.The authors of this paper use existing algorithms for calculation of low-level features, popular in the computer linguistics, and rhythm features, common for the literary texts. Low-level features include n-grams of words, frequencies of letters and punctuation marks, average word and sentence lengths, etc. Rhythm features are based on lexico-grammatical figures: anaphora, epiphora, symploce, aposiopesis, epanalepsis, anadiplosis, diacope, epizeuxis, chiasmus, polysyndeton, repetitive exclamatory and interrogative sentences. These features include the frequency of occurrence of particular rhythm figures per 100 sentences, the number of unique words in the aspects of rhythm, the percentage of nouns, adjectives, adverbs and verbs in the aspects of rhythm. Authorship verification is considered as a binary classification problem: whether the text belongs to a particular author or not. AdaBoost and a neural network with an LSTM layer are considered as classification algorithms. The experiments demonstrate the effectiveness of rhythm features in verification of particular authors, and superiority of feature types combinations over single feature types on average. The best value for precision, recall, and F-measure for the AdaBoost classifier exceeds 90% when all three types of features are combined.

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“…Nowadays, the focus of this closed-set scenario has shifted from literary to social media authorship attribution, where methods have been developed to deal with large-scale datasets of small-sized online texts. Examples are provided by the work of (Rocha et al, 2017), (Boenninghoff et al, 2019a), (Theophilo et al, 2019), and (Tschuggnall et al, 2019).…”

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confidence: 99%

Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution

Boenninghoff

Zeiler

Nickel

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Traditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts. In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task. Variational autoencoders (VAEs) have had tremendous success in learning latent representations. However, existing VAEs are currently still bound by limitations imposed by the assumed Gaussianity of the underlying probability distributions in the latent space. In this work, we are extending a VAE with an embedded Gaussian mixture model to a Student-t mixture model, which allows for an independent control of the "heaviness" of the respective tails of the implied probability densities. Experiments over an Amazon review dataset indicate superior performance of the proposed method.This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/.1 Each review text had a length of less than 1000 tokens, i.e. relatively short text sizes.

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Similarity Learning for Authorship Verification in Social Media

Cited by 29 publications

References 28 publications

Explainable Authorship Verification in Social Media via Attention-based Similarity Learning

Explainable Authorship Verification in Social Media via Attention-based Similarity Learning

Comparison of Style Features for the Authorship Verification of Literary Texts

Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution

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