Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, and Style Change Detection

Bevendorff, Janek; Chulvi, Berta; Fersini, Elisabetta; Heini, Annina; Kestemont, Mike; Kredens, Krzysztof; Mayerl, Maximilian; Ortega-Bueno, Reynier; Pęzik, Piotr; Potthast, Martin; Rangel, Francisco; Rosso, Paolo; Stamatatos, Efstathios; Stein, Benno; Wiegmann, Matti; Wolska, Magdalena; Zangerle, Eva

doi:10.1007/978-3-031-13643-6_24

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…However, in the research carried out by Marco, S. et al on multi-label classification tasks and binary classification tasks [21], their experimental results proved that a model based on a transformer model has a better classification effect. PAN@CLEF2022 presented the Irony and Stereotype Purveyors (ISSs) task on Twitter [22], which investigates whether Twitter writers are likely to spread sarcasm and stereotypes. The model used by Marco, S. et al consisted of logistic regression (LR) that takes the predictions provided by the first-stage classifier (called the voter) as input [23].…”

Section: Related Workmentioning

confidence: 99%

Supervised Character Resemble Substitution Personality Adversarial Method

et al. 2023

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With the development of science and computer technology, social networks are changing our daily lives. However, this leads to new, often hidden dangers in areas such as cybersecurity. Of these, the most complex and harmful is the Advanced Persistent Threat attack (APT attack). The development of personality analysis and prediction technology provides the APT attack a good opportunity to infiltrate personality privacy. Malicious people can exploit existing personality classifiers to attack social texts and steal users’ personal information. Therefore, it is of high importance to hide personal privacy information in social texts. Based on the personality privacy protection technology of adversarial examples, we proposed a Supervised Character Resemble Substitution personality adversarial method (SCRS) in this paper, which hides personality information in social texts through adversarial examples to realize personality privacy protection. The adversarial examples should be capable of successfully disturbing the personality classifier while maintaining the original semantics without reducing human readability. Therefore, this paper proposes a measure index of “label contribution” to select the words that are important to the label. At the same time, in order to maintain higher readability, this paper uses character-level resemble substitution to generate adversarial examples. Experimental validation shows that our method is able to generate adversarial examples with good attack effect and high readability.

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Section: Related Workmentioning

confidence: 99%