Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection

[EN] We briefly report on the four shared tasks organized as part of the PAN 2020 evaluation lab on digital text forensics and authorship analysis. Each tasks is introduced, motivated, and the results obtained are presented. Altogether, the four tasks attracted 230 registrations, yielding 83 successful submissions. This, and the fact that we continue to invite the submissions of software rather than its run output using the TIRA experimentation platform, marks for a good start into the second decade of PAN evaluations labs.We thank Symanto for sponsoring the ex aequo award for the two best performing systems at the author profiling shared task of this year on Profiling fake news spreaders on Twitter. The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018¿096212-B-C31). The work of Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2_181441).Bevendorff, J.; Ghanem, BHH.; Giachanou, A.; Kestemont, M.; Manjavacas, E.; Markov, I.; Mayerl, M.... (2020). Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection. Springer. 372-383. https://doi.org/10.1007/978-3-030-58219-7_25S372383Bevendorff, J., et al.: Shared tasks on authorship analysis at PAN 2020. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 508–516. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_66Bevendorff, J., Stein, B., Hagen, M., Potthast, M.: Bias analysis and mitigation in the evaluation of authorship verification. In: 57th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 6301–6306 (2019)Bevendorff, J., Stein, B., Hagen, M., Potthast, M.: Generalizing unmasking for short texts. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, pp. 654–659 (2019)Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020)Giachanou, A., Ríssola, E.A., Ghanem, B., Crestani, F., Rosso, Paolo: The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In: Métais, E., Meziane, F., Horacek, H., Cimiano, P. (eds.) NLDB 2020. LNCS, vol. 12089, pp. 181–192. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51310-8_17Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 877–880 (2019)Kestemont, M., Stamatatos, E., Manjavacas, E., Daelemans, W., Potthast, M., Stein, B.: Overview of the cross-domain authorship attribution task at PAN 2019. In: Working Notes Papers of the CLEF 2019 Evaluation Labs. CEUR Workshop Proceedings (2019)Kestemont, M., et al.: Overview of the author identification task at PAN-2018: cross-domain authorship attribution and style change detection. In: Working Notes Papers of the CLEF 2018 Evaluation Labs. CEUR Workshop Proceedings (2018)Peñas, A., Rodrigo, A.: A simple measure to assess non-response. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011)Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Rangel, F., Giachanou, A., Ghanem, B., Rosso, P.: Overview of the 8th author profiling task at PAN 2020: profiling fake news spreaders on Twitter. In: CLEF 2020 Labs and Workshops, Notebook Papers (2020)Rangel, F., Franco-Salvador, M., Rosso, P.: A low dimensionality representation for language variety identification. In: Gelbukh, A. (ed.) CICLing 2016. LNCS, vol. 9624, pp. 156–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75487-1_13Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 430–435 (2018)Vo, N., Lee, K.: Learning from fact-checkers: analysis and generation of fact-checking language. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)Noreen, E.W.: Computer-Intensive Methods for Testing Hypotheses: An Introduction. A Wiley-Interscience Publication, Hoboken (1989)Wiegmann, M., Potthast, M., Stein, B.: Overview of the celebrity profiling task at PAN 2020. In: CLEF 2020 Labs and Workshops, Notebook Papers (2020)Wiegmann, M., Stein, B., Potthast, M.: Celebrity profiling. In: 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Association for Computational Linguistics (2019)Wiegmann, M., Stein, B., Potthast, M.: Overview of the celebrity profiling task at PAN 2019. In: CLEF 2019 Labs and Workshops, Notebook Papers (2019)Zangerle, E., Mayerl, M., Specht, G., Potthast, M., Stein, B.: Overview of the style change detection task at PAN 2020. In: CLEF 2020 Labs and Workshops, Notebook Papers (2020)Zangerle, E., Tschuggnall, M., Specht, G., Potthast, M., Stein, B.: Overview of the style change detection task at PAN 2019. In: CLEF 2019 Labs and Workshops, Notebook Papers (2019

Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection.

[EN] The paper gives a brief overview of the three shared tasks to be organized at the PAN 2021 lab on digital text forensics and stylometry hosted at the CLEF conference. The tasks include authorship verification across domains, author profiling for hate speech spreaders, and style change detection for multi-author documents. In part the tasks are new and in part they continue and advance past shared tasks, with the overall goal of advancing the state of the art, providing for an objective evaluation on newly developed benchmark datasets.The work of the researchers from Universitat Politecnica de Valencia was partially funded by the Spanish MICINN under the project MISMISFAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31), and by the Generalitat Valenciana under the project DeepPattern (PROMETEO/2019/121).Bevendorff, J.; Chulvi-Ferriols, MA.; Peña-Sarracén, GLDL.; Kestemont, M.; Manjavacas, E.; Markov, I.; Mayerl, M.... (2021). Overview of PAN 2021: Authorship Verification, Profiling Hate Speech Spreaders on Twitter, and Style Change Detection. Springer. 567-573. https://doi.org/10.1007/978-3-030-72240-1_6656757

A Decade of Shared Tasks in Digital Text Forensics at PAN

[EN] Digital text forensics aims at examining the originality and credibility of information in electronic documents and, in this regard, to extract and analyze information about the authors of these documents. The research field has been substantially developed during the last decade. PAN is a series of shared tasks that started in 2009 and significantly contributed to attract the attention of the research community in well-defined digital text forensics tasks. Several benchmark datasets have been developed to assess the state-of-the-art performance in a wide range of tasks. In this paper, we present the evolution of both the examined tasks and the developed datasets during the last decade. We also briefly introduce the upcoming PAN 2019 shared tasks.We are indebted to many colleagues and friends who contributed greatly to PAN's tasks: Maik Anderka, Shlomo Argamon, Alberto Barrón-Cedeño, Fabio Celli, Fabio Crestani, Walter Daelemans, Andreas Eiselt, Tim Gollub, Parth Gupta, Matthias Hagen, Teresa Holfeld, Patrick Juola, Giacomo Inches, Mike Kestemont, Moshe Koppel, Manuel Montes-y-Gómez, Aurelio Lopez-Lopez, Francisco Rangel, Miguel Angel Sánchez-Pérez, Günther Specht, Michael Tschuggnall, and Ben Verhoeven. Our special thanks go to PAN¿s sponsors throughout the years and not least to the hundreds of participants.Potthast, M.; Rosso, P.; Stamatatos, E.; Stein, B. (2019). A Decade of Shared Tasks in Digital Text Forensics at PAN. Lecture Notes in Computer Science. 11438:291-300. https://doi.org/10.1007/978-3-030-15719-7_39S2913001143

Rethinking the Authorship Verification Experimental Setups

One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset. Despite generating significant progress in the field, inconsistent performance differences between the closed and open test sets have been reported. To this end, we improve the experimental setup by proposing five new public splits over the PAN dataset, specifically designed to isolate and identify biases related to the text topic and to the author's writing style. We evaluate several BERT-like baselines on these splits, showing that such models are competitive with authorship verification state-of-the-art methods. Furthermore, using explainable AI, we find that these baselines are biased towards named entities. We show that models trained without the named entities obtain better results and generalize better when tested on DarkReddit, our new dataset for authorship verification.Comment: Accepted as a short paper at the EMNLP 2022 conference. 10 pages, 5 figures, 9 table

Cross-domain authorship attribution combining instance-based and profile-based features notebook for PAN at CLEF 2019

Being able to identify the author of an unknown text is crucial. Although it is a well-studied field, it is still an open problem, since a standard approach has yet to be found. In this notebook, we propose our model for the Authorship Attribution task of PAN 2019, that focuses on cross-domain setting covering 4 different languages: French, Italian, English, and Spanish. We use n-grams of characters, words, stemmed words, and distorted text. Our model has an SVM for each feature and an ensemble architecture. Our final results outperform the baseline given by PAN in almost every problem. With this model, we reach the second place in the task with an F1-score of 68%

Syllabic quantity patterns as rhythmic features for Latin authorship attribution

It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so-called syllabic quantity, that is, on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic-agnostic features. Our experiments, carried out on three different datasets using support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors