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 2018. Author identification, author profiling, and author obfuscation

[EN] PAN 2018 explores several authorship analysis tasks enabling a systematic comparison of competitive approaches and advancing research in digital text forensics.More specifically, this edition of PAN introduces a shared task in cross-domain authorship attribution, where texts of known and unknown authorship belong to distinct domains, and another task in style change detection that distinguishes between single author and multi-author texts. In addition, a shared task in multimodal author profiling examines, for the first time, a combination of information from both texts and images posted by social media users to estimate their gender. Finally, the author obfuscation task studies how a text by a certain author can be paraphrased so that existing author identification tools are confused and cannot recognize the similarity with other texts of the same author. New corpora have been built to support these shared tasks. A relatively large number of software submissions (41 in total) was received and evaluated. Best paradigms are highlighted while baselines indicate the pros and cons of submitted approaches.The work at the Universitat Polit`ecnica de Val`encia was funded by the MINECO research project SomEMBED (TIN2015-71147-C2-1-P)Stamatatos, E.; Rangel-Pardo, FM.; Tschuggnall, M.; Stein, B.; Kestemont, M.; Rosso, P.; Potthast, M. (2018). Overview of PAN 2018. Author identification, author profiling, and author obfuscation. Lecture Notes in Computer Science. 11018:267-285. https://doi.org/10.1007/978-3-319-98932-7_25S26728511018Argamon, S., Juola, P.: Overview of the international authorship identification competition at PAN-2011. In: Petras, V., Forner, P., Clough, P. (eds.) Notebook Papers of CLEF 2011 Labs and Workshops, 19–22 September 2011, Amsterdam, Netherlands, September 2011. http://www.clef-initiative.eu/publication/working-notesBird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Sebastopol (2009)Bogdanova, D., Lazaridou, A.: Cross-language authorship attribution. In: Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014, pp. 2015–2020 (2014)Choi, F.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics Conference (NAACL), pp. 26–33. Association for Computational Linguistics, Seattle, April 2000Custódio, J.E., Paraboni, I.: EACH-USP ensemble cross-domain authorship attribution. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedDaneshvar, S.: Gender identification in Twitter using n-grams and LSA. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedDaniel Karaś, M.S., Sobecki, P.: OPI-JSA at CLEF 2017: author clustering and style breach detection. In: Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings. CLEF and CEUR-WS.org, September 2017Giannella, C.: An improved algorithm for unsupervised decomposition of a multi-author document. The MITRE Corporation. Technical Papers, February 2014Glover, A., Hirst, G.: Detecting stylistic inconsistencies in collaborative writing. In: Sharples, M., van der Geest, T. (eds.) The New Writing Environment, pp. 147–168. Springer, London (1996). https://doi.org/10.1007/978-1-4471-1482-6_12Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2017: safety evaluation revisited. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Hagen, M., Potthast, M., Stein, B.: Overview of the author obfuscation task at PAN 2018. In: Working Notes Papers of the CLEF 2018 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org (2018)Hellekson, K., Busse, K. (eds.): The Fan Fiction Studies Reader. University of Iowa Press, Iowa City (2014)Juola, P.: An overview of the traditional authorship attribution subtask. In: Forner, P., Karlgren, J., Womser-Hacker, C. (eds.) CLEF 2012 Evaluation Labs and Workshop - Working Notes Papers, 17–20 September 2012, Rome, Italy, September 2012. http://www.clef-initiative.eu/publication/working-notesJuola, P.: The rowling case: a proposed standard analytic protocol for authorship questions. Digital Sch. Humanit. 30(suppl–1), i100–i113 (2015)Kestemont, M., Luyckx, K., Daelemans, W., Crombez, T.: Cross-genre authorship verification using unmasking. Engl. Stud. 93(3), 340–356 (2012)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, CLEF and CEUR-WS.org (2018)Koppel, M., Schler, J., Bonchek-Dokow, E.: Measuring differentiability: unmasking pseudonymous authors. J. Mach. Learn. Res. 8, 1261–1276 (2007)Overdorf, R., Greenstadt, R.: Blogs, Twitter feeds, and reddit comments: cross-domain authorship attribution. Proc. Priv. Enhanc. Technol. 2016(3), 155–171 (2016)Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: Notebook Papers of the 5th Evaluation Lab on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN), Amsterdam, The Netherlands, September 2011Potthast, M., Hagen, M., Stein, B.: Author obfuscation: attacking the state of the art in authorship verification. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Potthast, M., Hagen, M., Völske, M., Stein, B.: Crowdsourcing interaction logs to understand text reuse from the web. In: Fung, P., Poesio, M. (eds.) Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 1212–1221. Association for Computational Linguistics, August 2013. http://www.aclweb.org/anthology/P13-1119Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Daelemans, W.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, Toulouse, France, pp. 8–11. CEUR-WS.org, September 2015Rangel, F., et al.: Overview of the 2nd author profiling task at PAN 2014. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) CLEF 2014 Evaluation Labs and Workshop - Working Notes Papers, Sheffield, UK, pp. 15–18. CEUR-WS.org, September 2014Rangel, F., Rosso, P., G’omez, M.M., Potthast, M., Stein, B.: Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter. In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org (2017)Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013. In: Forner, P., Navigli, R., Tufis, D. (eds.) CLEF 2013 Evaluation Labs and Workshop - Working Notes Papers, 23–26 September 2013, Valencia, Spain, September 2013Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at PAN 2017: gender and language variety identification in Twitter. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Rangel, F., Rosso, P., Verhoeven, B., Daelemans, W., Potthast, M., Stein, B.: Overview of the 4th author profiling task at PAN 2016: cross-genre evaluations. In: Balog, K., Cappellato, L., Ferro, N., Macdonald, C. (eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, September 2016Safin, K., Kuznetsova, R.: Style breach detection with neural sentence embeddings. In: Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2017Sapkota, U., Bethard, S., Montes, M., Solorio, T.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)Sapkota, U., Solorio, T., Montes, M., Bethard, S., Rosso, P.: Cross-topic authorship attribution: will out-of-topic data help? In: Proceedings of the 25th International Conference on Computational Linguistics. Technical Papers, pp. 1228–1237 (2014)Stamatatos, E.: Intrinsic plagiarism detection using character $$n$$n-gram Profiles. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E. (eds.) SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 38–46. Universidad Politécnica de Valencia and CEUR-WS.org, September 2009. http://ceur-ws.org/Vol-502Stamatatos, E.: On the robustness of authorship attribution based on character n-gram features. J. Law Policy 21, 421–439 (2013)Stamatatos, E.: Authorship attribution using text distortion. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1138–1149. Association for Computational Linguistics (2017)Stamatatos, E., et al.: Overview of the author identification task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF 2015 Evaluation Labs and Workshop - Working Notes Papers, 8–11 September 2015, Toulouse, France. CEUR-WS.org, September 2015Stamatatos, E., et al.: Clustering by authorship within and across documents. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2016. http://ceur-ws.org/Vol-1609/Takahashi, T., Tahara, T., Nagatani, K., Miura, Y., Taniguchi, T., Ohkuma, T.: Text and image synergy with feature cross technique for gender identification. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedTellez, E.S., Miranda-Jiménez, S., Moctezuma, D., Graff, M., Salgado, V., Ortiz-Bejar, J.: Gender identification through multi-modal tweet analysis using microtc and bag of visual words. In: Working Notes Papers of the CLEF 2018 Evaluation Labs, September 2018, to be announcedTschuggnall, M., Specht, G.: Automatic decomposition of multi-author documents using grammar analysis. In: Proceedings of the 26th GI-Workshop on Grundlagen von Datenbanken. CEUR-WS, Bozen, October 2014Tschuggnall, M., et al.: Overview of the author identification task at PAN-2017: style breach detection and author clustering. In: Cappellato, L., Ferro, N., Goeuriot, L., Mandl, T. (eds.) Working Notes Papers of the CLEF 2017 Evaluation Labs. CEUR Workshop Proceedings, vol. 1866. CLEF and CEUR-WS.org, September 2017. http://ceur-ws.org/Vol-1866

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

Psychographic Traits Identification based on political ideology: An author analysis study on spanish politicians tweets posted in 2020

In general, people are usually more reluctant to follow advice and directions from politicians who do not have their ideology. In extreme cases, people can be heavily biased in favour of a political party at the same time that they are in sharp disagreement with others, which may lead to irrational decision making and can put people’s lives at risk by ignoring certain recommendations from the authorities. Therefore, considering political ideology as a psychographic trait can improve political micro-targeting by helping public authorities and local governments to adopt better communication policies during crises. In this work, we explore the reliability of determining psychographic traits concerning political ideology. Our contribution is twofold. On the one hand, we release the PoliCorpus-2020, a dataset composed by Spanish politicians’ tweets posted in 2020. On the other hand, we conduct two authorship analysis tasks with the aforementioned dataset: an author profiling task to extract demographic and psychographic traits, and an authorship attribution task to determine the author of an anonymous text in the political domain. Both experiments are evaluated with several neural network architectures grounded on explainable linguistic features, statistical features, and state-of-the-art transformers. In addition, we test whether the neural network models can be transferred to detect the political ideology of citizens. Our results indicate that the linguistic features are good indicators for identifying finegrained political affiliation, they boost the performance of neural network models when combined with embedding-based features, and they preserve relevant information when the models are tested with ordinary citizens. Besides, we found that lexical and morphosyntactic features are more effective on author profiling, whereas stylometric features are more effective in authorship attribution.publishedVersio