Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter

[EN] This overview presents the Author Profiling shared task at PAN 2020. The focus of this year's task is on determining whether or not the author of a Twitter feed is keen to spread fake news. Two have been the main aims: (i) to show the feasibility of automatically identifying potential fake news spreaders in Twitter; and (ii) to show the difficulty of identifying them when they do not limit themselves to just retweet domain-specific news. For this purpose a corpus with Twitter data has been provided, covering the English and Spanish languages. Altogether, the approaches of 66 participants have been evaluated.First of all we thank the participants: 66 this year, record in terms of participants at PAN Lab since 2009! We have to thank also Martin Potthast, Matti Wiegmann, and Nikolay Kolyada to help with the 66 Virtual Machines in the TIRA platform. We thank Symanto for sponsoring the ex aequo award for the two best performing systems at the author profiling shared task of this year. 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).Rangel, F.; Giachanou, A.; Ghanem, BHH.; Rosso, P. (2020). Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter. CEUR Workshop Proceedings. 2696:1-18. http://hdl.handle.net/10251/166528S118269

Automatic authorship analysis using Deep neural networks

Authorship analysis helps to study the characteristics that distinguish how two different persons write. Writing style can be extracted in several ways, like using bag of words strategies or handcrafted features. However, with the growing of Internet, we have been able to witness an increase in the amount of user generated data in social networks like Facebook or Twitter. There is an increasing need in generating automatic methods capable of analyzing the style of a document for tasks like: determining the age of the author, determining the gender of the author, determining the authorship of the document given a set of possible authors, etc. Previous tasks are better known as author profiling and authorship attribution. Although capturing the style of an author can be a challenging task, in this thesis we explore representation learning strategies, in order to take advantage of the large amount of data generated by social media. In this thesis, we learned proper representations for the text inputs that were able to learn such patterns that are only distinguishable to an author (authorship attribution) or a social group of authors (author profiling). Proposed methods were compared using different publicly available datasets using social media data. Both author profiling and authorship attribution tasks are addressed using representation learning techniques such as convolutional neural networks and gated multimodal units. Our unimodal author profiling approach was submitted to the profiling shared task of the laboratory on digital forensics and stylometry(PAN). For authorship attribution, we proposed a convolutional neural network using character n-grams as input. We found that our approach outperformed standard attribution based methods as well as word based convolutional neural networks. For the author profiling task, we proposed one convolutional neural network for unimodal author profiling and adapted a gated multimodal unit for multimodal author profiling. The multimodal nature of user generated content consists of a scenario where the social group of an author can be determined not only using his/her written texts but using also the images that the user shared across the social networks. Gated multimodal units outperformed standard information fusion strategies: early and late fusion.Maestrí

Text stylometry for chat bot identification and intelligence estimation.

Authorship identification is a technique used to identify the author of an unclaimed document, by attempting to find traits that will match those of the original author. Authorship identification has a great potential for applications in forensics. It can also be used in identifying chat bots, a form of intelligent software created to mimic the human conversations, by their unique style. The online criminal community is utilizing chat bots as a new way to steal private information and commit fraud and identity theft. The need for identifying chat bots by their style is becoming essential to overcome the danger of online criminal activities. Researchers realized the need to advance the understanding of chat bots and design programs to prevent criminal activities, whether it was an identity theft or even a terrorist threat. The more research work to advance chat bots’ ability to perceive humans, the more duties needed to be followed to confront those threats by the research community. This research went further by trying to study whether chat bots have behavioral drift. Studying text for Stylometry has been the goal for many researchers who have experimented many features and combinations of features in their experiments. A novel feature has been proposed that represented Term Frequency Inverse Document Frequency (TFIDF) and implemented that on a Byte level N-Gram. Term Frequency-Inverse Token Frequency (TF-ITF) used these terms and created the feature. The initial experiments utilizing collected data demonstrated the feasibility of this approach. Additional versions of the feature were created and tested for authorship identification. Results demonstrated that the feature was successfully used to identify authors of text, and additional experiments showed that the feature is language independent. The feature successfully identified authors of a German text. Furthermore, the feature was used in text similarities on a book level and a paragraph level. Finally, a selective combination of features was used to classify text that ranges from kindergarten level to scientific researches and novels. The feature combination measured the Quality of Writing (QoW) and the complexity of text, which were the first step to correlate that with the author’s IQ as a future goal

Profiling hate speech spreaders on twitter task at PAN 2021

[EN] This overview presents the Author Profiling shared task at PAN 2021. The focus of this year¿s task is on determining whether or not the author of a Twitter feed is keen to spread hate speech. The main aim is to show the feasibility of automatically identifying potential hate speech spreaders on Twitter. For this purpose a corpus with Twitter data has been provided, covering the English and Spanish languages. Altogether, the approaches of 66 participants have been evaluated.First of all, we thank the participants: again 66 this year, as the previous year on Profiling Fake News Spreaders! We have to thank also Martin Potthast, Matti Wiegmann, Nikolay Kolyada, and Magdalena Anna Wolska for their technical support with the TIRA platform. We thank Symanto for sponsoring again the award for the best performing system at the author profiling shared task. The work of Francisco Rangel was partially funded by the Centre for the Development of Industrial Technology (CDTI) of the Spanish Ministry of Science and Innovation under the research project IDI-20210776 on Proactive Profiling of Hate Speech Spreaders - PROHATER (Perfilador Proactivo de Difusores de Mensajes de Odio). The work of the researchers from Universitat Politècnica de València was partially funded by the Spanish MICINN under the project MISMIS-FAKEnHATE 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). This article is also based upon work from the Dig-ForAsp COST Action 17124 on Digital Forensics: evidence analysis via intelligent systems and practices, supported by European Cooperation in Science and Technology.Rangel, F.; Peña-Sarracén, GLDL.; Chulvi-Ferriols, MA.; Fersini, E.; Rosso, P. (2021). Profiling hate speech spreaders on twitter task at PAN 2021. CEUR. 1772-1789. http://hdl.handle.net/10251/1906631772178

Experimental Analysis of the Relevance of Features and Effects on Gender Classification Models for Social Media Author Profiling

[Abstract] Automatic user profiling from social networks has become a popular task due to its commercial applications (targeted advertising, market studies...). Automatic profiling models infer demographic characteristics of social network users from their generated content or interactions. Users’ demographic information is also precious for more social worrying tasks such as automatic early detection of mental disorders. For this type of users’ analysis tasks, it has been shown that the way how they use language is an important indicator which contributes to the effectiveness of the models. Therefore, we also consider that for identifying aspects such as gender, age or user’s origin, it is interesting to consider the use of the language both from psycho-linguistic and semantic features. A good selection of features will be vital for the performance of retrieval, classification, and decision-making software systems. In this paper, we will address gender classification as a part of the automatic profiling task. We show an experimental analysis of the performance of existing gender classification models based on external corpus and baselines for automatic profiling. We analyse in-depth the influence of the linguistic features in the classification accuracy of the model. After that analysis, we have put together a feature set for gender classification models in social networks with an accuracy performance above existing baselines.This work was supported by projects RTI2018-093336-B-C21, RTI2018-093336-B-C22 (Ministerio de Ciencia e Innvovacion & ERDF) and the financial support supplied by the Conselleria de Educacion, Universidade e Formacion Profesional (accreditation 2019-2022 ED431G/01, ED431B 2019/03) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruna as a Research Center of the Galician University System.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431B 2019/0