3,204 research outputs found
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
Phonetic Detection for Hate Speech Spreaders on Twitter
Nowadays, hate messages have become the object of study on social media. Efficient and effective
detection of hate profiles requires various scientific disciplines, such as computational linguistics and
sociology. Here, we illustrate how we used lexical and phonetic features to determine if the author
spreads hate speech. This article presents a novel strategy for the characterization of the Twitter profile
based on the generation of lexical and phonetic user features that serve as input to a set of classifiers.
The results are part of our participation in the PAN 2021 in the CLEF in the task of Profiling Hate Speech
Spreaders on Twitter
Automatic Detection of Online Jihadist Hate Speech
We have developed a system that automatically detects online jihadist hate
speech with over 80% accuracy, by using techniques from Natural Language
Processing and Machine Learning. The system is trained on a corpus of 45,000
subversive Twitter messages collected from October 2014 to December 2016. We
present a qualitative and quantitative analysis of the jihadist rhetoric in the
corpus, examine the network of Twitter users, outline the technical procedure
used to train the system, and discuss examples of use.Comment: 31 page
Multilingual Cross-domain Perspectives on Online Hate Speech
In this report, we present a study of eight corpora of online hate speech, by
demonstrating the NLP techniques that we used to collect and analyze the
jihadist, extremist, racist, and sexist content. Analysis of the multilingual
corpora shows that the different contexts share certain characteristics in
their hateful rhetoric. To expose the main features, we have focused on text
classification, text profiling, keyword and collocation extraction, along with
manual annotation and qualitative study.Comment: 24 page
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
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
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