Merging datasets for hate speech classification in Italian

Fortuna, Paula; Bonavita, Ilaria; Nunes, Sérgio

doi:10.4000/books.aaccademia.4752

Cited by 19 publications

(9 citation statements)

References 14 publications

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“…The task consists in automatically annotating messages from Twitter and Facebook, with a boolean value indicating the presence (or not) of hate speech. Similar to Germeval 2018 submissions, also in this case the participating systems adopt a wide range of approaches, including bi-LSTM [39], SVM [53], ensemble classifiers [52,4], RNN [28], CNN and GRU [60]. The authors of the best-performing system, ItaliaNLP [17], experiment with three different classification models: one based on linear SVM, another one based on a 1-layer BiLSTM and a newly-introduced one based on a 2-layer BiLSTM which exploits multi-task learning with additional data from the 2016 SENTIPOLC task 4 .…”

Section: Hate Speech Detection On Languages Different From Englishmentioning

confidence: 99%

A Multilingual Evaluation for Online Hate Speech Detection

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et al. 2020

ACM Trans. Internet Technol.

147

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The increasing popularity of social media platforms such as Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this article, we propose a robust neural architecture that is shown to perform in a satisfactory way across different languages; namely, English, Italian, and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network–specific features, emotion lexica, emojis, word embeddings). To address such in-depth analysis, we use three freely available datasets for hate speech detection on social media in English, Italian, and German.

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Section: Hate Speech Detection On Languages Different From Englishmentioning

confidence: 99%

A Multilingual Evaluation for Online Hate Speech Detection

Corazza

Menini

Cabrio

et al. 2020

ACM Trans. Internet Technol.

147

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“…StopPropagHate (Fortuna et al, 2018) The authors use a classifier based on Recurrent Neural Networks with a binary cross-entropy as loss function. In their system, each input word is represented by a 10000-dimensional vector which is a one-hot encoding vector.…”

Section: Grcp (De La Peñamentioning

confidence: 99%

Domain Adaptation for Text Classification with Weird Embeddings

Basile¹

2020

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

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English. The Hate Speech Detection (HaSpeeDe) task is a shared task on Italian social media (Facebook and Twitter) for the detection of hateful content, and it has been proposed for the first time at EVALITA 2018. Providing two datasets from two different online social platforms differently featured from the linguistic and communicative point of view, we organized the task in three tasks where systems must be trained and tested on the same resource or using one in training and the other in testing: HaSpeeDe-FB, HaSpeeDe-TW and Cross-HaSpeeDe (further subdivided into Cross-HaSpeeDe FB and Cross-HaSpeeDe TW sub-tasks). Overall, 9 teams participated in the task, and the best system achieved a macro F1score of 0.8288 for HaSpeeDe-FB, 0.7993 for HaSpeeDe-TW, 0.6541 for Cross-HaSpeeDe FB and 0.6985 for Cross-HaSpeeDe TW. In this report, we describe the datasets released and the evaluation measures, and we discuss results.Italiano. HaSpeeDeè la prima campagna di valutazione di sistemi per l'identificazione automatica di discorsi di incitamento all'odio su social media (Facebook e Twitter) in lingua italiana, proposta nell'ambito di EVALITA 2018. Fornendo ai partecipanti due insiemi di dati estratti da due piattaforme differenti dal punto di vista linguistico e della comunicazione, abbiamo articolato HaSpeeDe in tre compiti in cui i sistemi sono addestrati e testati sulla stessa tipologia di dati oppure addrestrati su una tipologia e testati sull'altra: HaSpeeDe-FB, HaSpeeDe-TW e Cross-HaSpeeDe (a sua volta suddiviso in Cross-HaSpeeDe FB e Cross-HaSpeeDe TW). Nel complesso, 9 gruppi hanno partecipato alla campagna, e il miglior sistema ha ottenuto un punteggio di macro F1 pari a 0,8288 in HaSpeeDe-FB, 0,7993 in HaSpeeDe-TW, 0,6541 in Cross-HaSpeeDe FB e 0.6985 in Cross-HaSpeeDe TW. L'articolo descrive i dataset rilasciati e le modalità di valutazione, e discute i risultati ottenuti.

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“…Some academic work and several competitions have proposed some tasks to promote studies and advances in the area. Much of this work and data sets focus on English (Fortuna and Nunes, 2018) only, even though this is a widespread phenomenon that happens in any language.…”

Section: Introductionmentioning

confidence: 99%

MDD @ AMI: Vanilla Classifiers for Misogyny Identification

Abassi¹,

Nisioi²

2020

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

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Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it).This volume includes the reports of both task organisers and participants to all of the EVALITA 2020 challenges. In the 2020 edition, we coordinated the organization of 14 different tasks belonging to five research areas, being: (i) Affect, Hate, and Stance, (ii) Creativity and Style, (iii) New Challenges in Long-standing Tasks, (iv) Semantics and Multimodality, Time and Diachrony.The volume is opened by an overview to the EVALITA 2020 campaign, in which we describe the tasks, provide statistics on the participants and task organizers as well as our supporting sponsors. The abstract of the keynote speech made by Preslav Nakov titled "Flattening the Curve of the COVID-19 Infodemic: These Evaluation Campaigns Can Help!" is also included in this collection.Due to the 2020 COVID-19 pandemic, the traditional workshop was held online, where several members of the Italian NLP Community presented the results of their research. Despite the circumstances, the workshop represented an occasion for all participants from both academic institutions and private companies to disseminate their work and results and to share ideas through online sessions dedicated to each task and a general discussion during the plenary event.We carried on with the tradition of the "Best system across tasks" award. As in 2018, it represented an incentive for students, IT developers and researchers to push the boundaries of the state of the art by facing tasks in new ways, even if not winning.

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Merging datasets for hate speech classification in Italian

Cited by 19 publications

References 14 publications

A Multilingual Evaluation for Online Hate Speech Detection

A Multilingual Evaluation for Online Hate Speech Detection

Domain Adaptation for Text Classification with Weird Embeddings

MDD @ AMI: Vanilla Classifiers for Misogyny Identification

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