RuG @ EVALITA 2018: Hate Speech Detection In Italian Social Media

Bai, Xiaoyu; Merenda, Flavio; Zaghi, Claudia; Caselli, Tomasso; Nissim, Malvina

doi:10.4000/books.aaccademia.4824

Cited by 5 publications

(5 citation statements)

References 5 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

Corazza

Menini

Cabrio

et al. 2020

ACM Trans. Internet Technol.

141

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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.

141

View full text Add to dashboard Cite

show abstract

“…Most data is collected from social media platforms (such as Twitter ( Davidson et al, 2017 ), Facebook ( Ljubešić, Fišer & Erjavec, 2019 )), newspaper comments ( Gao & Huang, 2017 ), YouTube ( Obadimu et al, 2019 ), and Reddit ( Qian et al, 2019 ). Lately, however, the focus has been shifting to other languages, with several shared tasks organized that cover other languages besides English, including EVALITA 2018 ( Bai et al, 2018 ), GermEval 2018 ( Wiegand, Siegel & Ruppenhofer, 2018 ) and SemEval 2019 Task 5 on Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter ( Basile et al, 2019 ). The OffensEval 2020 shared task ( Zampieri et al, 2020a ) also featured five languages: Arabic, Danish, English, Greek, Turkish.…”

Section: Background and Related Workmentioning

confidence: 99%

Investigating cross-lingual training for offensive language detection

Pelicon

Shekhar

Škrlj

et al. 2021

PeerJ Computer Science

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Platforms that feature user-generated content (social media, online forums, newspaper comment sections etc.) have to detect and filter offensive speech within large, fast-changing datasets. While many automatic methods have been proposed and achieve good accuracies, most of these focus on the English language, and are hard to apply directly to languages in which few labeled datasets exist. Recent work has therefore investigated the use of cross-lingual transfer learning to solve this problem, training a model in a well-resourced language and transferring to a less-resourced target language; but performance has so far been significantly less impressive. In this paper, we investigate the reasons for this performance drop, via a systematic comparison of pre-trained models and intermediate training regimes on five different languages. We show that using a better pre-trained language model results in a large gain in overall performance and in zero-shot transfer, and that intermediate training on other languages is effective when little target-language data is available. We then use multiple analyses of classifier confidence and language model vocabulary to shed light on exactly where these gains come from and gain insight into the sources of the most typical mistakes.

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“…Most hate speech recognition systems at HaSpeeDe-tw2018 exploit SVM, Recurrent Neural Networks with LSTM or ensemble learning (meta) Bai et al (2018), Michele et al (2018), De la Pena Sarracén et al (2018, and word-embeddings as features Santucci et al (2018), pre-trained or extracted from the training set. Some systems also use cross-platform data (i.e.…”

Section: Related Workmentioning

confidence: 99%

Hate versus politics: detection of hate against policy makers in Italian tweets

Duzha¹,

Casadei²,

Tosi³

et al. 2021

SN Soc Sci

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Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and antiimmigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets.

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RuG @ EVALITA 2018: Hate Speech Detection In Italian Social Media

Cited by 5 publications

References 5 publications

A Multilingual Evaluation for Online Hate Speech Detection

A Multilingual Evaluation for Online Hate Speech Detection

Investigating cross-lingual training for offensive language detection

Hate versus politics: detection of hate against policy makers in Italian tweets

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