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Register a new userBuilding an Expert Annotated Corpus of Brazilian Instagram Comments for Hate Speech and Offensive Language Detection
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Added by
Francielle Alves Vargas
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The understanding of an offense is subjective and people may have different opinions about the offensiveness of a comment. Moreover, offenses and hate speech may occur through sarcasm, which hides the real intention of the comment and makes the decision of the annotators more confusing. Therefore, providing a well-structured annotation process is crucial to a better understanding of hate speech and offensive language phenomena, as well as supplying better performance for machine learning classifiers. In this paper, we describe a corpus annotation process proposed by a linguist, a hate speech specialist, and machine learning engineers in order to support the identification of hate speech and offensive language on social media. In addition, we provide the first robust dataset of this kind for the Brazilian Portuguese language. The corpus was collected from Instagram posts of political personalities and manually annotated, being composed by 7,000 annotated documents according to three different layers: a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive, and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology to the dictatorship, antisemitism, and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. The proposed annotation approach is also language and domain-independent nevertheless it is currently customized for Brazilian Portuguese.
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The 2020 US Elections have been, more than ever before, characterized by social media campaigns and mutual accusations. We investigate in this paper if this manifests also in online communication of the supporters of the candidates Biden and Trump, by uttering hateful and offensive communication. We formulate an annotation task, in which we join the tasks of hateful/offensive speech detection and stance detection, and annotate 3000 Tweets from the campaign period, if they express a particular stance towards a candidate. Next to the established classes of favorable and against, we add mixed and neutral stances and also annotate if a candidate is mentioned without an opinion expression. Further, we annotate if the tweet is written in an offensive style. This enables us to analyze if supporters of Joe Biden and the Democratic Party communicate differently than supporters of Donald Trump and the Republican Party. A BERT baseline classifier shows that the detection if somebody is a supporter of a candidate can be performed with high quality (.89 F1 for Trump and .91 F1 for Biden), while the detection that somebody expresses to be against a candidate is more challenging (.79 F1 and .64 F1, respectively). The automatic detection of hate/offensive speech remains challenging (with .53 F1). Our corpus is publicly available and constitutes a novel resource for computational modelling of offensive language under consideration of stances.
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