MIN _ PT : An E uropean P ortuguese Lexicon for Minorities Related Terms (2021)

Abstract: Hate speech-related lexicons have been proved to be useful for many tasks such as data collection and classification. However, existing Portuguese lexicons do not distinguish between European and Brazilian Portuguese, and do not include neutral terms that are potentially useful to detect a broader spectrum of content referring to minorities. In this work, we present MIN_PT, a new European Portuguese Lexicon for Minorities-Related Terms specifically designed to tackle the limitations of existing resources. We describe the data collection and annotation process, discuss the limitation and ethical concerns, and prove the utility of the resource by applying it to a use case for the Portuguese 2021 presidential elections.

## introduction dictionaries and lexicons are commonly used in the field of hate speech automatic detection @xcite , with applications ranging from data collection @xcite to feature extraction @xcite and classification @xcite ) by applying some matching function with dictionary terms. however, even though such resources have been proved to be useful in numerous applications, lexical knowledge for hate speech classification has received little attention in literature @xcite . this work takes up this demand and focuses on presenting a new european portuguese lexicon for minorities-related terms. the need for annotating a new resource derives from two different issues: lack of explicit european portuguese lexicon, and the need for neutral terms. ## related work lexicons can be analyzed in terms of how the data is generated and annotated. while some works have been manually annotated by humans, and others rely on automatic procedures where data is compiled by computational methods, other works conjugate both methods by manually curating the automatically compiled data. hatebase is one of the widely used lexicons in the field. it corresponds to a broad multilingual vocabulary manually annotated in terms of different categories (e.g nationality, gender) with data across 95 languages and 175 countries. however, the containing words and phrases have been compiled by non-trained crowdsourced internet volunteers, and therefore the quality of the annotation can not be guaranteed. moreover, the lexicon does not differentiate portuguese and brazilian content. several works have been using hatebase terms as keywords for content search in social media platforms, e.g @xcite @xcite . one of these works has contributed particularly to enrich the lexicon english content @xcite . the authors expand the initial term list with n-grams from the extracted messages when searching with the keywords and finally manually remove irrelevant terms. @xcite presents another lexicon created to detect racist discourse in dutch social media. starting with a list of words from the liwc (linguistic inquiry and word count) (tausczik and pennebaker, 2010), the authors compile a set of terms by applying successive automatic expansions and manual annotation phases. hurtlex @xcite is a multilingual lexicon automatically expanded and manually validated with 17 different dimensions such as: negative stereotypes ethnic slurs; professions and occupations; etc. in the set of the discussed lexicons, hurtlex presents the more complete and complex taxonomy. however, for the purpose of our study, this taxonomy also misses neutral terms to refer to minorities, such as "mulheres" -womenor "muc ¸ulmanos" -muslim-, that can help to identify less explicit insults and also positive content. if we focus on the hurtlex portuguese subset of terms, we find again no distinction between brazilian and portuguese contexts. even though the discussed lexicons rely on automatic methods to compile an initial set of terms, they all require manual validation procedures to confirm relevant terms. in this procedure, annotators guarantee that terms match the taxonomy classification rules, which highlights the importance of human annotators to assure higher-quality resources. accordingly, in this work we also rely on a manual enumeration and annotation of terms to create a new european portuguese lexicon for minorities-related terms, containing both offensive and non-offensive terms. our approach also aligns with the recommendation for synthetic data creation as the compiled data is generated, annotated and validated by experts in an attempt to mimic real behaviour @xcite . ## methodology this section describes the data collection and annotation procedure followed to build min pt, a european portuguese lexicon for minorities related terms. we followed a qualitative approach with successive iterations and annotators' participation, as recommended in @xcite . starting with and initial set of terms (section 3.1), the annotators worked individually and collectively in successive iterations to create new annotation rules, remove undesired terms and expand the existent terms with new ones (section 3.2). then, two annotators discussed the lexicon terms to reach a consensus on a set of definitions and instructions, deciding which terms are kept and which terms must be eliminated (section 3.3). the curated list of terms and their classification is available in a public github repository foot_1 . ## results the min pt european portuguese lexicon for minorities related terms is composed of 155 carefully curated terms (cf. section 3) related to 7 minority groups, as described in even though our new lexicon contains much less terms than hurtlex @xcite , 155 vs 3902 terms, it is worth noticing that only 23% of the terms in min pt are present in hurtlex. therefore, the new lexicon presented in this work will prove to be a valuable resource for hate-speech detection, either on its own or in combination with other resources. ## lexicon application: the case of portuguese 2021 presidential elections the annotation of this lexicon was motivated by the will to conduct an analysis on the portuguese 2021 presidential elections twittersphere, aiming at understanding whether and how candidates' speeches and replies would tackle minority topics. the analyzed data is a subset from the portuguese presidential elections, jan 24th 2021 (ramalho, 2021) and corresponds to 35,101 tweets from september 2nd, 2020 to november 22th, 2020. for the six candidates using twitter, we performed a keyword matching with the terms in the min pt lexicon to compute the percentage of tweets (figure 1 ) and their replies (figure 2 ) referring to minorities. marisa matias (mmatias ), andré ventura (andrecventura) and ana gomes (anamartinsgomes) are the candidates tackling a higher percentage of minorities topics. however, the targeted minorities are distinct depending on the candidate. while ana gomes focused more uniformly on the different groups, marisa matias discussed more refugees and women issues and andré ventura focused on roma and people based on ethnicity, i.e racism issues. comparing both figures, it is also interesting to see that the candidates' audience does not exactly resonate with the candidate in terms of mentioned minority topics. moreover, while none of the candidates mentions any of the explicit insults in our lexicon, they were present in the audience. while our lexicon proved to be valuable for an initial topic analysis, a more in depth analysis should be performed to get further insights on how politicians are referring to minorities. ## limitations and ethical concerns lexicons are static resources that can not mimic the contextual and mutating nature of language, and certain terms may refer to minorities, be considered as insults or just be neutral words depending on the andrecventura anamartinsgomes figure 2: relative frequencies of minority mentions in replies to candidates. finally, even though the presence of the terms in our lexicon may imply hate speech against minorities, it should not be used for direct hate speech classification with keyword matching. depending on the context and the data author, such terms may have a neutral and even positive meaning. ## conclusions we presented min pt, a new european portuguese lexicon for minorities-related terms. we discussed existing annotated lexicons, grounding the need for a new lexicon. following a qualitative approach, we produced a high-quality lexicon containing also neutral words and specific for european portuguese. we also presented a use case of the lexicon on the analysis of portuguese politicians' tweets. future iterations of this work would benefit from the contribution of more annotators to increase the diversity of the available vocabulary.