Addressing hate speech in online spaces has been conceptualized as a classification task that uses Natural Language Processing (NLP) techniques. Through this conceptualization, the hate speech detection task has relied on common conventions and practices from NLP. For instance, inter-annotator agreement is conceptualized as a way to measure dataset quality and certain metrics and benchmarks are used to assure model generalization. However, hate speech is a deeply complex and situated concept that eludes such static and disembodied practices. In this position paper, we critically reflect on these methodologies for hate speech detection, we argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.

Directions for NLP Practices Applied to Online Hate Speech Detection

The range of works that can be considered as developing NLP for social good (NLP4SG) is enormous. While many of them target the identification of hate speech or fake news, there are others that address, e.g., text simplification to alleviate consequences of dyslexia, or coaching strategies to fight depression. However, so far, there is no clear picture of what areas are targeted by NLP4SG, who are the actors, which are the main scenarios and what are the topics that have been left aside. In order to obtain a clearer view in this respect, we first propose a working definition of NLP4SG and identify some primary aspects that are crucial for NLP4SG, including, e.g., tackled areas, ethics, privacy and bias. Then, we draw upon a corpus of around 50,000 articles downloaded from the ACL Anthology. Based on a list of keywords retrieved from the literature and revised in view of the task, we retrieve from this corpus articles that can be considered to be on NLP4SG according to our definition and analyze them. The result of the analysis is a map of the current NLP4SG research and insights concerning the white spots on this map.

Cartography of Natural Language Processing for Social Good (NLP4SG): Searching for Definitions, Statistics and White Spots

Mainstream research on hate speech focused so far predominantly on the task of classifying, e.g., social media posts, with respect to predefined typologies of rather coarse-grained hate speech categories. This may be sufficient if the goal is to detect and delete abusive language posts. However, removal is not always possible due to the legislation of a country. Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives. For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post. As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain. The outcome of the experiments is promising and can serve as inspiration for further work on the task.

Targets and Aspects in Social Media Hate Speech

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.

MIN_PT: An European Portuguese Lexicon for Minorities Related Terms

**Topics:** Semantics: Lexical, Sentence level, Textual Inference and Other areas 
Sentiment Analysis, Stylistic Analysis, and Argument Mining 
Sentiment Analysis, Stylistic Analysis, and Argument Mining (CL) 
Speech, Vision, Robotics, Multimodal Grounding 
Theme Track Demo: Resources and Evaluation

Virtual Poster Session 6 

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

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Workshop on NLP for Positive Impact

workshop paper

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IJCNLP-AACL 2021

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**Date: ** 6 August 2021 

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**Workshop Link: ** https://www.workshopononlineabuse.com/home 

**Schedule: ** 13:00 UTC - 22:00 UTC

Paula Fortuna

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SHORT BIO

Presentations

Directions for NLP Practices Applied to Online Hate Speech Detection

Cartography of Natural Language Processing for Social Good (NLP4SG): Searching for Definitions, Statistics and White Spots

Targets and Aspects in Social Media Hate Speech

MIN_PT: An European Portuguese Lexicon for Minorities Related Terms

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