Related papers: WOLI at SemEval-2020 Task 12: Arabic Offensive Lan…

With the popularity of social media, communications through blogs, Facebook, Twitter, and other plat-forms have increased. Initially, English was the only medium of communication. Fortunately, now we can communicate in any language. It has…

The spread of disinformation and propagandistic content poses a threat to societal harmony, undermining informed decision-making and trust in reliable sources. Online platforms often serve as breeding grounds for such content, and malicious…

Toxic online speech has become a crucial problem nowadays due to an exponential increase in the use of internet by people from different cultures and educational backgrounds. Differentiating if a text message belongs to hate speech and…

The importance of building sentiment analysis tools for Arabic social media has been recognized during the past couple of years, especially with the rapid increase in the number of Arabic social media users. One of the main difficulties in…

Social network platforms are generally used to share positive, constructive, and insightful content. However, in recent times, people often get exposed to objectionable content like threat, identity attacks, hate speech, insults, obscene…

This paper presents the system descriptions submitted at the FIRE Shared Task 2021 on Urdu's Abusive and Threatening Language Detection Task. This challenge aims at automatically identifying abusive and threatening tweets written in Urdu.…

With the growth of social media platform influence, the effect of their misuse becomes more and more impactful. The importance of automatic detection of threatening and abusive language can not be overestimated. However, most of the…

Arabic is one of the most important and growing languages in the world. With the rise of social media platforms such as Twitter, Arabic spoken dialects have become more in use. In this paper, we describe our approach on the NADI Shared Task…

This paper describes the WLV-RIT entry to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) shared task 2020. The HASOC 2020 organizers provided participants with annotated datasets containing social…

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment…

We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023. ArAIEval offers two tasks over Arabic text: (i) persuasion technique detection, focusing on identifying…

his paper describes our techniques to detect hate speech against women and immigrants on Twitter in multilingual contexts, particularly in English and Spanish. The challenge was designed by SemEval-2019 Task 5, where the participants need…

Musicians frequently use social media to express their opinions, but they often convey different messages in their music compared to their posts online. Some utilize these platforms to abuse their colleagues, while others use it to show…

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the…

The paper presents a system developed for the SemEval-2019 competition Task 5 hat-Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in…

The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet subtle distinct categories of offensive speech. In this work,…

Women are influential online, especially in image-based social media such as Twitter and Instagram. However, many in the network environment contain gender discrimination and aggressive information, which magnify gender stereotypes and…

We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating…

In this paper we present deep-learning models that submitted to the SemEval-2018 Task~1 competition: "Affect in Tweets". We participated in all subtasks for English tweets. We propose a Bi-LSTM architecture equipped with a multi-layer self…

The increasing accessibility of the internet facilitated social media usage and encouraged individuals to express their opinions liberally. Nevertheless, it also creates a place for content polluters to disseminate offensive posts or…