Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research

Hardage, David; Najafirad, Peyman

doi:10.18653/v1/2020.nlpcovid19-2.36

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…The authors of (Hardage and Najafirad, 2020) addressed the interpretability problems associated with hateful and offensive speech. They suggested a unique application of learned feature significance that outperformed earlier state-of-the-art text classification algorithms and delivered more transparent results.…”

Section: Hate Speech and Sentiment Analysismentioning

confidence: 99%

HatEmoTweet: Low Level Emotion Classifications and Spatio-Temporal Trends of Hate and Offensive COVID-19 Tweets

Adesokan

Madria

Nguyen

2023

Preprint

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Social media platforms (like Twitter) positively and negatively impact users in diverse societies; one of Twitter’s negative effects is the usage of hate and offensive language. Hate speech fosters prejudice; it also harms the vulnerable. There are always emotions associated with hateful and offensive actions. This work addressed hate and offensive tweet detection, low-level emotional classifications using 28 labels to train transformers models in three ways (model 1-BERTG28, model 2-BERTG27 and model 3-RoBERTaG27) before predicting the hateful and offensive tweets emotions. Model 1 was trained on low-level labels, and model 2 and 3 were trained on 27 labels excluding the neutral label. This study performed topic modeling to extract the discussed theme, spatiotempo-ral trends to determine where and when these tweets occurred, and event summarization for identified hate and offensive tweets. GoEmotions and Ekman were used for direct and indirect assessment, respectively, to evaluate the model’s precision, recall, and F1-score. In terms of precision evaluation, the model 1 outperformed Google Research on GoEmo-tions. Furthermore, this study’s model 2 and 3 outperformed the Google research on both the GoEmotions and Ekman’s evaluation in terms of precision and F1-score. Generally, model 2 was the best model in the Social Network Analysis and Mining analysis for both recall and F1-score while model 3 performed better for precision. Due to the training on samples without the neutral label, model 2 obtained 27% and model 3 achieved 29% label prediction out of the 30% neutral samples that was predicted in model 1 for hate and offensive tweets. This is a significant improvement to optimize classified emotions that are not truly neutral by eliminating the false neutral classes.

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Section: Hate Speech and Sentiment Analysismentioning

confidence: 99%

HatEmoTweet: Low Level Emotion Classifications and Spatio-Temporal Trends of Hate and Offensive COVID-19 Tweets

Adesokan

Madria

Nguyen

2023

Preprint

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“…Besides, there are also many case studies of hate speech on world topics such as immigrants (Capozzi et al 2019;Indurthi et al 2019), refugees (Frías-Vázquez and Pérez 2019; Frías-Vázquez and Arcila 2019), and presidential elections (Grimminger and Klinger 2021;Siegel et al 2021). Recently, anti-Asian hate speeches have also received a lot of attention due to the outbreak of COVID-19 (Hardage and Najafirad 2020;Fan, Yu, and Yin 2020;Vishwamitra et al 2020). As a text classification task, the most important factor in hate speech detection is the construction of effective features.…”

Section: Related Workmentioning

confidence: 99%

Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference

Ning

2022

ICWSM

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With the spreading of hate speech on social media in recent years, automatic detection of hate speech is becoming a crucial task and has attracted attention from various communities. This task aims to recognize online posts (e.g., tweets) that contain hateful information. The peculiarities of languages in social media, such as short and poorly written content, lead to the difficulty of learning semantics and capturing discriminative features of hate speech. Previous studies have utilized additional useful resources, such as sentiment hashtags, to improve the performance of hate speech detection. Hashtags are added as input features serving either as sentiment-lexicons or extra context information. However, our close investigation shows that directly leveraging these features without considering their context may introduce noise to classifiers. In this paper, we propose a novel approach to leverage sentiment hashtags to enhance hate speech detection in a natural language inference framework. We design a novel framework SRIC that simultaneously performs two tasks: (1) semantic relation inference between online posts and sentiment hashtags, and (2) sentiment classification on these posts. The semantic relation inference aims to encourage the model to encode sentiment-indicative information into representations of online posts. We conduct extensive experiments on two real-world datasets and demonstrate the effectiveness of our proposed framework compared with state-of-the-art representation learning models.

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“…Using lexicon-based approaches and the Perspective-API, they achieved the automatic labelling of these data [16]. Hardage et al practiced a toxic and HSD study on COVID-19 related tweets using GloVE and CNN methods [17]. Specifically, they aimed to increase the false positive evaluation criterion.…”

Section: Background Of Hsd Throughout Covid-19mentioning

confidence: 99%

Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

Baydogan

Alataş

2022

Teh. vjesn.

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The use of various online social media platforms rising day by day caused an increase in the correct or incorrect information shared by users, especially during COVID-19. The introduction of COVID-19 on the world agenda gave rise to an overall bad reaction against East Asia (esp. China) in online social media platforms. The social media users who spread degrading, racist, disrespectful, abusive, discriminatory, critical, abuse, harsh, offensive, etc. posts accused the Asian people of being responsible for the outbreak of COVID-19. For this reason, the development of the Hate Speech Detection (HSD) system was necessary in order to prevent the spread of these posts about COVID-19. In this article, a textual-based study on COVID-19-related hate speech (HS) sharing in online social networks was carried out with Shallow Learning (SL) and Deep Learning (DL) methods. In the first step of this study, typical Natural Language Processing (NLP) pipeline was applied for gathered two different datasets. This NLP pipeline was performed using bag of words, term frequency, document matrix, etc. techniques for features extraction representing datasets. Then, ten different SL and DL models were fine-tuned for HS datasets related to COVID-19. Accuracy, precision, sensitivity, and F-score performance measurement criteria were calculated to compare the performance of the SL and DL algorithms for the problem of HSD. The RNN, one of the models proposed for the first and second dataset in HSD, prevailed with the highest accuracy values of 78.7% and 90.3%, respectively. Due to the promising results of all approaches operated in the HSD, they are forecasted to be chosen in the solution of many other social media and network problems related to COVID-19.

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Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research

Cited by 10 publications

References 12 publications

HatEmoTweet: Low Level Emotion Classifications and Spatio-Temporal Trends of Hate and Offensive COVID-19 Tweets

HatEmoTweet: Low Level Emotion Classifications and Spatio-Temporal Trends of Hate and Offensive COVID-19 Tweets

Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference

Deep-Cov19-Hate: A Textual-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks throughout COVID-19 with Shallow and Deep Learning Models

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