Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter Discussions

• URL: http://arxiv.org/abs/2111.12939v1
• Date: Thu, 25 Nov 2021 06:35:49 GMT
• Title: Probabilistic Impact Score Generation using Ktrain-BERT to Identify Hate Words from Twitter Discussions
• Authors: Sourav Das, Prasanta Mandal, Sanjay Chatterji
• Abstract summary: This paper presents experimentation with a Keras wrapped lightweight BERT model to successfully identify hate speech. The dataset used for this task is the Hate Speech and Offensive Content Detection (HASOC 2021) data from FIRE 2021 in English. Our system obtained a validation accuracy of 82.60%, with a maximum F1-Score of 82.68%.
• Score: 0.5735035463793008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media has seen a worrying rise in hate speech in recent times. Branching to several distinct categories of cyberbullying, gender discrimination, or racism, the combined label for such derogatory content can be classified as toxic content in general. This paper presents experimentation with a Keras wrapped lightweight BERT model to successfully identify hate speech and predict probabilistic impact score for the same to extract the hateful words within sentences. The dataset used for this task is the Hate Speech and Offensive Content Detection (HASOC 2021) data from FIRE 2021 in English. Our system obtained a validation accuracy of 82.60%, with a maximum F1-Score of 82.68%. Subsequently, our predictive cases performed significantly well in generating impact scores for successful identification of the hate tweets as well as the hateful words from tweet pools.

Related papers

• Analysis and Detection of Multilingual Hate Speech Using Transformer Based Deep Learning [7.332311991395427]
  As the prevalence of hate speech increases online, the demand for automated detection as an NLP task is increasing. In this work, the proposed method is using transformer-based model to detect hate speech in social media, like twitter, Facebook, WhatsApp, Instagram, etc. The Gold standard datasets were collected from renowned researcher Zeerak Talat, Sara Tonelli, Melanie Siegel, and Rezaul Karim. The success rate of the proposed model for hate speech detection is higher than the existing baseline and state-of-the-art models with accuracy in Bengali dataset is 89%, in English: 91%, in German
  arXiv  Detail & Related papers  (2024-01-19T20:40:23Z)
• Into the LAIONs Den: Investigating Hate in Multimodal Datasets [67.21783778038645]
  This paper investigates the effect of scaling datasets on hateful content through a comparative audit of two datasets: LAION-400M and LAION-2B. We found that hate content increased by nearly 12% with dataset scale, measured both qualitatively and quantitatively. We also found that filtering dataset contents based on Not Safe For Work (NSFW) values calculated based on images alone does not exclude all the harmful content in alt-text.
  arXiv  Detail & Related papers  (2023-11-06T19:00:05Z)
• CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network [52.85130555886915]
  CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations. We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
  arXiv  Detail & Related papers  (2023-03-02T17:30:43Z)
• Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021 [50.591267188664666]
  We present two shared tasks of abusive and threatening language detection for the Urdu language. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. For both subtasks, m-Bert based transformer model showed the best performance.
  arXiv  Detail & Related papers  (2022-07-14T07:38:13Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model [0.5801044612920815]
  This paper investigates the feasibility of leveraging domain-specific word embedding in Bidirectional LSTM based deep model to automatically detect/classify hate speech. The experiments showed that domainspecific word embedding with the Bidirectional LSTM based deep model achieved a 93% f1-score while BERT achieved up to 96% f1-score on a combined balanced dataset from available hate speech datasets.
  arXiv  Detail & Related papers  (2021-11-02T11:42:54Z)
• Hate speech detection using static BERT embeddings [0.9176056742068814]
  Hate speech is emerging as a major concern, where it expresses abusive speech that targets specific group characteristics. In this paper, we analyze the performance of hate speech detection by replacing or integrating the word embeddings. In comparison to fine-tuned BERT, one metric that significantly improved is specificity.
  arXiv  Detail & Related papers  (2021-06-29T16:17:10Z)
• An Information Retrieval Approach to Building Datasets for Hate Speech Detection [3.587367153279349]
  A common practice is to only annotate tweets containing known hate words'' A second challenge is that definitions of hate speech tend to be highly variable and subjective. Our key insight is that the rarity and subjectivity of hate speech are akin to that of relevance in information retrieval (IR)
  arXiv  Detail & Related papers  (2021-06-17T19:25:39Z)
• Leveraging Multilingual Transformers for Hate Speech Detection [11.306581296760864]
  We leverage state of the art Transformer language models to identify hate speech in a multilingual setting. With a pre-trained multilingual Transformer-based text encoder at the base, we are able to successfully identify and classify hate speech from multiple languages.
  arXiv  Detail & Related papers  (2021-01-08T20:23:50Z)
• Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis [51.39895377836919]
  COVID-19 has sparked racism and hate on social media targeted towards Asian communities. We study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
  arXiv  Detail & Related papers  (2020-05-25T21:58:09Z)
• Demoting Racial Bias in Hate Speech Detection [39.376886409461775]
  In current hate speech datasets, there exists a correlation between annotators' perceptions of toxicity and signals of African American English (AAE) In this paper, we use adversarial training to mitigate this bias, introducing a hate speech classifier that learns to detect toxic sentences while demoting confounds corresponding to AAE texts. Experimental results on a hate speech dataset and an AAE dataset suggest that our method is able to substantially reduce the false positive rate for AAE text while only minimally affecting the performance of hate speech classification.
  arXiv  Detail & Related papers  (2020-05-25T17:43:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.