mlsanityData to 7 Oct 2026

Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages

arXiv 2204.11356 ↗First posted 24 Apr 2022cs.LG cs.CL cs.SIcited by 5

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Abstract (arXiv)

The rise in the number of social media users has led to an increase in the hateful content posted online. In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched languages. This hate speech is depicted with the help of images to form "Memes" which create a long-lasting impact on the human mind. In this paper, we take up the task of hate and offense detection from multimodal data, i.e. images (Memes) that contain text in code-switched languages. We firstly present a novel triply annotated Indian political Memes (IPM) dataset, which comprises memes from various Indian political events that have taken place post-independence and are classified into three distinct categories. We also propose a binary-channelled CNN cum LSTM based model to process the images using the CNN model and text using the LSTM model to get state-of-the-art results for this task.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 24 references that are in the map. First posted on arXiv 24 Apr 2022.

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5 of 24 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  4. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  5. 2018Mind Your Language: Abuse and Offense Detection for Code-Switched Languagescited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 3.

5 papers.