Memes and text-embedded images have rapidly become compelling cultural artifacts that both facilitate expressive communication and serve as conduits for spreading hate speech against marginalized communities. Detecting hate speech within such multimodal content poses significant challenges due to the complex and subtle interplay between textual and visual elements. This paper presents our approach for Subtask A of the Shared Task on Multimodal Hate Detection in Marginalized Movement@CASE 2025, focusing on the binary classification of memes into Hate or No Hate categories. We propose a novel multimodal architecture that integrates DistilBERT for textual encoding with Vision Transformer (ViT) for image representation, combined through an advanced late fusion mechanism leveraging multi-head attention. Our method utilizes attention-based feature alignment to capture nuanced cross-modal interactions within memes. The proposed system achieved an F1-score of 0.7416 on the test set, securing the 13th position in the competition. These results underscore the value of sophisticated fusion strategies and attention mechanisms in comprehending and detecting complex socio-political content embedded in memes.

This paper introduces a new large-scale n-gram corpus that is created specifically from social media text. Two distinguishing characteristics of this corpus are its monthly temporal attribute and that it is created from 1.65 billion comments of user-generated text in Reddit. The usefulness of this corpus is exemplified and evaluated by a novel Topic-based Latent Semantic Analysis (TLSA) algorithm. The experimental results show that unsupervised TLSA outperforms all the state-of-the-art unsupervised and semi-supervised methods in SEMEVAL 2015: paraphrase and semantic similarity in Twitter tasks.

This paper presents a new corpus-based method for calculating the semantic similarity of two target words. Our method, called Second Order Co-occurrencePMI (SOC-PMI), uses Pointwise Mutual Information to sort lists of important neighbor words of the two target words. Then we consider the words which are common in both lists and aggregate their PMI values (from the opposite list) to calculate the relative semantic similarity. Our method was empirically evaluated using Miller and Charler’s (1991) 30 noun pair subset, Ruben-stein and Goodenough’s (1965) 65 noun pairs, 80 synonym test questions from the Test of English as a Foreign Language (TOEFL), and 50 synonym test questions from a collection of English as a Second Language (ESL) tests. Evaluation results show that our method outperforms several competing corpus-based methods.