Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people’s canceling attitudes on social media. Specifically, we study the impact of annotators’ morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator’s judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.

In this work, we investigate various state-of-the-art (SOTA) speech pre-trained models (PTMs) for their capability to capture prosodic sig-natures of the generative sources for audio deepfake source attribution (ADSD). These prosodic characteristics can be considered oneof major signatures for ADSD, which is unique to each source. So better is the PTM at capturing prosodic signs better the ADSD per-formance. We consider various SOTA PTMs that have shown top performance in different prosodic tasks for our experiments on benchmark datasets, ASVSpoof 2019 and CFAD. x-vector (speaker recognition PTM) attains the highest performance in comparison to allthe PTMs considered despite consisting lowest model parameters. This higher performance can be due to its speaker recognition pre-training that enables it for capturing unique prosodic characteristics of the sources in a better way. Further, motivated from tasks suchas audio deepfake detection and speech recognition, where fusion of PTMs representations lead to improved performance, we explorethe same and propose FINDER for effective fusion of such representations. With fusion of Whisper and x-vector representations through FINDER, we achieved the topmost performance in comparison to all the individual PTMs as well as baseline fusion techniques and attaining SOTA performance.

This paper examines gender bias in Estonian, a grammatically genderless Finno-Ugric language, which doesn’t have gendered noun system nor any gendered pronouns, but expresses gender through vocabulary. In this work, we focus on the male-female compound words ending with -tädi ‘aunt’ and -onu ‘uncle’, aiming to pinpoint the occupations these words signify for women and men, and to examine whether they reveal occupational differentiation and gender stereotypes. The findings indicate that these compounds go beyond occupational titles and highlight prevalent gender bias.

In this work, we investigate multilingual speech Pre-Trained models (PTMs) for Audio deepfake detection (ADD). We hypothesize thatmultilingual PTMs trained on large-scale diverse multilingual data gain knowledge about diverse pitches, accents, and tones, during theirpre-training phase and making them more robust to variations. As a result, they will be more effective for detecting audio deepfakes. To validate our hypothesis, we extract representations from state-of-the-art (SOTA) PTMs including monolingual, multilingual as well as PTMs trained for speaker and emotion recognition, and evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark databases. We show that representations from multilingual PTMs, with simple downstream networks, attain the best performance for ADD compared to other PTM representations, which validates our hypothesis. We also explore the possibility of fusion of selected PTM representations for further improvements in ADD, and we propose a framework, MiO (Merge into One) for this purpose. With MiO, we achieve SOTA performance on ASV and ITW and comparable performance on DECRO with current SOTA works.

Newspapers have always remained an important medium for disseminating information to the masses. With continuous access and availability of news, there is a severe competition among news media agencies to attract user attention. Therefore, ensuring fairness in news reporting, such as, politically stance neutral reporting has become more crucial than before. Although several research studies have explored and detected political stance in English news articles, there is a lack of research focusing on low-resource languages like Estonian. To address this gap, this paper examines the effectiveness of established stance-detection features that have been successful for English news media, while also proposing novel features tailored specifically for Estonian. Our study consists of 32 different features comprising of lexical, Estonian-specific, framing and sentiment-related features out of which we identify 15 features as useful for stance detection.

Rhymes and poems are a powerful medium for transmitting cultural norms and societal roles. However, the pervasive existence of gender stereotypes in these works perpetuates biased perceptions and limits the scope of individuals’ identities. Past works have shown that stereotyping and prejudice emerge in early childhood, and developmental research on causal mechanisms is critical for understanding and controlling stereotyping and prejudice. This work contributes by gathering a dataset of rhymes and poems to identify gender stereotypes and propose a model with 97% accuracy to identify gender bias. Gender stereotypes were rectified using a Large Language Model (LLM) and its effectiveness was evaluated in a comparative survey against human educator rectifications. To summarize, this work highlights the pervasive nature of gender stereotypes in literary works and reveal the potential of LLMs to rectify gender stereotypes. This study raises awareness and promotes inclusivity within artistic expressions, making a significant contribution to the discourse on gender equality.