Proceedings of the 21st International Conference on Natural Language Processing ( ICON ): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate) (2024)

Abstract: The rapid expansion of social media has led toan increase in code-mixed content, presentingsignificant challenges in the effective detectionof hate speech and fake narratives. To advanceresearch in this area, a shared task titled De-coding Fake Narratives in Spreading HatefulStories (Faux-Hate) was organized as part ofICON 2024. This paper introduces a multi-task learning model designed to classify Hindi-English code-mixed tweets into two distinct cat-egories: hate speech and false content. The pro-posed framework utilizes fastText embeddingsto create a shared feature space that adeptly cap-tures the semantic and syntactic intricacies ofcode-mixed text, including transliterated termsand out-of-vocabulary words. These sharedembeddings are then processed through twoindependent Support Vector Machine (SVM)classifiers, each specifically tailored for oneof the classification tasks. Our team, secured10th place among the participating teams, asevaluated by the organizers based on Macro F1scores.

## introduction the rise of social media platforms has revolutionized the way people communicate, but it has also introduced significant challenges, particularly in terms of the spread of harmful and toxic content. one of the most concerning issues emerging in recent times is faux hate, a novel form of hate speech that arises from the intersection of fake narratives and hate speech. in many instances, individuals unknowingly express hateful opinions based on fabricated claims that have gained traction online. this dangerous combination not only fuels further divisiveness but also perpetuates the spread of false information, creating an environment where harmful rhetoric can thrive unchecked. faux hate is a growing issue, particularly in the context of online communities where misinformation circulates rapidly, exacerbating the spread of hatred. the challenge with faux hate lies in its complexity and the difficulty of identifying it in many cases. while traditional hate speech detection models have made significant strides @xcite , faux hate presents an additional layer of complexity. hateful comments fueled by false claims can often appear indistinguishable from genuine hate speech, especially if the underlying fake narrative is not widely recognized or acknowledged. this makes detection particularly difficult, as it requires not only identifying the harmful speech but also understanding the falsehood that triggers it. in cases where the fake claim is not obvious or well-known, identifying faux hate can be a formidable task, even for sophisticated automated systems. what sets faux hate apart from conventional hate speech is the need for a deeper understanding of the context in which the hate is generated. hate speech that stems from misinformation or fake claims does not exist in isolation-it is intricately linked to the narratives that are propagated alongside it. recognizing faux hate, therefore, requires both an awareness of the falsity of the claim and an understanding of how this claim may fuel or amplify hate speech. this intersection of fake narratives and harmful rhetoric represents a novel challenge in the field of online content moderation and demands a tailored approach to detection that goes beyond the capabilities of traditional hate speech classification systems. to address this emerging problem, the shared task on faux hate detection was introduced as part of icon 2024, providing a unique opportunity for the research community to develop and evaluate methodologies specifically aimed at identifying faux hate. as a part of shared task we have released feud 1 data set comprising of 8000 fakeinstigated hateful comments in hindi-english codemixed text includes real-world examples of online discourse, where participants were challenged to distinguish between genuine hate speech and faux hate generated by fake narratives using multi-task learning. this task encourages researchers to explore innovative approaches that not only identify harmful speech but also detect the underlying false claims that give rise to it. the significance of this task lies in its potential to advance the state of the art in hate speech detection. by focusing on faux hate, the shared task draws attention to the need for more nuanced models that can understand the complexities of online discourse. in this paper, we present the results of the competition, analyzing the performance of different methodologies submitted by participants. the structure of the paper is as follows: section 2 provides an overview of the label taxonomy. sections 4 and 5 describe the dataset used in the shared task, outlining the task setup and the evaluation metrics employed. section 6 presents a comprehensive analysis of the methodologies used by the participants. section 7 discusses the findings of the shared task. ## label taxonomy the feud data set is annotated using a two-level hierarchical structure. the annotation process begins by categorising social media comments into fake and non-fake labels. subsequently, the fake and non-fake content undergo a further labelling process to determine the presence of hate content, forming the binary class labelling stage. in the second phase, the focus shifts exclusively to hate labels, enabling more fine-grained multi-class labelling for target and severity annotation. these designated labels are crucial in understanding hateful content's complex nature, facilitating a more comprehensive and insightful analysis. the employed taxonomy for annotating the proposed data set is as follows: fake: fake label denotes comments deliberately crafted to spread misinformation with the potential to mislead readers @xcite . comments bearing false content are assigned the fake label '1', while others receive '0'. hate: hate label refers to comments that target and marginalize individuals or communities based on attributes such as religion, physical appearance, skin colour, ethnicity, and political opinion @xcite . authors label comments with hateful content as '1' and those without as '0'. target target label represents the subject of interest in hate posts. based on the target audience, hate speech is further categorized into three sub-classes. severity determining the severity level is essential to ensure freedom of expression while appropriately addressing hate speech. in the proposed data set, each hateful comment is classified into one of three severity sub-classes. ## tasks we proposed to include the following sub-tasks as part of this shared task. the objective of this sub-task is to develop a single model that outputs both the fake and hate labels for each text sample. the objective of this sub-task is to develop a single model that generate both the target and severity labels for given text sample. ## data & resources we gathered data from multiple social media platforms, including twitter and youtube, covering various topics such as religion, sports, health, politics, finance, and entertainment to ensure data diversity. initially, fake narratives were sourced from reputable fact-checking websites like alt-news, boomlive, and factly. these fake narratives were then used as search queries to extract hateful reactions. the data was annotated using crowdsourcing. further details on data annotation and the annotation guidelines are provided in @xcite . the detailed description of the dataset can be found in the proposed feud corpus is highly imbalanced, especially for task b. to address this, we used the macro f1 score as an evaluation parameter, which gives equal importance to all class labels. this approach is beneficial for assessing the performance of minority labels without prioritizing the majority label. by opting for the macro f1 score, we ensure that all class labels are given equal weight in the evaluation. ## system description this section describes the systems submitted for the shared task. a total of 22 teams from various institutes registered for the shared task. out of these, 12 teams participated in task a, which focuses on binary classification, and 10 teams participated in task b, which involves more fine-grained classification with multi-task learning. several teams submitted more than one run during the competition. this section briefly discusses the methodologies used in each submitted model. the team dcst_unigoa @xcite ) participated in both task a and task b. they used hingroberta, a pre-trained transformer finetuned on hindi-english code-mixed text with a class weightage technique, securing first place in both tasks. team rmk_mithra @xcite used tf-idf-based features with automl models in their proposed team tensortext @xcite used an xlm-roberta-based ensemble network with multi-task learning, securing 4th place in task a and 5th place in task b. team radicaldecoders @xcite employed a hard parameter shared xlm-roberta and hatebertbased multi-task learning model, securing 2nd place in task a and 3rd place in task b. team vector_visionaries @xcite developed an attention-weight-tuned lora adopterbased model for building task-specific classification heads, securing 13th place in task a. lastly, team vtu_bgm @xcite used a fasttext-based multi-task learning approach, securing 10th place in task a. ## discussion among the submitted teams, dcst_unigoa secured the first rank in both task a and task b, achieving a macro f1 score of 0.6155. they used a pre-trained hingroberta model, trained on hindi-english text, to address the faux-hate issue. key observations from the shared task include that domain-specific and language-agnostic pre-trained models achieved better results than other models. additionally, most models failed to achieve better results for the more fine-grained classification in task b. among the submitted teams, the popular choice was a hard parameter shared encoder model, with a task-specific classification layer on top to solve this multi-task learning problem. the leaderboard for both task a and task b is illustrated in ## conclusion this paper provides an overview of the models submitted to our first shared task on faux-hate identification, part of the icon 2024 conference. the shared task addresses the critical issue of faux-hate content detection in hindi-english code-mixed text through multi-task learning. a total of 15 teams participated in task a, and 13 teams participated in task b. this work aims to promote research in this critical category of hate speech, which is particularly harmful.