Enhancing Society-Undermining Disinformation Detection through Fine-Grained Sentiment Analysis Pre-Finetuning (2024)

Abstract: In the era of the digital world, while freedom of speech has been flourishing, it has also paved the way for disinformation, causing detrimental effects on society. Legal and ethical criteria are insufficient to address this concern, thus necessitating technological intervention. This paper presents a novel method leveraging pre-finetuning concept for efficient detection and removal of disinformation that may undermine society, as deemed by judicial entities. We argue the importance of detecting this type of disinformation and validate our approach with real-world data derived from court orders. Following a study that highlighted four areas of interest for rumor analysis, our research proposes the integration of a fine-grained sentiment analysis task in the pre-finetuning phase of language models, using the GoEmotions dataset. Our experiments validate the effectiveness of our approach in enhancing performance significantly. Furthermore, we explore the application of our approach across different languages using multilingual language models, showing promising results. To our knowledge, this is the first study that investigates the role of sentiment analysis pre-finetuning in disinformation detection.

## introduction the advent of digitalization has significantly impacted societal discourse, notably manifesting in the phenomenon of "fake news," a term so ubiquitous that it was selected as the macquarie dictionary word of the decade. 1 fake news and disinformation have infiltrated every aspect of our lives, from politics and elections @xcite , to financial markets @xcite , and public health narratives @xcite . based on the intentions behind their dissemination, 1 https://www.macquariedictionary.com.au/blog/ article/780/ judgement example punishable the underworld member kept beating the victims in the private guest house. ## related work though the role of sentiment features in fake news detection has been examined extensively @xcite @xcite @xcite @xcite , it is noteworthy that little attention has been directed towards severe instances of disinformation, particularly society-undermining disinformation. @xcite delineated a research agenda for society-undermining disinformation detection, but did not propose a specific method to address this concern. @xcite introduced the concept of harmfulness assessment in relation to covid-19 misinformation. our research represents a pioneering effort to tackle societyundermining disinformation. further distinguishing our work is our exploration of the role of sentiment pre-finetuning tasks within this context, a topic which, to our knowledge, has not been previously explored. ## dataset in this study, our primary focus lies in the identification of disinformation that judges deem detrimental to societal harmony. consequently, we align our approach with the previous study @xcite , using court orders as our primary data source. our spreading rumors in a way that is sufficient to undermine public order and peace. the dataset statistics, related to paragraph 5, article 63 of the social order maintenance act in taiwan, are illustrated in in order to mitigate the potential for such stifling of free speech on future social media platforms, we propose that only severe disinformation, capable of undermining societal harmony, should be promptly identified and removed from the platform. other posts, like the impunity examples in given the unique nature of our dataset and the difficulty of reproducing similar scenario-based annotations, we utilize all available instances, embracing the real-world challenges of few-shot learning and class imbalance. to ensure a substantial test set, we divide the dataset into two halves, with 50% of instances assigned to the training set and the remaining to the test set. we make our dataset publicly available for replication and further investigation, adhering to the same licensing terms as used by readr. foot_3 ## methods drawing inspiration from the logic that judges apply in their courtroom decisions: although it is improper for the transferred person to post without verification and judgment, this post does not cause the listeners to fear or panic due to the untruth. to facilitate pre-finetuning of bert-chinese using the goemotions dataset, we translate all instances to chinese using the google translation api. as the nature of these court orders is unique, we aim to replicate application scenarios in other languages to identify potential performance gaps and assess the performance of a universal language model, i.e., mbert. for pre-finetuning mbert, we explore two settings: (1) using the original goemotions dataset, and (2) using the translated goemotions dataset. ## conclusion this paper has shed light on a critical, yet often overlooked, aspect of the discourse around false information: the detection of society-undermining disinformation. by conducting a series of rigorous experiments, we have established a notable connection between such disinformation and fine-grained sentiment labels. our innovative pre-finetuning approach equips language models with enhanced capabilities to detect such disinformation, improving their performance significantly across multiple language scenarios. moreover, the cross-lingual applicability of our pre-finetuning methodology underscores its robustness and versatility. it sets the stage for future investigations that could further refine this approach for different languages. however, we recognize that this is only the beginning. the insights and results obtained in this study represent a preliminary step towards a comprehensive understanding of society-undermining disinformation and the development of robust detection strategies. future research should continue to delve deeper into the complex interplay between disinformation, sentiment, and societal impact, exploring the diverse avenues we have outlined.