Linking Stance and Stereotypes About Migrants in Italian Fake News
Authors: Alessandra Teresa Cignarella, Simona Frenda, Tom Bourgeade, Cristina Bosco, Francesca D'Errico (Università di Torino, Aequa-tech, Università di Bari Aldo Moro, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This paper investigates stance and stereotypes within a dataset of Italian Twitter conversational threads. These conversations begin with tweets containing misinformation in the form of racial hoaxes targeting migrants, flagged as untrustworthy by fake news debunking websites. The conversational structure of the dataset allows us to observe and collect evidence on linguistic and social phenomena involved in the spread of stereotypes and the interactions between users that arise from them. We offer a theoretical framework, along with quantitative and qualitative analyses of our annotated data at various levels of granularity, providing insights into the dynamics of Italian online discourse surrounding migration.

Introduction
In the era of information overload and widespread digital communication, terms like “disinformation,” “fake news,” “hoaxes,” “misinformation,” and “rumors” have become buzzwords dominating public discourse. While these terms are often used interchangeably, it’s important to recognize the nuanced differences among them and establish clear terminology. “Fake news” refers to fabricated or misleading information presented as legitimate news, often intended to deceive or manipulate public opinion. “Disinformation” encompasses a broader range of intentionally false or misleading information disseminated with the aim of influencing beliefs or actions. Conversely, “rumor” refers to unverified or unsubstantiated pieces of information that circulate widely within communities. Recently, a specific type of rumor that spreads claims threatening the health or safety of individuals or groups based on race, ethnicity, or religion has been defined as a “racial hoax” by Cerase and Santoro, updating an earlier definition from Russell. This paper aims to highlight a connection between research investigating these phenomena and research conducted in the field of Stance Detection (SD). As illustrated in Figure 1, a recent survey by Küçük and Can outlines relationships among various tasks and subtasks in Sentiment Analysis, emphasizing SD at the center. In the blue-highlighted boxes in Figure 1, we see that Rumor Stance Classification and Fake News Stance Classification are closely related to SD. In this work, we connect stance and stereotypes by re-annotating our previous corpus, focusing specifically on the conversational structure of the data. The paper is organized as follows: Section 2 surveys related work on Fake News and Stance Detection on one hand, and Racial Hoaxes and Stereotypes on the other. Section 3 describes the corpus collection and the annotation process, particularly regarding stance. In Section 4, we conduct a corpus-based analysis, also considering stereotypes, and provide examples from the corpus. Finally, Section 5 discusses insights gained from observing annotated phenomena, concluding with final remarks and potential directions for future research.

Conclusion
In this article, we explored the expression of stance and stereotypes within a dataset of Twitter conversational threads in Italian, focused on the topic of migration. The dataset consists of dialogues originating from tweets containing misinformation marked as untrustworthy by experts. The analysis of the dataset shed light on the distribution of stance labels and their relationship with stereotypes. The majority of tweets were annotated as Comment, followed by Support, Query, and finally Deny. While there was no significant co-occurrence between stance and stereotypes, tweets annotated as Support were more likely to contain racial stereotypes. On the other hand, tweets annotated as Comment were less likely to exhibit forms of stereotyping. The corpus analysis provided insights into how the structure and nature of conversations, as well as lexical choices in messages, affect the perceived stance of users towards racial hoaxes. In conclusion, this work paves the way for further investigations into topics closely related to the social phenomenon of misinformation, which should be countered to stimulate accurate information dissemination and create a more inclusive online environment. In future research, we may increase the size of the dataset, improve the annotation guidelines, and consider the feedback provided by the annotators. We may also further investigate the relationship between stance and stereotypes, as well as explore interventions to mitigate the harmful effects of stereotypes in online conversations.