Debunker Assistant: a support for detecting online misinformation
Authors: Arthur T. E. Capozzi, Alessandra Teresa Cignarella, Simona Frenda, Mirko Lai, Marco Antonio Stranisci, Alessandra Urbinati (Aequa-tech, Università di Torino, MOBS, Northeastern University, 2023)
Topic: Fact Checking and Fake News Detection

Abstract
This paper describes the framework developed for the Debunker-Assistant, an application that allows users and newspapers to assess the trustworthiness of a news item starting from its headline, body of text, and URL. The Debunker-Assistant adapts ideas from Natural Language Processing and Network Science to counter the spread of online misinformation. Its centerpiece is a set of four News Misinformation Indicators based on linguistically engineered features, models, and network analysis metrics (Echo Effect, Alarm Bell, Sensationalism, and Reliability). In this short contribution, we describe the back-end structure on which the indicators are implemented.

Introduction
Fake news threatens democracies, public health, and news outlets’ credibility. For this reason, tackling misinformation is an open challenge faced by governments, private companies, and the scientific communities [1]. There are many proposed approaches, some based on AI methods, others on fact-checking by human experts, and still others on a combination of the two [2, 3]. However, the fact that fake news detection algorithms are often owned by private social media companies, and additionally the adoption of “black-boxed” algorithms contribute to the lack of transparency in the fake news identification and filtering process. Debunker-Assistant (D-A) is a back-end AI tool which supports the analysis and detection of online misinformation. The D-A tool takes as an input the link of a news article and returns its misinformation profile based on Natural Language Processing (NLP) and Network Analysis (NA) features. Such an approach is inspired by the survey provided by [4]. D-A works with Italian and is not bounded to a specific topic: it is designed as a general-purpose tool that can extract relevant features for assessing the quality of information. Its main purposes are: displaying the indicators to deal with misinformation; de-biasing the mechanisms to make the internet trustworthy; showing insights about a certain context to aid the search and discovery of information. In this paper, we present the various features of NLP and NA embedded in the D-A tool and how these features have been designed and developed. As represented in Figure 1, D-A allows users to search for news and compare them against a given set of four macro-indicators of misinformation: Echo Effect, Alarm Bell, Sensationalism, and Reliability. These indicators are designed on the basis of specific linguistic and network features, such as the absence of sources, non-authority of references, the presence of specific figures of speech or flames, and other stylistic characteristics. To operationalize our objectives, we focus on three main actions: Inform: D-A can be freely used by citizens who want to check if content they have read includes potential misinformation signals; Support: D-A can also be adopted by journalists who work in the field of debunking, empowering their activity; Teach: Teachers can use D-A for designing information literacy activities in their classrooms.

Conclusion
D-A is a tool developed in support of detecting online misinformation. Its social impact is twofold. First, it contributes to counter misinformation and improve the quality of information. In fact, the tool is aimed at supporting those who deal with this issue every day. Second, it provides a better understanding of how these artificial intelligence technologies work in the context of the news media. Given the complex and ever-changing nature of content creation and information dissemination, there are several directions for improvement. For example, users could be involved in providing anonymous feedback on the news itself and on the characterization of the evaluated articles, improving the overall performance of the tool, especially for those features that are less explored in the literature. In addition, this type of interaction makes the user think about important aspects of online information, thus increasing awareness. Over time, as users search for new URLs, the core data that feed the models will expand to cover larger and more diverse sets of domains, incorporating a richer perspective on news consumption. To help these research directions, we plan to develop a user-friendly interface and evaluate the general user experience. Finally, a future challenge would be to scale the model for other languages starting from English.