Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks (2021)

Abstract: Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in today’s society. However, this has also enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs. Detecting fake news is important to prevent misinformation and maintain a healthy society. While prior works have tackled this problem by building supervised learning systems, automatedly modeling the social media landscape that enables the spread of fake news is challenging. On the contrary, having humans fact check all news is not scalable. Thus, in this paper, we propose to approach this problem interactively , where human insight can be continually combined with an automated system, enabling better social media representation quality. Our experiments show performance improvements in this setting.

## introduction over the last decade, an increasing number of people access news online @xcite , often using social networking platforms to engage, consume and propagate this content in their social circles. social networks provide easy means to distribute news and commentary, resulting in a sharp increase in the number of media outlets @xcite , and a rapid spread of content. in particular, false news stories tend to spread at lightning speeds, and due to the volume, cannot be checked manually. an alternative to fact-checking claims, which is arguably easier to scale, is to focus on their source, and ask who can you trust? prior works have formulated this as a traditional classification problem using techniques such as feature-based svm's @xcite , and more recently graph neural networks (gnns) @xcite @xcite , which create a better representation of social media interactions. graphs often consist of nodes corresponding to news sources (associated with a discrete factuality level -high, low, or mixed), the articles they release, and their social context, corresponding to social media users engaging and sharing information in their networks. gnns can utilize this information by using edge interactions to create node representations contextualized by their graph neighbours. this leads to a stronger representation of the complex information landscape on social media that enables fake news to spread, allowing it to be better detected. for this reason, we adopt graphs as our automated framework (1). despite the success of these works, fake news detection is still a challenging research problem and human performance is significantly higher than fully automated systems @xcite . clearly, having humans fact check every information source is not scalable. thus, our goal in this paper is to explore a different form of interaction with humans, where they can provide advice @xcite to the automated system. advice corresponds to localized judgements (provided through natural language) that help characterize the content and social interactions associated with sources. these judgements, associated with article and social media user nodes, are then propagated through the information graph using the gnn, allowing the system to take advantage of it to improve it's representation. as advice is not providing source labels directly, which is a timeconsuming process requiring a global view of the source's interactions, it is scalable. for example, one challenging aspect of the problem is that low-factuality ("fake news") sources may not always propagate false information (some of the articles they publish may be factual), and vice-versa (leading to model confusion). human interaction, in the form of advice, can help clean up some of this uncertainty, by identifying claims containing egregious falsehoods. the model could then use this information and trust sources making these claims less. we refer to this form of advice, mapping a specific article to known falsehoods as relevant claim advice. in another case, referred to as news spreader advice, a human could inform the system that a user that is spreading a sources' articles frequently spreads lies, which would increase the likelihood that that source and any other source this user spreads articles from are fake. fig 1 shows how both of these advice types can be seamlessly added to an information graph. in this work, we show that our protocol in which humans iteratively provide these types of advice by interacting with the model (even after it is trained) improves overall fake news detection performance. in summary, we formulate fake news source detection as a reasoning problem over an information graph. we then suggest an interactive learning based approach for incorporating human knowledge as advice to clean up uncertain graph decisions, which allows us to better learn and reason on this graph. finally, we perform experiments that demonstrate that this setup leads to performance improvements on fake news source detection. ## summary and future work in this paper, we proposed an approach to tackle fake news detection interactively by designing a protocol for a graph based system to continuously solicit human advice, and take advantage of it to improve overall information quality, which enables better fake news detection performance. we showed the benefits of two forms of advice (relevant claims and news spreaders), provided either all at once or continuously. in the future, we plan to have humans actually provide this advice, and explore other advice types.