There are two main current technologies to deal with the detection of disinformation. One, related to the needs of fact-checkers, focuses on the processing and analysis of single messages. The other, related to the detection of disinformation campaigns organised to influence a social network, relies on social network analysis: highly similar behaviour of different user accounts along time series are indications of a disinformation campaign. However, both research lines remain separate research fields, although one gives context to the other. In fact, current AI models for misinformation detection are limited in the ability to represent and consider contextual information. It is still a research frontier we want to address. HAMiSoN’s most breakthrough goal is the integration of different technologies at both message and social network levels into a single system. Although we plan to take advantage of the hidden variable they share (their intentionality), there are many research questions that have to be addressed. A straightforward approach would be to run all involved systems separately and then compare and combine their output. However, they don’t leverage each other's signals and, in fact, the current state of the art achieves rather low performance. The alternative we want to explore is what we call an “holistic” approach, where all tasks are considered simultaneously by one integrated system.

The holistic integration is an important stepping stone for modelling and detecting organised misinformation campaigns. Such campaigns consist of multiple messages and multiple related claims spread in a coordinated way through multiple pathways in social networks. We will articulate the integration of evidence coming from the message and network levels around the idea of disinformation intentionality: agents that create and introduce disinformation in the social media networks carefully select narratives aimed to have a concrete impact such as influencing the outcome of elections by discrediting political adversaries, influence financial markets, polarise and destabilise society, generate distrust, destroy reputation, etc. This adversarial game has, at the end, benefited and injured agents. Our hypothesis is that, given a scenario (e.g. political elections), the set of intentions will be finite (e.g. destroy opponent's reputation), and the narratives used to achieve it (e.g. X has money overseas) will be limited and predictable according to a general taxonomy. Once agents of disinformation select the narratives to achieve their intended goal, they create sets of different messages with the same hidden intent, and finally coordinate their spreading in the social networks through troll farms. Our methodology will start with the reverse path: gather evidence from message and social network levels and try to infer the hidden intent. Then, once the hidden intent is detected, come back to the message and network levels with all the aggregated evidence from the three levels and try to capture the items lost in the first round by the local approaches. The identification of harmful networks will make arise new misleading messages, and newly identified disinformation content can be collected enabling improved disinformation propagation path identification in a virtuous loop.

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