Tackling Multilinguality and Internationality in Fake News (2021)

Abstract: The last several years have seen a massive increase in the quantity and influence of disinformation being spread online. Various approaches have been developed to target the process at different stages from identifying sources to tracking distribution in social media to providing follow up debunks to people who have encountered the disinformation. One common conclusion in each of these approaches is that disinformation is too nuanced and subjective a topic for fully automated solutions to work but the quantity of data to process and cross-reference is too high for humans to handle unassisted. Ultimately, the problem calls for a hybrid approach of human experts with technological assistance. In this paper we will demonstrate the application of certain state-of-the-art NLP techniques in assisting expert debunkers and fact checkers as well as the role of these NLP algorithms within a more holistic approach to analyzing and countering the spread of disinformation. We will present a multilingual corpus of disinformation and debunks which contains text, concept tags, images and videos as well as various methods for searching and leveraging the content.

## introduction the topic of fake news and intentional spread of disinformation has been gaining increasing prominence over the last decade. the distinction of terminology and attempts to classify various erroneous or misleading statements online is constantly evolving but disinformation, as shown in @xcite , has recently settled as the commonly accepted term to describe the intentional and systematic spread of incorrect information. the spread of this incorrect information is strongly reliant on social media, causing a strong emotional reaction and quick propagation before its inaccuracy can be effectively exposed. this means that in most cases minimal effort is put into crafting the disinformation, instead relying on speed, volume and reuse of slightly modified pre-existing materials. there are, of course, always new disinformation materials popping up but they are in the minority and should they gain traction, they will very quickly get picked up, modified slightly and reused. a very stark example of this kind of interaction was provided in the early months of the covid pandemic as reliable scientifically-tested information was still rather scarce and the void was filled by a wide variety of rapidly-spreading fake and unsupported claims. this can be viewed both from the perspective of journalists mobilizing to counteract the spread 1 and from that of researchers looking into assisting their efforts e.g. in identifying spread of disinformation that has already been debunked as in @xcite or in tracking the comparative effect of disinformation and debunking tweets as in @xcite . this means that combating the spread of disinformation can happen on a variety of levels. one option is to identify the creator and limit their reach -a replacement will pop up eventually but rebuilding a presence in the social network requires time and resources. alternatively, it is possible to identify a piece of disinformation early in its spread and expose it to the people who interact with it before it really gains traction. finally, it is possible to monitor social media for trending topics and work on creating convincing well-supported debunks to new disinformation that has gained popularity. an ideal approach would combine all three aspects in some manner. finally, the reality is that while disinformation might typically involve minor or simple modifi-cations, it is still intentionally crafted to be misleading and is constantly evolving and adapting. this makes completely automated approaches to combating it impractical. meanwhile, the sheer volume of information that needs to be tracked, analysed and correlated makes a completely manual approach equally impractical. the solution will inevitably then involve a hybrid approach. to that end, we present a data set based on a collection of fact-checker created debunks of pieces of disinformation that has been extended with additional metadata. several forms of advanced search functionality have been developed on top of it in order to make discovering relevant content in the data set as straight-forward as possible. this will allow fact-checking experts to easily check for previous work on disinformation they encounter, point to previous instances of it being used and react quickly to its spread in order to counter it early on. ## the data set the data set used for our experiments is based on a snapshot of the database of known fakes foot_1 (dbkf). it is a collection of debunking content from highly respected fact-checking organizations around the world extended with additional metadata related to said debunks in order to enable the advanced search and correlation functionality we present here. figure 1 shows an overview of the major types of objects contained within the dbkf and the connections between them. at its core, the data model of the data set is based around the claim foot_2 and claim-review foot_3 format defined within schema.org which is already familiar to and used by many fact checking organizations. the core objects defined within the schema.org specification are a claim (a short statement summarizing the target of the debunk) and claimreview (a typically article-length debunk of the claim being discussed). as can be seen in figure 1 , these two objects are extended with additional explicit objects. the two most important additions are appearances and evidences -the former are links to posts where a specific claim is being made and the latter are external content supporting the explanation and reasoning contained within a debunk. appearances are automatically expanded to in-clude additional metadata available at the external website (more on that in subsection 3.1) and a number of state-of-the-art systems are used to enrich the objects further (more on that in subsections 3.3 and 4.2). some of this metadata is more detailed and contained within specialized objects such as image, video, concept and annotation (which is an instance of a concept at a specific location in a document's text). at the time of writing, the data set contains figure 2 provides a specific example of a debunk that might be retrieved by the system (pre-advanced enrichment steps). the claim is that an immigrant destroyed a statue in italy, three appearances provide links to three tweets that made that claim, the debunk disproves the claim and explains what actually happened while the evidences support that explanation. ## enrichment this section will go over the additional enrichment steps that are used to take the dbkf beyond a simple collection of existing debunks and unlock the ability to do advanced searching and correlation by expanding the metadata available on the debunks. this contains the additional metadata added to appearance and evidence objects, language tagging of all texts and named entity recognition. ## search the final step in unlocking the full potential of the data set is to enable powerful search functionality that can make discovering existing debunk information and locating similar cases of disinformation in the past as quick and easy as possible. this kind of searching should utilize the full capability of the collected and enriched data set and can be used as a stepping stone to semi-automated and automated systems such as early detection of disinformation and chat bots. it is also a first step to being able to detect larger trends within the data such as tracking the spread of a particular disinformation claim across countries or watching a particular piece of disinformation change and evolve in response to fact-checker debunks. there are several aspects of this search functionality that build on each other. firstly, we will describe the full-text and faceted search then nearduplicate detection based on visual similarity and multilingual search that uses latest neural machine translation. subsection 5.1 will also briefly discuss what we envision these search capabilities building to in the future. ## conclusion and future work in conclusion, the dbkf already contains a large amount of data extended with useful metadata and powerful search capability. this can make it a powerful tool in the arsenal of fact-checkers and also allows its incorporation in counter-disinformation campaigns where people are targeted with evidence of a claim's falsehood before they spread it unknowingly. the contents of the database are also constantly growing with the automatic ingestion of new content. future developments can include the addition of new fact-checking sources, support for metadata-expansion of more types of social media posts and further building on the modeling and search functionality.