Challenges for Automatic Detection of Fake News Related to Migration : Invited paper

Abstract

Fake news and misinformation is a widespread phenomenon these days, affecting social media, alternative and traditional media. In a climate of increasing polarization and perceived societal injustice, the topic of migration is one domain that is frequently the target of fake news, addressing both migrants and citizens in host countries. The problem is inherently a multi-lingual and multi-modal one in that it involves information in an array of languages, material in textual, visual and auditory form and often involves communication in a language which may be unfamiliar to recipients or which these recipients only may have basic knowledge of. We argue that semi-automatic approaches, empowering users to gain a clearer picture and base their decisions on sound information, are needed to counter the problem of misinformation. In order to deal with the scale of the problem, such approaches involve a variety of technologies from the field of Artificial Intelligence (AI). In this paper we identify a number of challenges related to implementing approaches for the detection of fake news in the context of migration. These include collecting multi-lingual and multi-modal datasets related to the migration domain, providing explanations of AI tools used in verification to both media professionals and consumers. Further efforts in truly collaborative AI will be needed.

Misinformation and Its Impact on Contested Policy Issues: The Example of Migration Discourses

“…Undoubtedly, misinformation has an impact throughout the migration cycle: in countries of origin-this is often through depicting destination societies as a mythical or idealized place of great wealth, prosperity, and opportunity, "Eldorado"-a destination that holds the promise of a better life, attracting individuals seeking improved living conditions, economic prospects, or social stability, giving false hope or unrealistic expectations [8]; in circumventing immigration rules that often ignore the complex reality of migration policymaking [32]; in fostering or hampering the socioeconomic inclusion of migrants in host communities [33]; and in the social rejection that often surrounds the return of migrants to their countries of origin-when migrants return to their countries of origin, they may encounter difficulties in reintegrating into their communities due to stigmatization, cultural readjustment, economic difficulties, and limited social support networks. The complexity of analyzing misinformation about migration lies in the frequent involvement of multiple languages, representation forms, and communication, which may be unfamiliar or not fully understandable to its recipients [34].…”

Deepfakes in Cyber Warfare: Threats, Detection, Techniques and Countermeasures

“…It is a relatively new field of research, and academics from both academia and industry have contributed deepfake databases, as well as synthesis and detection algorithms, all of which have contributed to the rise in popularity of deep fakes [13,14]. "Deepfakes are the outcome of artificial intelligence (AI) applications that merge, combine, replace, and superimpose photos and video clips to generate fake videos that look to be legitimate" [15]. Deepfakes take advantage of current developments in deep neural networks to produce artificial media that is extremely lifelike [16].…”

A Collaborative Intelligence Approach to Fighting COVID-19 False News: A Chinese Case

“…Humprecht [8], after summarizing the characteristics of fake news and analyzing its different effects in different countries, found that online disinformation is not only a technology-driven phenomenon but is also shaped by national information environments. While these and other authors have applied human-machine collaboration methods in their studies [19,3,14,2,22,20], which play an important role in improving the accuracy of fake news detection, bringing together IT experts, universities, and media to devise technologies that can help journalists to find fake online claims and for readers to verify uncertain sources. However, little research has thus far focused on COVID-19 related fake news detection, and there is no relevant dataset in Chinese for machine learning training.…”