Machine Learning-enhanced digital Information Operations of the People's Republic of China in the Republic of Korea
Digitální informační operace Čínské lidové republiky v Jižní Koreji podpořené strojovým učením
diploma thesis (DEFENDED)
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http://hdl.handle.net/20.500.11956/177207Identifiers
Study Information System: 225305
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- Kvalifikační práce [19704]
Author
Advisor
Referee
Kaczmarski, Marcin
Schlotti, Jivanta
Faculty / Institute
Faculty of Social Sciences
Discipline
International Master in Security, Intelligence and Strategic Studies (IMSISS)
Department
Department of Security Studies
Date of defense
16. 9. 2020
Publisher
Univerzita Karlova, Fakulta sociálních vědLanguage
English
Grade
Excellent
Keywords (English)
Information Operation, Republic of Korea, People's Republic of China, Machine Learning, Risk Society Theory, Natural Language Processing, Chinese Communist Party, Disinformation, Vulnerability, Non-Linear WarfareMachineLearning-enhanceddigitalInformationOperationsofthePeople's Republic ofChinain the Republic ofKorea Hendrik Arne Pasligh Abstract This study addressesthe research question if, how and to what end the People'sRepublic of China (PRC) might deploy digital information operations enhanced by machine learning (ML) technology in and against the Republic of Korea (ROK). To do so, Ulrich Beck's risk society theory is employed as the theoretical framework, which provides valuable insights into the environment in which information operations are conducted today. This environment is susceptible to information operations on a qualitatively fundamentally different new level. Further, this study establishes a terminology of information operations, bringing clarity to several ill-defined terms that prevail within academic literature. A scenario will be built to visualise a potential PRC information operation against the ROK. The majority of the study seeksto identify and analyse the relevant factors for such a scenario, particularly focusing on PRC strategic interestsandROK vulnerabilities against information operations.This studyfinds that: It is very likely that ML-enhanced artificial agents will increasingly be able to pose as human beings in the digital world; it is very likely that historical issues betweenthe...
