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Found 4 papers, 2 papers with code
Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation
In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students.
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models
Here we create a novel adversarial attack model against deep user sequence embedding based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users.
The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic
Concerned citizens (i. e., the crowd), who are users of the platforms where misinformation appears, can play a crucial role in disseminating fact-checking information and in countering the spread of misinformation.
By Hook or by Crook: Exposing the Diverse Abuse Tactics of Technical Support Scammers
Although recent research has provided insights into TSS, these scams have now evolved to exploit ubiquitously used online services such as search and sponsored advertisements served in response to search queries.
Cryptography and Security
