A RAFT agent bearing a carboxylic acid group is applied to the miniemulsion polymerization of methyl methacrylate (MMA) in order to prepare the stability-enhanced functionalized latex. At the polymerization temperatures of 60, 70, and 80 °C, the polymerization kinetics, evaluation of the molecular weight, and PDI are found to be strongly dependent on the temperature. The higher the temperature, the faster the polymerization rate, the lower the molecular weight, and the lower the PDI are obtained. The PMMA nanoparticles prepared by the miniemulsion polymerization using this RAFT agent show some interesting characteristics. As the amounts of the RAFT agent increase, the magnitude of the zeta potential and the conductivity correspondingly increase and the size of the PMMA nanoparticle decreases from 118.8 to 49.5 nm. These results imply that the carboxyl group (or partially in anionic form) is present on the surface of the polymer particles, and therefore, the stability of the system is enhanced. Furthermore, no noticeable sign of creaming or destabilization of the PMMA nanoparticles was observed for at least several months by remaining as a homogeneous latex.

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Online trolls increase social costs and cause psychological damage to individuals. With the proliferation of automated accounts making use of bots for trolling, it is difficult for targeted individual users to handle the situation both quantitatively and qualitatively. To address this issue, we focus on automating the method to counter trolls, as counter responses to combat trolls encourage community users to maintain ongoing discussion without compromising freedom of expression. For this purpose, we propose a novel dataset for automatic counter response generation. In particular, we constructed a pair-wise dataset that includes troll comments and counter responses with labeled response strategies, which enables models fine-tuned on our dataset to generate responses by varying counter responses according to the specified strategy. We conducted three tasks to assess the effectiveness of our dataset and evaluated the results through both automatic and human evaluation. In human evaluation, we demonstrate that the model fine-tuned on our dataset shows a significantly improved performance in strategy-controlled sentence generation.