Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop systems for analyzing and verifying news spreading online. The SemEval-2023 Task 3 is an attempt to address several subtasks under this overarching problem, targeting writing techniques used in news articles to affect readers' opinions. The task addressed three subtasks with six languages, in addition to three ``surprise'' test languages, resulting in 27 different test setups. This paper describes our participating system to this task. Our team is one of the 6 teams that successfully submitted runs for all setups. The official results show that our system is ranked among the top 3 systems for 10 out of the 27 setups.

翻译：主流媒体和社交媒体上虚假信息的传播以多种方式误导用户。记者和事实核查员的人工检测与验证已难以应对误导性信息的大规模传播与快速扩散。这促使学术界和工业界开发用于分析和验证在线新闻传播的系统。SemEval-2023任务3旨在解决这一全局问题下的若干子任务，聚焦新闻文章中影响读者观点的写作技巧。该任务涉及六种语言的三个子任务，外加三种"惊喜"测试语言，形成了27种不同的测试设置。本文描述了我们参与该任务的系统方案。本团队是成功提交所有设置结果的六个团队之一。官方结果显示，在27种测试设置中，我们的系统在其中10种设置中位列前三。