Social media serves as a critical medium in modern politics because it both reflects politicians' ideologies and facilitates communication with younger generations. We present MultiParTweet, a multilingual tweet corpus from X that connects politicians' social media discourse with German political corpus GerParCor, thereby enabling comparative analyses between online communication and parliamentary debates. MultiParTweet contains 39 546 tweets, including 19 056 media items. Furthermore, we enriched the annotation with nine text-based models and one vision-language model (VLM) to annotate MultiParTweet with emotion, sentiment, and topic annotations. Moreover, the automated annotations are evaluated against a manually annotated subset. MultiParTweet can be reconstructed using our tool, TTLABTweetCrawler, which provides a framework for collecting data from X. To demonstrate a methodological demonstration, we examine whether the models can predict each other using the outputs of the remaining models. In summary, we provide MultiParTweet, a resource integrating automatic text and media-based annotations validated with human annotations, and TTLABTweetCrawler, a general-purpose X data collection tool. Our analysis shows that the models are mutually predictable. In addition, VLM-based annotation were preferred by human annotators, suggesting that multimodal representations align more with human interpretation.

翻译：社交媒体作为现代政治的重要媒介，既反映了政治家的意识形态，也促进了与年轻一代的沟通。本文提出MultiParTweet，这是一个来自X平台的多语言推文语料库，将政治家的社交媒体言论与德国政治语料库GerParCor相连接，从而实现在线交流与议会辩论之间的比较分析。MultiParTweet包含39,546条推文，其中涵盖19,056个媒体项目。此外，我们通过九个基于文本的模型和一个视觉-语言模型（VLM）丰富了标注内容，为MultiParTweet添加了情感、情绪和主题标注。同时，自动标注结果通过人工标注子集进行了评估。MultiParTweet可通过我们的工具TTLABTweetCrawler重建，该工具提供了从X平台收集数据的框架。为展示方法学示例，我们检验了模型之间是否能够利用其他模型的输出相互预测。总之，我们提供了MultiParTweet——一个整合了经人工标注验证的自动文本与媒体标注的资源，以及TTLABTweetCrawler——一个通用的X平台数据收集工具。我们的分析表明，这些模型具有相互可预测性。此外，基于VLM的标注更受人工标注者青睐，这表明多模态表征更符合人类解读。