Extending a Parliamentary Corpus with MPs' Tweets: Automatic Annotation and Evaluation Using MultiParTweet

2025-12-12

AI Summary

Existing political discourse analysis lacks structured cross-modal alignment between parliamentary debates and social media content, hindering comparative studies of institutional versus public political communication. Method: We introduce MultiParTweet—the first framework systematically aligning multilingual parliamentary Twitter data with the German parliamentary corpus GerParCor. We develop TTLABTweetCrawler, a reproducible cross-platform crawler, collecting 39,546 tweets and 19,056 associated media items. We integrate nine multilingual text models and one vision-language model (VLM) to automate multimodal annotation of sentiment, emotion, and topics, validated on human-annotated subsets. Contribution/Results: (1) First structured mapping between parliamentary proceedings and social media discourse; (2) A VLM-driven multimodal annotation paradigm—human preference tests show statistically significant superiority over text-only models; (3) Empirical validation of cross-modal predictability among model outputs, enabling robust consistency analysis across modalities.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

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.

Problem

• Extends parliamentary corpus with politicians' tweets for comparative analysis.
• Automatically annotates tweets with emotion, sentiment, and topic using models.
• Evaluates automated annotations against manual ones to validate accuracy.

Innovation

• Multilingual tweet corpus linking social media and parliamentary debates
• Automated annotation using nine text models and one vision-language model
• General-purpose data collection tool for reconstructing and analyzing tweets