Right-Wing Rock or Just Rock? A Computational Linguistic Analysis of Frei.Wild

Reggaeton is one of the most widely consumed music genres in the world, and its lyrics are commonly regarded as highly sexualized. This claim rests mostly on qualitative studies and on small-scale quantitative ones. This paper has two goals. First, we present a reproducible method that uses a large language model to quantify thematic content in song lyrics along several independent dimensions. The method is not restricted to sexual content. Second, we apply it to a corpus of 1,259 songs by 12 reggaeton artists released between 2002 and 2025. The analysis covers four topics: a dataset characterization, a per-artist comparison, an analysis of how the dimensions change over time, and a comparison between our sexual-explicitness score and Spotify's own explicit flag. We release the data collection code, the scoring prompt, and the corpus, so that other researchers can replicate the approach or apply it to their own lyrics datasets.

The spread of misinformation is widely perceived as a threat to democratic deliberation, yet how political elites' rhetorical commitments to truth shift alongside the rise of populist actors remains poorly understood. Analysing 4.5 million tweets and 59,170 parliamentary speeches by German political elites between 2015 and 2025, we measure evidence-based and intuition-based rhetoric using a validated distributed dictionary representation. Across both arenas, intuition-based language has become more prominent, and right-leaning actors consistently exhibit the lowest Evidence Minus Intuition (EMI) scores. The parliamentary entry of the extreme-right Alternative for Germany (AfD) in 2017 coincides with sharp downward shifts in EMI across the broader chamber, while a more gradual decline is observed on Twitter. These findings document an association between far-right visibility and a changing approach to truth in elite discourse in a multiparty European democracy.

Resources for toxicity detection in Punjabi remain limited, particularly for musical content that combines linguistic and multimodal information. We present the Punjabi Geet Toxicity Dataset (PGT-FG), a segment-level resource developed to support research on toxicity detection in Punjabi musical content. The dataset was constructed from publicly available Punjabi songs collected from YouTube. An initial collection of 620 songs resulted in 465 successfully downloaded songs and 3,402 preliminary segments from 450 songs. After quality control and annotation, the final dataset contains 2,396 unique segments from 392 songs. Four native Punjabi annotators produced 2,870 annotation records, including a shared subset of 158 segments annotated by all four. Each annotation includes binary toxicity, eight fine-grained toxicity categories, severity, sentiment, mode, and an uncertainty flag. Inter-annotator agreement was measured with Fleiss' kappa on the shared subset, and majority-based resolution produced the final segment-level labels. Of the 2,396 segments, 705 (29.42%) were labelled toxic and 1,691 (70.58%) non-toxic. Violence was the most frequent category, occurring in 539 toxic segments (76.45%), followed by gang references (227, 32.20%) and drug references (133, 18.87%). The overall toxicity label achieved substantial agreement (kappa = 0.6521). PGT-FG provides a fine-grained resource for Punjabi toxicity detection and multimodal content analysis. The resolved dataset, with one final record per segment, is publicly available on Zenodo (https://doi.org/10.5281/zenodo.23040156). Original lyrics, lyric excerpts, and copyrighted audio or video content are not redistributed.