Due to the lack of a language-tag in the YouTube-API, which would be extremely helpful determining the language of a videoclip (precisely: the language of the title and description; language detection in audiovisual content requires much more effort and cpu-power), i ran a quick test with python´s guess-language module I think it´s a simple but pretty reliable algorithm for language detection tasks, based on a trigram detection and the enchant-library for over 60 languages.
In our project about political communication on YouTube we stumbled upon a large amount of non-german videoclips (sampling from the feed for the most-viewed videos in the “news & politics” - category in germany). To get an overview of the linguistic diversity on the german version on YouTube, I conducted a language-detection test on a small subsample of our data (350 videoclips).
First of all, just about 50% of the recent videos (“top viewed in news..”) have a german description (30 of them have a title in another language; a detection solely based on the (usually short) titles seems to be unreliable), followed by clips with an english-speaking description. The amount of an unsuccessful classification task (“UNKNOWN”) is noteworthy, but may be reduced by a combination with the title-classification. In line with the results of our manual-coding approach, a significant amount of descriptions and titles are in an arabic language. Furthermore, the results of the automatic language-detection could be combined with videoclip-metadata. For example, videoclips with a german description are obviously less successful than those with an arabic or english description. At the moment, i´m validating the classification results with a manual coding of the titles and descriptions, but this approach might be a starting point to reduce/filter the sample-size of audiovisual material, esp. in preparation of a manual content analysis or simply to get an overview of a huge amount of user-generated-content).