Crawling microblogging services to gather language-classified URLs. Workflow and case study
Résumé
We present a way to extract links from messages published on microblogging platforms and we classify them according to the language and possible relevance of their target in order to build a text corpus. Three platforms are taken into consideration: FriendFeed, identi.ca and Reddit, as they account for a relative diversity of user profiles and more importantly user languages. In order to explore them, we introduce a traversal algorithm based on user pages. As we target lesser-known languages, we try to focus on non-English posts by filtering out English text. Using mature open-source software from the NLP research field, a spell checker (aspell) and a language identification system (langid.py), our case study and our benchmarks give an insight into the linguistic structure of the considered services.
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