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We develop an NLP method for inferring potential contributors among multitude of users within crowdsourcing forums (CSFs). The method basically provides a way to predict expertise from their structures (syntax–semantic patterns) when crowdsourced votes are unavailable. It primarily deals with tackling core adverse conditions, which hinder the identification of crowds’ expertise levels, and standardization of measuring linguistic quality of crowdsourced text. To solve the former, an expertise estimation and linguistic feature annotation algorithm is developed. To approach the later, a comprehensive linguistic characterization of crowdsourced text, along with extensive joint syntax–punctuation analyses, have been carried out. The entire corpora are comprised of approximately 8 different domains, 3 million and 50,000 sentences, and 32 million and 90,000 words, contributed by a crowd of 50,000 users. The analyses revealed six major linguistic patterns, identified on the basis of ordered lists of structural (syntactic) categories, learned from grammatical constructions, practiced by major groups of experts. In addition, nine different text-oriented expertise dimensions are identified, as crucial steps towards establishing standard linguistic-based expertise-framework for most CSFs. Potentially, the resulting framework simplifies the measurement of crowds’ proficiency, in those particular forums, where crowds’ tasks (e.g., answering questions, technically discerning deep features within images of galaxies for classifying them into certain categories) are intimately connected with their writing (e.g., describing answers illustratively, expressing complex phenomena observed in classified images). Moreover, wide varieties of linguistic annotations: latent topic annotations, named entities, syntactic and punctuation annotations, semantic and character set annotations, word and character n-grams ( n  = 2 and 3) annotations, are extracted. That is for building baseline and enhanced versions of expertise models (about 20 different models built). The successive achievements of enhancing baseline models, with iteratively adding linguistic feature annotations in a two-stage enhancement process, indicate the adaptability of the learned models.

Expertise Detection in Crowdsourcing Forums Using the Composition of Latent Topics and Joint Syntactic–Semantic Cues