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Blogs are a source of grey literature which are widely adopted by softwarepractitioners for disseminating opinion and experience. Analysing such articlescan provide useful insights into the state-of-practice for software engineeringresearch. However, there are challenges in identifying higher quality contentfrom the large quantity of articles available. Credibility assessment can helpin identifying quality content, though there is a lack of existing corpora.Credibility is typically measured through a series of conceptual criteria, with'argumentation' and 'evidence' being two important criteria.  We create a corpus labelled for argumentation and evidence that can aid thecredibility community. The corpus consists of articles from the blog of asingle software practitioner and is publicly available.  Three annotators label the corpus with a series of conceptual credibilitycriteria, reaching an agreement of 0.82 (Fleiss' Kappa). We present preliminaryanalysis of the corpus by using it to investigate the identification of claimsentences (one of our ten labels).  We train four systems (Bert, KNN, Decision Tree and SVM) using three featuresets (Bag of Words, Topic Modelling and InferSent), achieving an F1 score of0.64 using InferSent and a Linear SVM.  Our preliminary results are promising, indicating that the corpus can helpfuture studies in detecting the credibility of grey literature. Future researchwill investigate the degree to which the sentence level annotations can inferthe credibility of the overall document.

Towards a corpus for credibility assessment in software practitioner blog articles