Popularity on YouTube is an important metric for influencers and brands. It is linked to video relevance, content, and features that attract audience attention and interest. We present and test a model of YouTube video popularity drivers that trigger several engagement actions (i.e., number of views, likes, dislikes, and comments). These drivers include characteristics—such as language elements, linguistic style, subjectivity, emotion valence, and video category—that influence online video popularity on YouTube. An analysis of a database comprising more than 11,000 videos from 150 digital influencers shows that several factors help to boost the number of views, likes/dislikes, and comments. We find that medium‐length and long videos posted during non‐business hours and weekdays and those using a subjective language style, less‐active events, and temporal indications are more likely to receive views, likes, and comments. Moreover, the use of negative or low‐arousal emotion helps to promote a general interest in a YouTube video.

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Abstract:In this paper we present the annotation of a corpus with named entities that are classified into semantic types and disambiguated by linking them to their corresponding entry in the Portuguese DBpedia. This corpus, QTLeap Corpus, is a multilingual collection of question and answer pairs from a chat-based helpdesk service for Information and Communication Technologies. The resulting annotated corpus is a gold-standard named entity annotated lexical resource that is useful in supporting the training and evaluation of named entity annotation and disambiguation tools for Portuguese.

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