What Ignites a Reply?: Characterizing Conversations in Microblogs

Nowadays, microblog platforms provide a medium to share content and interact with other users. With the large-scale data generated on these platforms, the origin and reasons of users engagement in conversations has attracted the attention of the research community. In this paper, we analyze the factors that might spark conversations in Twitter, for the English and Spanish languages. Using a corpus of 2.7 million tweets, we reconstruct existing conversations, then extract several contextual and content features. Based on the features extracted, we train and evaluate several predictive models to identify tweets that will spark a conversation. Our findings show that conversations are more likely to be initiated by users with high activity level and popularity. For less popular users, the type of content generated is a more important factor. Experimental results shows that the best predictive model is able obtain an average score $F1=0.80$. We made available the dataset scripts and code used in this paper to the research community via Github.