Abstract
The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the capacity of communicating information is fundamental for advertising, promoting alerts of civil protection, etc.
Highlights
In recent years, social media have become an important communication tool and instrument for monitoring preferences of users, as well as making predictions in a number of contexts
After the assessment of the above-mentioned approaches, we have considered a Classification And Regression Tree (CART) model with Recursive Partitioning procedure (RPART model) as the best learning algorithm
The proposed analysis identified additional relevant metrics with respect to those proposed in the literature, namely, Publication Time and Listed Count
Summary
Social media have become an important communication tool and instrument for monitoring preferences of users, as well as making predictions in a number of contexts. It is possible to send some direct private messages to other users without provoking diffusion Another solution to enhance the diffusion and the echo of tweets is to include in a tweet including a direct mention of a user; this can be done by using the B@^ prefix such as B@usernickname^. In the world of Twitter, the effectiveness of a tweet is frequently measured in terms of retweet count, which is the number of times the tweet has been retweeted [46]. It gives a measure of the number of reached audience and/or appreciation
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