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Assessing the reTweet proneness of tweets: predictive models for retweeting

机译：评估推文的转发推文倾向：转发推文的预测模型

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摘要

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.
机译：评估社交网络帖子病毒性现象背后的机制的问题对于许多活动具有重要价值，例如广告和病毒营销，影响和促进，早期监测和应急响应。在几个社交网络中，Twitter.com是实时传播信息最有效的网站之一，帖子（即tweet）的传播效果与tweet被转发的次数有关。文献中提出了不同的模型来理解推文的转发倾向（要转发的推文的倾向或倾向）。在本文中，提出了进一步的步骤，因此已经对从Twitter数据中提取的几个功能进行了分析，以创建预测模型，目的是预测推文的转发程度（即，给定推文可能转发的转发数量）。主要目标是获得有关一条推文可能从社交网络获得的转发数的指示。在本文中，提出了将分类树与递归分区程序一起用于预测的方法，并将获得的结果在准确性和处理时间方面与其他方法进行了比较。过去18个月中，使用DISIT Lab的Twitter Vigilance研究和研究平台收集了用于拟议研究的Twitter数据。这项工作是在欧洲委员会RESOLUTE H2020的智慧城市项目的背景下开发的，其中，信息交流的能力是广告，促进民防警报等的基础。

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