A Mobile Tool for Collecting and Labeling Data on Twitter's Network Sociability Practices

Abstract

This work presents a mobile tool for collecting and labeling data about Twitter's sociability practices. Such data are related to the execution of functionalities such as Tweet, Comment, Retweet and Like. During the capture, the data are labeled with a sequence of actions, which is learned via the GSP algorithm, and the polarity of sentiment contained in the tweet. At the end, the data are processed to compute the volume of Bourdieusian Social Capital (BSC) of the profile and are also available for the user of the tool to export them. In addition, the composition of the BSC from the polarity of sentiment is computed.