A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence

TL;DR

This paper reviews various features used to characterize Twitter users, unifies their definitions, and demonstrates their application in predicting offline influence, revealing that common online influence indicators are less relevant for real-world influence detection.

Contribution

It provides a comprehensive, unified typology of Twitter user features and applies them to offline influence prediction, outperforming existing methods.

Findings

Most online influence features are not relevant for offline influence detection.

Content-based features can effectively classify users as Influencers or not.

Proposed methods outperform state-of-the-art on CLEF RepLab 2014 dataset.

Abstract

Many works related to Twitter aim at characterizing its users in some way: role on the service (spammers, bots, organizations, etc.), nature of the user (socio-professional category, age, etc.), topics of interest , and others. However, for a given user classification problem, it is very difficult to select a set of appropriate features, because the many features described in the literature are very heterogeneous, with name overlaps and collisions, and numerous very close variants. In this article, we review a wide range of such features. In order to present a clear state-of-the-art description, we unify their names, definitions and relationships, and we propose a new, neutral, typology. We then illustrate the interest of our review by applying a selection of these features to the offline influence detection problem. This task consists in identifying users which are influential in real-life, based on their Twitter account and related data. We show that most features deemed efficient to predict online influence, such as the numbers of retweets and followers, are not relevant to this problem. However, We propose several content-based approaches to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.