Prediction and Characterization of High-Activity Events in Social Media Triggered by Real-World News

TL;DR

This paper analyzes how real-world news events trigger bursty collective activity on Twitter, proposes a classification methodology for event intensity, and demonstrates early prediction of highly impactful events based on initial activity patterns.

Contribution

It introduces a novel methodology to classify and predict high-impact social media events early in their evolution, based on activity patterns observed in Twitter data.

Findings

High-activity events exhibit distinguishable early-stage characteristics.

Early activity data can predict the most impactful events with high accuracy.

Bursty behavior in social networks aligns with natural collective phenomena.

Abstract

On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event's reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event's lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience.