Abstract
Burst analysis and prediction is a fundamental problem in social network analysis, since user activities have been shown to have an intrinsically bursty nature. Bursts may also be a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross platform social media data may be valuable for predicting bursts within a single social media platform. A Long-Short-Term-Memory (LSTM) model is proposed in order to capture the temporal dependencies and associations based upon activity information. The data used to test the model was collected from Twitter, Github, and Reddit. Our results show that the LSTM based model is able to leverage the complex cross-platform dynamics to predict bursts. In situations where information gathering from platforms of concern is not possible the learned model can provide a prediction for whether bursts on another platform can be expected.
Highlights
Social media platforms are among the most widely used communication channels and have become an indispensable part of our everyday life, due to the speed and reduction in cost that these services provide to its users [1]
The seminal work presented in [9] sets a foundation for how “bursts” of activity are a key component in human dynamics and are an ubiquitous phenomenon in data acquired from social systems
Burst analysis and prediction is a fundamental problem in social network analysis since the patterns are typically non-linear and unpredictable
Summary
Social media platforms are among the most widely used communication channels and have become an indispensable part of our everyday life, due to the speed and reduction in cost that these services provide to its users [1]. Information spread through social media can be good for rapidly raising awareness of important issues such as environmental protection [4] and can be misused for malicious content spreading activities [5] Over time, this can render society vulnerable to rumors through misinformation campaigns that can polarize communities [6]. The seminal work presented in [9] sets a foundation for how “bursts” of activity are a key component in human dynamics and are an ubiquitous phenomenon in data acquired from social systems It is intriguing how “intense activity followed by longer periods of inactivity” can manifest in social coding platforms [10] from complex timelines of work interspersed with communication about version control. The work presented here proposes a new model in which activity traces across multiple platforms can be predicted for the platform aggregate and for specific community level associated users (hashtags, repos, and posts)
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