Information Diffusion on Twitter

Abstract

Characterizing, predicting, and quantifying the impact of postings, tweets, messages, etc. on social media platforms is a topic of growing interest due to the increasing reliance on using social media as a means for various purposes by individuals and organizations alike. In this paper, we describe an information diffusion model on the social network of Twitter. The model treats information diffusion on social media as a multivariate time series problem and deals mainly with three different dimensions of Twitter data and the different patterns of information diffusion. These dimensions are the volume of tweets, the sentiment of tweets and influence of tweets. To discover different patterns of information diffusion on Twitter, time series clustering is used where Dynamic Time Warping distance is adopted as the distance measure. To predict different parameters of each of the three dimensions, the linear time series model of Autoregressive Integrated Moving Average (ARIMA) and the non-linear time series model of Long Short-Term Memory (LSTM) Recurrent Neural Networks are used and their performance is compared. Results indicate that LSTM models achieve far better performance and hold great potential to be utilized for real-world applications.