Analyzing the impact of social media on social movements: a computational study on Twitter and the occupy wall street movement

• Authors: Li Tan;Suma Ponnam;Patrick Gillham;Bob Edwards;Erik Johnson
• Venue: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
• Year: 2013

The extensive use of digital social media by social movement actors is an emerging trend that restructures the communication dynamics of social protest, and it is widely credited with contributing to the successful mobilizations of recent movements (e.g., Arab Spring, Occupy Wall Street). Yet, our understanding of both the roles played by social movement's use of social media and the extent of its impact is largely derived from anecdotal evidence, news reports, and a thin body of scholarly research on web-based technologies. In this research we explore several computational methods for measuring the impact of social media on a social movement. Inspired by methodologies originally developed for analyzing computer networks and other dynamic systems, these methods measure various static and dynamic aspects of social networks, and their relations to an underlying social movement. We demonstrated the feasibility and benefits of these measurement methods in the context of Twitter and the Occupying Wall Street movement (OWS). By analyzing tweets related to OWS, we demonstrated the link between the vitality of the movement and the volume of the related tweets over time. We show that there is a positive correlation between the dynamic of tweets and the short-term trend of OWS. The correlation makes it possible to forecast the short-term trend of a social movement using social media data. By ranking users based on the number of their OWS-related tweets and the durations of their tweeting, we are able to identify "buzz makers". Using a strategy similar to the page-rank algorithm, we define the influence of a user by the number of re-tweets that his/her original tweets incite. By tracing where OWS-related tweets are generated, we measure the geographic diffusion of OWS. By analyzing the percentage of OWS tweets generated from different sources, we show that smart phones and applications such as tweet deck had been used extensively for tweeting in the OWS movement. This indicates the involvement of a younger and more technology-inclined generation in OWS.