Information Cascades – Empirically analyzing the effect of Twitter and Digg
Social media networking sites have very clearly demonstrated the effect of information cascades. Many users of common sites like Instagram, Facebook, and Twitter are updated on major news stories by other users they’re connected with sharing articles and information. The validity of the information is not always apparent, and many users are influenced by what they are presented with online. As such, the dissemination of information through these sites often leads to information cascades with incredible effects. Researchers at the University of Southern California attempted to analyze this effect in a recent study to properly measure social media’s impacts. They focused on two social networking sites, Twitter and Digg, to quantify the effect. These two social media sites were especially important to their research as they allow users to quickly and succinctly share links to articles and other sites and have easily accessible data.
The researchers found interesting results regarding the network formation of the two sites. Digg has a more dense network than does Twitter. When a user posts a story on Digg, it travels quickly among connected users, until it appears on the homepage where a number of unconnected users can access the information. While it can spread far after appearing on the homepage, the spread slows down quickly though it still has the potential to receive a big reaction from Digg’s users. Twitter functions in a different network pattern. Twitter’s network is less dense and as such see a slower rate of dissemination initially. However, they continue to spread at a high rate for a longer period and generally reach a bigger audience of users than do Digg’s. This dissemination of information is interesting in analyzing the ability of information cascades to develop based on the network structure of the networking sites. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewFile/1509/1839