Information Diffusion and External Influence in Networks
This Paper is available online [1].
Summary
This paper is unique in that it investigates external influence as a factor of information spread in social networks, rather than just diffusion as most previous work had done. The authors propose a model which allows information to spread from one node to another in the graph according to a probabilistic model. Additionally, it allows for information to spread from outside of the graph network also from a probability distribution. Furthermore, even though a node has neighbors that may allow information to diffuse, it may still have a generative story that the information came from an external source. They evaluate their results on a dataset of Twitter - using ever tweet from one month. Internal diffusion within the network can occur when one person tweets the same link as someone that he/she is following. External influence is when something enters the social graph, even if it is the same piece of information elsewhere in the graph if the tweeter does not directly follow them, or possibly if they happened to find the same information as someone they follow, but not through their tweets.
Datasets
The authors use two different datasets including synthetic data (to establish a baseline), and links from tweets. The interesting thing that this paper has over many other papers dealing with twitter is that they have every tweet from January 2011, totaling over 3 billion.
Methodology
Experimental Results
The experiments on twitter suggest that only about 71% of URL mentions on twitter come from network effects, the rest is explained by their external influences. They argue that this 29% is statistically significant and cannot be ignored. In other words, a proper treatment of a social network should also allow for external influences in addition to evaluations of just the network.
