An Exploratory Analysis of the Evolution of Information Diffusion Pattern in Twitter Networks
Advisor Information
Lotfollah Najjar
Location
UNO Criss Library, Room 107
Presentation Type
Oral Presentation
Start Date
7-3-2014 9:15 AM
End Date
7-3-2014 9:30 AM
Abstract
In this study we use the concept of power law distribution to explore and analyze the network evolution of two real-life social networks formed around two contexts. Power law in simple words is a property of a network which states that only a tiny fraction of individual users are responsible for a majority of content produced in the network. Our social movement network is a Twitter network of users contributing in online social movement in Egypt 2011 uprising. The other dataset is an extreme event network, which is the Twitter network formed around Boston Bombing 2013 incident. The essence of these two networks are different as the former is a long-term event in social/political context, while the latter is an unpredictable short term-event with the purpose of responding to an emergency situation. We explore the network structure and the pattern of information diffusion during time in the two networks. The number of total retweets a user gets from other users is the degree of the node representing the user in the network. For both networks we analyzed the retweet pattern by extracting the Tweet message content and identifying whether it’s a retweet post or an original message. Our results show that although both networks follow a power law degree distribution, the distribution is more heavy-tailed with factor of 10 in the Egypt social movement network; and the extreme event network is more distributed. The results will follow a discussion and theory-supported explanations of the results.
An Exploratory Analysis of the Evolution of Information Diffusion Pattern in Twitter Networks
UNO Criss Library, Room 107
In this study we use the concept of power law distribution to explore and analyze the network evolution of two real-life social networks formed around two contexts. Power law in simple words is a property of a network which states that only a tiny fraction of individual users are responsible for a majority of content produced in the network. Our social movement network is a Twitter network of users contributing in online social movement in Egypt 2011 uprising. The other dataset is an extreme event network, which is the Twitter network formed around Boston Bombing 2013 incident. The essence of these two networks are different as the former is a long-term event in social/political context, while the latter is an unpredictable short term-event with the purpose of responding to an emergency situation. We explore the network structure and the pattern of information diffusion during time in the two networks. The number of total retweets a user gets from other users is the degree of the node representing the user in the network. For both networks we analyzed the retweet pattern by extracting the Tweet message content and identifying whether it’s a retweet post or an original message. Our results show that although both networks follow a power law degree distribution, the distribution is more heavy-tailed with factor of 10 in the Egypt social movement network; and the extreme event network is more distributed. The results will follow a discussion and theory-supported explanations of the results.