Article: Roles in Communication Networks

Sandra Gonzalez-Bailonblog

Abstract: Communication through social media mediates coordination and information diffusion across a range of social settings. However, online networks are large and complex, and their analysis requires new methods to summarize their structure and identify nodes holding relevant positions. We propose a method that generalizes the sociological theory of brokerage, originally devised on the basis of local transitivity and paths of length two, to make it applicable to larger, more complex structures. Our method makes use of the modular structure of networks to define brokerage at the local and global levels. We test the method with two different data sets. The findings show that our approach is better at capturing role differences than alternative approaches that only consider local or global network features.

Workshop: Renmin University (Beijing)

Sandra Gonzalez-Bailonannouncement, talk

International faculty and students met for one week at the School of Journalism and Communication of Renmin University to discuss modeling approaches to Big Data. The Annenberg team included professor Sandra González-Bailón and PhD students Jiaying Liu, Jingwen Zhang, Sijia Yang, and Bo Mai. Other invited international faculty included Javier Borge-Holthoefer, from the Qatar Computing Research Institute; Georgios Paltoglou, from Wolverhampton University; and Taha Yasseri, from the University of Oxford. The workshop consisted of faculty lectures and student presentations, and it included visits to the digital media companies Tencent and Baidu.

Course Development Grant

Sandra Gonzalez-Bailonannouncement


The Penn Social Science and Policy Forum just awarded us a grant to develop the course “The Theory of Networks: How Digital Technologies Shape Collective Behavior and Why it Matters”. Sandra González-Bailón and Victor Preciado will teach this course in Spring 2015.

Article: Bias in Online Networks

Sandra Gonzalez-Bailonpublication

SN_elsevier
Abstract: We consider the sampling bias introduced in the study of online networks when collecting data through publicly available APIs (application programming interfaces). We assess differences between three samples of Twitter activity; the empirical context is given by political protests taking place in May 2012. We track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the search and the streaming APIs, and to different filtering parameters. We find that smaller samples do not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions, partly because of the higher influence of snowballing in identifying relevant nodes. We discuss the implications of this bias for the study of diffusion dynamics and political communication through social media, and advocate the need for more uniform sampling procedures to study online communication.