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The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.
We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the project is to better understand how social media can be abused, for example by astroturfing.
Our work to date includes a number of core research themes:
- We study how individuals’ limited attention span affects what information we propagate and what social connections we make, and how the structure of social networks can help predict which memes are likely to become viral.
- We explore social science questions via social media data analytics. Examples of research to date include analyses of geographic and temporal patterns in movements like Occupy Wall Street, societal unrest in Turkey, polarization and cross-ideological communication in online political discourse, partisan asymmetries in online political engagement, the use of social media data to predict election outcomes and forecast key market indicators, and the geographic diffusion of trending topics.
- We produce images, videos, and demos to demonstrate applications of our data mining research, from visualizing meme diffusion patterns to detecting social bots on Twitter.
The current focus of the project follows three directions:
- Modeling efforts to better understand how information spreads, why some memes go viral, competition for attention, the role of sentiment on the diffusion process, the mutual interaction between traffic on the network and the emergent structure of the network.
- Analyzing differences in meme diffusion patterns between different domains, such as news and scientific results, and the correlations between certain online behaviors and offline events.
- Expanding the platform to make the data derived from our analyses of meme diffusion and from our machine learning algorithms more easily accessible and thus more useful to social scientists, reporters, and the general public.
We gratefully acknowledge support from National Science Foundation award CCF-1101743 (ICES proposal on Meme Diffusion Through Mass Social Media) and James S. McDonnell Foundation complex systems grant on Contagion of Ideas in Online Social Networks, as well as a seed Data to Insight grant from the Lilly Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.
We acknowledge the collaboration of many researchers. Alessandro Vespignani and Johan Bollen were Co-PIs on the NSF grant. Several other key collaborators at IU and other institutions contributed to various research thrusts of this project: Emilio Ferrara, Bruno Gonçalves, Przemyslaw Grabowicz, Luca Aiello, Judy Qiu, Xiaoming Gao, Andrew Younge, Tak-Lon Wu, and YY Ahn. Other collaborators include Nicola Perra, Marton Karsai, Fabio Rojas, Joseph DiGrazia, Chato Castillo, Francesco Bonchi, Rossano Schifanella, Snehal Patil, Emily Metzgar, Luis Rocha, Geoffrey Fox, and Chris Ogan.
The project contributed to the support and training of postdoctoral fellows Diego Fregolente, Ruby Wang, and Giovanni Luca Ciampaglia. And of many graduate students who were involved in various aspects of the research: Clayton A Davis, Karissa McKelvey, Mark Meiss, Jacob Ratkiewicz, Michael Conover, Lilian Weng, Qian Zhang, Huina Mao, Onur Varol, Azadeh Nematzadeh, Pablo Moriano, Alex Rudnick, Jiayi Zhu, Rachael Filper, Jasleen Kaur, Prashant Shiralkar, and Zeyao Yang, as well as undergraduate students Bryce Lewis and Kehontas Rowe.