Questions about the recent controversy? Read The Truth about Truthy.
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 Truthy 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.
- Principal Investigators
- Research Personnel
- Other Collaborators
We gratefully acknowledge support from the Lilly Foundation (Data to Insight Center Research Grant), the National Science Foundation (ICES award CCF-1101743 on Meme Diffusion Through Mass Social Media), and the James S. McDonnell Foundation (complex systems grant on contagion of ideas in online social networks). 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.