The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale behavioral trace data from online social networking platforms we are able to analyze and model the spread of information, from political discourse to market trends, in unprecedented detail.
Our work to date includes a number of core research themes. Truthy is a web-based system to analyze and visualize the diffusion of information on Twitter. The Truthy system evaluates thousands of tweets an hour to identify new and emerging bursts of activity around memes of various flavors. Building on this foundation we have undertaken several analyses of political communication on Twitter, addressing political polarization and cross-ideological communication, the automated prediction of political affiliation from network and text data, and partisan asymmetries in online political engagement. Members of the Truthy team have successfully applied a custom psycholinguistic sentiment analysis framework to the problem of forecasting key market indicators, technology which now underpins the trading decisions of a $40 million investment fund.
The current focus of the project follows three directions:
- Expanding the platform to make the data more easily accessible and thus more useful to social scientists, reporters, and the general public.
- Modeling efforts to better understand how information spreads, why some memes go viral, the role of sentiment on the diffusion process, the mutual interaction between traffic on the network and the emergent structure of the network.
- Adopting network analysis methods in a machine learning framework to automatically detect astroturf in political campaigns.
L. Weng, F. Menczer, and Y.-Y. Ahn
Virality Prediction and Community Structure in Social Networks. Nature Sci. Rep., (3) 2522, 2013.
E. Ferrara, O. Varol, F. Menczer, and A. Flammini
Traveling Trends: Social Butterflies or Frequent Fliers? In Proc. 1st ACM Conf. on Online Social Networks (COSN), pages 213–222, 2013
L. Weng and F. Menczer
Computational analysis of collective behaviors via agent-based modeling. In P. Michelucci, editor, Handbook of Human Computation, pages 761–767. Springer, 2013
J DiGrazia, K McKelvey, J Bollen, and F Rojas
More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior. PLOS ONE 8(11): e79449, 2013.
M Karsai, N Perra, and A Vespignani
Time varying networks and the weakness of strong ties. Scientific reports 4: 4001, 2014.
The role of information diffusion in the evolution of social networks. Proc. 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD, 2013.
Clustering Memes in Social Media. Proc. IEEE/ACM Intl. Conf. on Advances in Social Networks Analysis and Mining ASONAM, 2013.
Q Zhang, N Perra, B Gonçalves, F Ciulla, and A Vespignani.
Characterizing scientific production and consumption in Physics. Nature Sci. Rep., (3) 1640, 2013.
D Mocanu, A Baronchelli, N Perra, B Gonçalves, Q Zhang, et al.
The Twitter of Babel: Mapping World Languages through Microblogging Platforms. PLoS ONE, (8)4: e61981, 2013.
Interoperability of Social Media Observatories. Web Science 2013, Web Observatory Workshop, May 5 2013.
Design and Prototyping of a Social Media Observatory. WWW 2013, Web Observatory Workshop, May 17 2013.
Conover MD, Davis C, Ferrara E, McKelvey K, Menczer F, Flammini A.
The Geospatial Characteristics of a Social Movement Communication Network. PLoS ONE 8(3): e55957, 2013.
Truthy: Enabling the Study of Online Social Networks. CSCW 2013 Demonstration, 25 February 2013.
Competition among memes in a world with limited attention. Nature Sci. Rep., (2) 335, 2012.
Modeling Dynamical Processes in Complex Socio-technical Systems. Nature Physics, 8, 32-39, 2012.
Visualizing Communication on Social Media: Making Big Data Accessible. Proc. CSCW Workshop on Collective Intelligence as Community Discourse and Action, 2012.
Detecting and Tracking Political Abuse in Social Media. Proc. 5th International AAAI Conference on Weblogs and Social Media ICWSM, 2011.
Predicting the Political Alignment of Twitter Users. Proceedings of 3rd IEEE Conference on Social Computing SocialCom, 2011.
Networks of Political Communication I: Multi-Mode Interactions in an Online Social Network. International School and Conference on Network Science NetSci, 2011.
Truthy: Mapping the Spread of Astroturf in Microblog Streams. Proc. 20th Intl. World Wide Web Conf. Companion WWW, 2011.
Networks of Political Communication II: Partisan Engagement and Social Media. International School and Conference on Network Science NetSci, 2011.
Abuse of social media and political manipulation. In Markus Jakobsson (Eds.), The Death of The Internet, Wiley, 2012.
An Information Propagation Model Based on User Interests. In H. Sayama, A. Minai, D. Braha, and Y. Bar-Yam (Eds.), Unifying Themes in Complex Systems Volume VIII: Proc. 8th International Conference on Complex Systems ICCS, 2011.
Data and Software
- Sci Rep 2013 Dataset: Prediction of Viral Memes on Twitter
- ICWSM 2011 Dataset: Truthy/Legitimate Classification
- ICWSM 2011 Dataset: Political Polarization on Twitter
- Klatsch: a framework and language for exploring and analyzing feeds of social media data
- Fast visualization of large dynamic networks (winner of WICI Data Challenge)
- Virality Prediction and Community Structure in Social Networks – ignite talk by Lilian Weng at Science of Success symposium, Harvard, June 2013
- The Role of Information Diffusion in the Evolution of Social Networks – presentation at KDD 2013
- On Truthy Tweeting – from the Conference on Truthiness in Digital Media (#Truthicon) at Harvard University, March 2012
- The Truthy Project Ferrets Out Online Deception - by WSJ
- Political Polarization on Twitter – presentation by Mike Conover at ICWSM 2011
- Political Communication on Twitter: Misinformation, Polarization and Partisan Engagement - D2I seminar
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.