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.
- Truthy is an ensemble of web services and tools 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.
O Varol, E Ferrara, C Ogan, F Menczer and A Flammini
Evolution of online user behavior during a social upheaval. Proc. ACM Web Science Conference, 2014 (BEST PAPER AWARD)
Moriano, P; Ferrara, E; Flammini, A; Menczer, F
Dissemination of scholarly literature in social media. Altmetrics’14 Workshop, 2014
R Fulper, GL Ciampaglia, E Ferrara, F Menczer, YY Ahn, A Flammini, B Lewis and K Rowe
Misogynistic Language on Twitter and Sexual Violence. CHASM Workshop, 2014
X. Gao, E. Roth, K. McKelvey, C. Davis, A. Younge, E. Ferrara, F. Menczer, J. Qiu
Supporting a Social Media Observatory with Customizable Index Structures — Architecture and Performance. In Cloud Computing for Data Intensive Applications, Springer, 2014
S. Liu, N. Perra, M. Karsai, A. Vespignani
Controlling contagion processes in activity-driven networks. Physical Review Letter, 112:118702, 2014
M Karsai, N Perra, and A Vespignani
Time varying networks and the weakness of strong ties. Nature Sci. Rep., 4:4001, 2014
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
Lilian Weng, Jacob Ratkiewicz, Nicola Perra, Bruno Goncalves, Carlos Castillo, Francesco Bonchi, Rossano Schifanella, Filippo Menczer, and Alessandro Flammini.
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
- 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.