Congratulations to Przemyslaw Grabowicz, Luca Aiello, and Fil Menczer for winning the WICI Data Challenge. A prize of $10,000 CAD accompanies this award from the Waterloo Institute for Complexity and Innovation at the University of Waterloo. The Challenge called for tools and methods that improve the exploration, analysis, and visualization of complex-systems data. The winning entry, titled Fast visualization of relevant portions of large dynamic networks, is an algorithm that selects subsets of nodes and edges that best represent an evolving graph and visualizes it either by creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is deployed in the movie generation tool of the Truthy system, which allows users to create, in near-real time, YouTube videos that illustrate the spread and co-occurrence of memes on Twitter. Przemek and Luca worked on this project while visiting CNetS in 2011 and collaborating with the Truthy team. Bravo!
Read our latest paper titled Social Dynamics of Science in Nature Scientific Reports. Authors Xiaoling Sun, Jasleen Kaur, Staša Milojević, Alessandro Flammini & Filippo Menczer ask, How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.
Research by our Truthy team was recently featured in New Scientist, USA Today, and the cover story of Science News. The Truthy project, developed by CNetS researchers and doctoral students, aims to study the factors affecting the spread of information — and misinformation — in social media.
The Truthy site charts tweet sentiment and volume related to themes such as social movements and news. It also monitors Twitter activity to build interactive networks that let visitors visualize the diffusion networks of memes, identify the most influential information spreaders, and explore those influential feeds and other information about their online activity, such as sentiment and language. Other tools let you map the geo-temporal diffusion of memes, generate YouTube movies that display how hashtags emerge and connect, and download data directly from Twitter. With these analytics, one can begin to ask question such as: How does sentiment change in response to events and memes? What memes survive over time? Who are the most influential users on a particular topic?
For more press coverage go to the Truthy press page.
In our paper on Competition among memes in a world with limited attention in Nature Scientific Reports, Lilian Weng and coauthors Sandro Flammini, Alex Vespignani, and Fil Menczer report that we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas. The findings have been mentioned in the popular press, including Information Week, The Atlantic, and the Dutch daily NRC.
Prof. Flammini (PI) and Menczer have been awarded a three-year, $2M grant from DARPA in the context of the Social Media in Strategic Communication (SMISC) program, whose primary goal is “to develop a new science of social networks built on an emerging technology base,” Our IU unit leads a three-group team that includes collaborators at Lockheed-Martin Advanced Technology Lab and the University of Michigan. The funded project is aimed at designing and implementing a system to detect online persuasion campaigns.
We’re pleased to report several exciting developments in our interdisciplinary project studying information diffusion in complex online social networks. The past year has resulted in several publications. Our results on the Truthy astroturf monitoring and detection system were presented at WWW 2011 and ICWSM 2011. Research into the polarized network structure of political communication on Twitter was presented at ICWSM and received the 2011 CITASA Best Student Paper Honorable Mention. We demonstrated the feasibility of the prediction of individuals’ political affiliation from network and text data (SocialCom 2011), a machine learning application that enables large-scale instrumentation of nearly 20,000 individuals’ political behaviors, policy foci, and geospatial distribution (Journal of Information Technology and Politics). We’re also working on a paper on partisan asymmetries in online political activity surrounding the 2010 U.S. congressional midterm elections.
Our results have been widely covered in the press, including the Wall Street Journal, Science, Communications of the ACM, NPR [1,2], The Chronicle of Higher Education, Discover Magazine, The Atlantic, New Scientist, MIT Technology Review, and many more.
Current and future research is supported by an award from the NSF Interface between Computer Science and Economics & Social Sciences program, and a McDonnell Foundation grant. The former will focus on building an infrastructure for the study of information diffusion in social media, the characterization of meme spread patterns, and the development of sentiment analysis tools for social media. The latter will focus on modeling efforts, especially agent-based models of information diffusion, competition for attention, and the relationship between information sharing events and social network evolution.
Astroturfers, Twitter-bombers and smear campaigners need beware this election season as a group of leading Indiana University information and computer scientists today unleashed Truthy.indiana.edu, a sophisticated new Twitter-based research tool that combines data mining, social network analysis and crowdsourcing to uncover deceptive tactics and misinformation leading up to the Nov. 2 elections. Combing through thousands of tweets per hour in search of political keywords, the team based out of IU’s School of Informatics and Computing will isolate patterns of interest and then insert those memes (ideas or patterns passed by imitation) into Twitter’s application programming interface (API) to obtain more information about the meme’s history.
In the run-up to the mid-term elections, Truthy uncovered a number of abuses such as robot-driven traffic to politician websites and networks of bot accounts controlled by individuals to promote fake news. These findings have been widely covered in the press, with mentions in The Atlantic, MIT Technology Review, PC World, New Scientist, NPR, Ars Technica, Fast Company, The Chronicle of Higher Education, The New York Times Magazine, and many other media. Read more here and here.
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.
J DiGrazia, K McKelvey, J Bollen, and F Rojas
More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior. Under review, 2013.
M Karsai, N Perra, and A Vespignani
The emergence and role of strong ties in time-varying communication networks. Tech. Rep. arXiv:1303.5966 [physics.soc-ph], 2013.
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)
- 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 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.
Fil Menczer is one of the organizers of Hypertext 2009, the 20th ACM Conference on Hypertext an Hypermedia. The conference will be held June 29-July 1 at the Villa Gualino Convention Centre, on the hills overlooking Torino, Italy. Hypertext is the main venue for high quality peer-reviewed research on “linking.” The Web, the Semantic Web, the Web 2.0, and Social Networks are all manifestations of the success of the link. With a 70% increase in submissions, Hypertext 2009 will have a strong and diverse technical program covering all research concerning links: their semantics, their presentation, the applications, as well as the knowledge that can be derived from their analysis and their effects on society. The conference will also feature demos, posters, a student research competition, four workshops, and keynotes by Lada Adamic and Ricardo Baeza-Yates.