The DESPIC project aimed to design a system detect persuasion campaigns at their early stage of inception, in the context of online social media. This open problem is challenging due to the fact that, during the early phase of formation of these persuasion campaigns (such as orchestrated misinformation, rumors, and viral advertising commercials) the amount of observable content is sparse, the information diffusion network is still small, and no large cascades of memes (for example a given hashtag on Twitter) has happened yet. The early identification of such campaigns is instrumental to understand their epidemic spreading through the whole network, before they become indistinguishable from any grass-root, legitimate topic.
The milestones of this project included:
- Developing state-of-the-art clustering and classification techniques to early detect and aggregate topics, exploiting both content-based features (e.g., text or sentiment) and network-based patterns (e.g., the diffusion network), from social media data streams.
- Combining sophisticated network analysis with content and time series mining in a machine learning framework to automatically detect, in near-real-time, coordinated persuasion campaigns.
- Apply these techniques to the detection of social bots.
Update: Our DESPIC team placed third in the DARPA bot detection challenge. Read How DARPA Took On the Twitter Bot Menace with One Hand Behind Its Back in MIT Technology Review.
IU Principle Investigators
IU Research Team
EPJ Data Science, 6(13). 2017.
Onur Varol, Emilio Ferrara, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini. Online Human-Bot Interactions: Detection, Estimation, and Characterization. In Proc.2017 AAAI International Conference on Web and Social Media (ICWSM)
Emilio Ferrara, Onur Varol, Filippo Menczer, and Alessandro Flammini. Detection of Promoted Social Media Campaigns. In Proceedings of the 2016 AAAI International Conference on Web and Social Media (ICWSM)
Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. The rise of social bots. Communications of the ACM 59(7), 2016
V.S. Subrahmanian, Amos Azaria, Skylar Durst, Vadim Kagan, Aram Galstyan, Kristina Lerman, Linhong Zhu, Emilio Ferrara, Alessandro Flammini, Filippo Menczer, Rand Waltzman, Andrew Stevens, Alexander Dekhtyar, Shuyang Gao, Tad Hogg, Farshad Kooti, Yan Liu, Onur Varol, Prashant Shiralkar, Vinod Vydiswaran, Qiaozhu Mei, and Tim Huang. The DARPA twitter bot challenge. IEEE Computer 49(6), 2016
C. A. Davis, O. Varol, E. Ferrara, A. Flammini, F. Menczer: BotOrNot: A system to evaluate social bots. Proc. WWW Developers Day Workshop, 2016. Preprint arXiv:1602.00975
Varol, O., Ferrara, E., Ogan, C. L., Menczer, F., & Flammini, A. Evolution of online user behavior during a social upheaval. In Proceedings of the 2014 ACM conference on Web science (pp. 81-90). Best paper award
M. JafariAsbagh, E. Ferrara, O. Varol, F. Menczer, and A. Flammini. Clustering memes in social media streams. Social Network Analysis and Mining, 4(1):237, 2014.
E Ferrara, M JafariAsbagh, O Varol, V Qazvinian, F Menczer, and A Flammini. Clustering memes in Social media. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE/ACM. 2013
Weng, L., Ratkiewicz, J., Perra, N., Gonçalves, B., Castillo, C., Bonchi, F., Schifanella, R., Menczer, F., & Flammini, A. The Role of Information Diffusion in the Evolution of Social Networks. In: Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), ACM. 2013
Conover, M. D., 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
Conover, M. D., Ferrara, E., Menczer, F., & Flammini, A. The Digital Evolution of Occupy Wall Street. PloS one, 8(5), e64679. 2013
Ferrara, E., Varol, O., Menczer, F., & Flammini, A. Traveling Trends: Social Butterflies or Frequent Fliers? In: Proceedings of the ACM Conference on Online Social Networks (COSN 2013), 2013. ACM. 2013
This project was partially supported by DARPA (SMISC project, award W911NF-12-1-0037). 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 DARPA.