Events
Past Event
Wednesdays@NICO Seminar: Predicting Human Behavior in Techno-Social Systems: Fighting Abuse and Illicit Activities
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level Chambers Hall
Details
Wednesdays@NICO | 12:00-1:00 PM, February 10, 2016 | Chambers Hall, Lower Level
Predicting Human Behavior in Techno-Social Systems: Fighting Abuse and Illicit Activities
Emilio Ferrara, Computer Scientist, Information Sciences Institute, University of Southern California
Abstract
The increasing availability of data across different socio-technical systems, such as online social networks, social media, and mobile phone networks, presents novel challenges and intriguing research opportunities. As more online services permeate through our everyday life and as data from various domains are connected and integrated with each other, the boundary between the real and the online worlds becomes blurry. Such data convey both online and offline activities of people, as well as multiple time scales and resolutions. In this talk, I'll discuss my research efforts aimed at characterizing and predicting human behavior and activities in techno-social worlds: starting by discussing network structure and information spreading on large online social networks, I'll move toward characterizing entire online conversations, such as those around big real-world events, to capture the dynamics driving the emergence of collective attention and trending topics. I'll describe a machine learning framework leveraging these insights to detect promoted campaigns that mimic grassroots conversation. Aiming at learning the signature of abuse at the level of the single individuals, I'll illustrate the challenges posed by characterizing human activity as opposed to that of synthetic entities (social bots) that attempt emulate us, to persuade, smear, tamper or deceive. I'll draw a parallel with detecting illicit activities in the real world leveraging the traces left by criminals' interactions via mobile phones. I'll conclude envisioning the design of computational systems that will help us making effective, timely decisions (informed by social data), and create actionable policies to contribute create a better future society.
Bio
Dr. Emilio Ferrara is a Computer Scientist at the USC's Information Sciences Institute. Ferrara's research interests include designing machine-learning systems to model and predict individual behavior in techno-social systems, characterize information diffusion and information campaigns, and predict crime and abuse in such environments. He has held research positions in institutions in Italy, Austria, and UK (2009-2012). Before joining USC in 2015, he was a Research Assistant Professor at the School of Informatics and Computing of Indiana University (2012-2015). Ferrara earned a Ph.D. in Mathematics and Computer Science from University of Messina (Italy), and has published over 60 articles on machine learning, network science, and social media, appeared in top venues including PNAS, Communications of the ACM, Physical Review Letters, and several ACM and IEEE transactions and top conferences (WWW, CSCW, etc.). His research on social network abuse and crime prediction has been featured on the major news outlets (TIME, BBC, The New York Times, etc.) and tech magazines (MIT Technology Review, Vice, Mashable, New Scientist, etc). His research has been supported by DARPA, ONR, and IARPA. Ferrara is Guest Editor of two special issues on network science and computational social sciences, published respectively on EPJ Data Science and Future Internet. He's member of the PC for conferences including ACM WWW, ICWSM, and SocInfo. Ferrara is co-chair of workshops recurring at ECCS, WWW, SocInfo, and WebScience; he was Local & Sponsor Chair of ACM Web Science 2014 and Publicity co-chair of SocInfo 2014. In 2015, Ferrara was named IBM Watson Analytics VIP Influential in Big Data.
Time
Wednesday, February 10, 2016 at 12:00 PM - 1:00 PM
Location
Lower Level Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
WED@NICO SEMINAR: Lightning Talks with NU Scholars!
Northwestern Institute on Complex Systems (NICO)
12:00 PM
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Lower Level, Chambers Hall
Details
May 20th Speakers:
Yulin Yu, Postdoctoral Fellow, Kellogg School of Management
Feihong Xu, PhD Candidate, McCormick School of Engineering
Maalvika Bhat, PhD Student, School of Communication
Rochana Chaturvedi, Postdoctoral Fellow, Kellogg School of Management
NICO Lightning talks are open to Northwestern graduate students, postdoctoral fellows, and visiting scholars! If you are interested in signing up for a future session, please fill out this short survey.
Talk Titles and Abstracts:
Yulin Yu
Postdoctoral Fellow
Kellogg School of Management &
Northwestern Institute on Complex Systems
Human–AI Creative Pathways: How People and Machines Differ in Creative Strategy
Generative AI offers the promise of amplifying creativity by recombining knowledge at a scale far beyond human capacity, yet humans still hold key advantages in flexibility and contextual reasoning. To understand how each achieves novelty, we analyzed more than 5,000 responses to the Divergent Association Task from both humans and AI systems using network-based methods. We find that while individual humans use fewer and simpler conceptual categories than machines, the collective diversity of human ideas is substantially higher. Human creative pathways tend to follow a one-directional but highly unpredictable trajectory, whereas AI systems rely on repetitive, back-and-forth exploration patterns. Finally, both humans and machines show anchoring effects—early ideas shape later responses—but in opposite ways: humans anchor low, while machines anchor high.
Feihong Xu
PhD Candidate
Engineering Sciences & Applied Mathematics
McCormick School of Engineering
A Well-Calibrated Model Similarity Measure for Arbitrary Neural Networks
Deep learning approaches have transformed biological and biomedical image analysis, but model opacity and fragility remain major obstacles to trustworthy use. One barrier is the lack of a well-calibrated measure of similarity across arbitrary neural networks trained with different architectures, checkpoints, random initializations, and training strategies. Existing notions of model similarity span functional and representational domains, often rely on heuristic assumptions, and are susceptible to spurious signals introduced by probing samples, making principled cross-model meta-analysis difficult. Here, we clarify prevailing notions of deep neural network similarity and benchmark their robustness under extensive out-of-distribution perturbations. We then introduce the Ahmad RV coefficient on chain weight matrices (wARV), a theoretically grounded weight-space similarity measure that combines chain-normalized weights with the RV coefficient. wARV is sample-agnostic, symmetric, computationally efficient, and better calibrated than current measures. Across benchmarks varying random initialization, training checkpoint, architecture, and training strategy, wARV more faithfully tracks functional similarity while avoiding confounding effects from probing data. Applying wARV to deep neural network models on both generic and medical image classification tasks, we uncover substantial learning heterogeneity and instability even among models with similar predictive performance.
Maalvika Bhat
PhD Student
Technology and Social Behavior
School of Communication &
McCormick School of Engineering
Scholars See Clickbait as a Greater Threat to Science Than to Their Own Work
As scientific research competes for attention in a media landscape driven by sensationalism, the risks of misrepresentation grow. This study examines whether academics, while widely recognizing clickbait as a threat to science broadly, tend to downplay its relevance to their own work. Surveying 5,603 U.S.-based researchers, we find a consistent perception gap between systemic and personal risk, one that varies by career stage and disciplinary context. Early-career scholars show a pronounced version of this asymmetry: they express heightened concern about clickbait’s harms to science while rating its relevance to their own work as comparatively lower, a pattern that leaves them most exposed at a stage when reputational stakes are highest.
Rochana Chaturvedi
Postdoctoral Fellow
Kellogg School of Management &
Northwestern Institute on Complex Systems
Who Gets the Callback? Generative Artificial Intelligence and Gender Bias
Large language models are increasingly embedded in hiring workflows, raising concerns about their potential to amplify societal biases — yet how these biases manifest within and across occupations, and the role of model 'personality' in shaping these biases, remains unexplored. We introduce a three-part attribution framework applied to 332,044 real-world job ads, measuring gender-based callback bias, associations of skills and traits with gendered stereotypes in LLMs, and the effect of simulated recruiter personas. We find that LLMs systematically favor men, especially in higher-wage roles, with their decisions tracking traditional gendered language cues in job postings. Notably, assigning a low-agreeableness persona reduces model bias, implicating sycophancy as a mechanism reinforcing societal stereotypes; at the same time, controversial personas trigger internal guardrails leading to more cautious and less-biased outputs. These findings highlight how alignment choices in AI-driven hiring systems shape bias, with important implications for fairness and diversity.
Location:
In person: Chambers Hall, 600 Foster Street, Lower Level
Remote option: https://northwestern.zoom.us/j/98031689779
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems, networks, and artificial intelligence. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, May 20, 2026 at 12:00 PM - 1:00 PM
Location
Lower Level, Chambers Hall Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)