Filipo Sharevski (DePaul University)

Learning how people deal with misinformation on social media is relatively new line of inquiry as so far the research has focused on narrow information manipulation interventions. As the dissemination and/or discernment of falsehoods is uniquely driven by users’ identity and self-representation on social media, the inquiry into the misinformation “folklore” entails careful balancing between extracting users’ actual behavior traits and ensuring face-saving and dignified participation. In this talk, I will cover the strategies employed in inquires regarding users’ dealings with misinformation on both mainstream platforms (e.g. Twitter, TikTok) and alternative platforms (e.g. Gettr) that rest of substantive data-driven analysis of the platform and/or misinformation issue of interest, pilot testing, and nuanced phenomenological observations. Misinformation as a topic enables abundant social media data to append the analysis of the qualitative user responses, so. I will also cover mixed analytical approaches that enrich the overall human-subject inquiry on social media misinformation.

Speaker's Biography

Dr. Filipo Sharevski is an associate professor of cybersecurity at DePaul University and the director of the Adversarial Cybersecurity Automation Lab. His main research interest is focused on information manipulation as it unfolds across cyberspace, particularly materialized through m/disinformation campaigns on social media, social engineering, adversarial machine learning, as well as usable security ergonomics. He regularly publishes in relevant cybersecurity venues, actively participates in the ACM and IEEE associations, and holds leadership positions in scientific communities focused on creating meaningful and equitable pathways of cybersecurity education. Dr. Sharevski earned the PhD degree in interdisciplinary information security at Purdue University, CERIAS in 2015.

View More Papers

Navigating Murky Waters: Automated Browser Feature Testing for Uncovering...

Mir Masood Ali (University of Illinois Chicago), Binoy Chitale (Stony Brook University), Mohammad Ghasemisharif (University of Illinois Chicago), Chris Kanich (University of Illinois Chicago), Nick Nikiforakis (Stony Brook University), Jason Polakis (University of Illinois Chicago)

Read More

podft: On Accelerating Dynamic Taint Analysis with Precise Path...

Zhiyou Tian (Xidian University), Cong Sun (Xidian University), Dongrui Zeng (Palo Alto Networks), Gang Tan (Pennsylvania State University)

Read More

The “Beatrix” Resurrections: Robust Backdoor Detection via Gram Matrices

Wanlun Ma (Swinburne University of Technology), Derui Wang (CSIRO’s Data61), Ruoxi Sun (The University of Adelaide & CSIRO's Data61), Minhui Xue (CSIRO's Data61), Sheng Wen (Swinburne University of Technology), Yang Xiang (Digital Research & Innovation Capability Platform, Swinburne University of Technology)

Read More

REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder...

Wenjie Qu (Huazhong University of Science and Technology), Jinyuan Jia (University of Illinois Urbana-Champaign), Neil Zhenqiang Gong (Duke University)

Read More