Understanding the Dark Side of Internet Memes

Date Monday, May 20, 2024
LocationISI Foundation, Seminar Room 1st floor
Speaker(s)Usman Naseem, Lecturer (~Asst. Prof.) School of Computing, Macquarie University, Australia
Computational Social ScienceData Science
Foto di Kevin Gonzalez su Unsplash

Dr. Usman Naseem, Lecturer (~Asst. Prof.)
School of Computing, Macquarie University, Australia
Usman Naseem is a Lecturer (~Assistant Professor) in the School of Computing at Macquarie University, Australia. Before transitioning to academia, he worked in industry for over 10 years in various technical and leadership roles. His research interests include NLP, multimodality, and social computing, focusing on developing socially aware methods. He actively contributes to the academic community by serving on the program committees, including as an area chair, for prestigious conferences such as ACL, EMNLP, COLING, WSDM, Webconf, SIGIR, ACM MM, and IEEE Transactions. He is the recipient of several national and international awards, grants, and fellowships, including Best Paper Awards from IEEE Transactions in 2022 and at the EMNLP 2023 workshop and the DAAD AINet fellowship in 2023. More about him can be found here:

Memes, a prevalent form of humor in the digital era, efficiently convey sarcasm, wit, and cultural references, spreading rapidly on the Internet due to their relatability and shareability. While memes offer insights into online communities and the dynamics of information diffusion, they also pose risks by spreading misinformation, hate speech, cyberbullying, and harmful content.
In this talk, I will discuss our work on the MemeAnalytics project, which aims to enhance our understanding of memes shared in online communities. The work focuses on developing advanced computational methods for large-scale meme analysis. Specifically, I will present two projects that showcase the creation of novel algorithms to identify misinformation and harmful content within memes. Concluding the talk, I will share the results that demonstrate the efficacy and adaptability of these models in safeguarding online communities from the darker aspects of meme culture.

Published on friday, 17 may 2024

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