Search Results for author: Ahmadreza Mosallanezhad

Found 10 papers, 3 papers with code

Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach

1 code implementation ACL 2021 Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall, Huan Liu

The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session.

Toward Privacy and Utility Preserving Image Representation

no code implementations30 Sep 2020 Ahmadreza Mosallanezhad, Yasin N. Silva, Michelle V. Mancenido, Huan Liu

Face images are rich data items that are useful and can easily be collected in many applications, such as in 1-to-1 face verification tasks in the domain of security and surveillance systems.

Face Verification Privacy Preserving

"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?

1 code implementation10 Dec 2020 Mansooreh Karami, Ahmadreza Mosallanezhad, Michelle V Mancenido, Huan Liu

Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.

Sentence Sentiment Analysis +1

Causal Learning for Socially Responsible AI

no code implementations25 Apr 2021 Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu

The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.

Fairness

Domain Adaptive Fake News Detection via Reinforcement Learning

no code implementations16 Feb 2022 Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu, Michelle V. Mancenido, Huan Liu

With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news.

Fake News Detection reinforcement-learning +1

Estimating Topic Exposure for Under-Represented Users on Social Media

no code implementations7 Aug 2022 Mansooreh Karami, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu

To reduce the bias induced by the contributors, in this work, we focus on highlighting the engagers' contributions in the observed data as they are more likely to contribute when compared to lurkers, and they comprise a bigger population as compared to the contributors.

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