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05/21/2023
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Textual Classification Models via Adversarial Perturbation
Local Surrogate models have increased in popularity for use in explainin...
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04/03/2023
Understanding Individual and Team-based Human Factors in Detecting Deepfake Texts
In recent years, Natural Language Generation (NLG) techniques in AI (e.g...
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03/18/2023
NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online
Online texts with toxic content are a threat in social media that might ...
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01/16/2023
CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild
User-generated textual contents on the Internet are often noisy, erroneo...
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11/17/2022
UPTON: Unattributable Authorship Text via Data Poisoning
In online medium such as opinion column in Bloomberg, The Guardian and W...
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10/19/2022
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective
Two interlocking research questions of growing interest and importance i...
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03/19/2022
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
We proposes a novel algorithm, ANTHRO, that inductively extracts over 60...
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03/15/2022
Do Language Models Plagiarize?
Past literature has illustrated that language models do not fully unders...
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10/20/2021
Adversarial Socialbot Learning via Multi-Agent Deep Hierarchical Reinforcement Learning
Socialbots are software-driven user accounts on social platforms, acting...
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05/31/2021
Large-Scale Data-Driven Airline Market Influence Maximization
We present a prediction-driven optimization framework to maximize the ma...
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11/20/2020
Detecting Universal Trigger's Adversarial Attack with Honeypot
The Universal Trigger (UniTrigger) is a recently-proposed powerful adver...
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11/17/2020
SIENA: Stochastic Multi-Expert Neural Patcher
Neural network (NN) models that are solely trained to maximize the likel...
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09/01/2020
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
In recent years, the proliferation of so-called "fake news" has caused m...
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11/05/2019
Why X rather than Y? Explaining Neural Model' Predictions by Generating Intervention Counterfactual Samples
Even though the topic of explainable AI/ML is very popular in text and c...
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10/05/2017
Machine Learning Based Detection of Clickbait Posts in Social Media
Clickbait (headlines) make use of misleading titles that hide critical i...
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07/16/2011