activity
20182024
most citedA longitudinal study of the top 1% toxic Twitter profiles

12 citations · 26 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CL2024★ 2 cited

ConvoCache: Smart Re-Use of Chatbot Responses

Conor Atkins, Ian Wood, Mohamed Ali Kaafar +3

We present ConvoCache, a conversational caching system that solves the problem of slow and expensive generative AI models in spoken chatbots. ConvoCache finds a semantically simila…

cs.SI2024

SenTopX: Benchmark for User Sentiment on Various Topics

Hina Qayyum, Muhammad Ikram, Benjamin Zhao +3

Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered…

cs.CY2024★ 2 cited

On mission Twitter Profiles: A Study of Selective Toxic Behavior

Hina Qayyum, Muhammad Ikram, Benjamin Zi Hao Zhao +3

The argument for persistent social media influence campaigns, often funded by malicious entities, is gaining traction. These entities utilize instrumented profiles to disseminate d…

cs.SI2024★ 2 cited

Exploring the Distinctive Tweeting Patterns of Toxic Twitter Users

Hina Qayyum, Muhammad Ikram, Benjamin Zi Hao Zhao +3

In the pursuit of bolstering user safety, social media platforms deploy active moderation strategies, including content removal and user suspension. These measures target users eng…

cs.CR2023

Challenges with Passwordless FIDO2 in an Enterprise Setting: A Usability Study

Michal Kepkowski, Maciej Machulak, Ian Wood +1

Fast Identity Online 2 (FIDO2), a modern authentication protocol, is gaining popularity as a default strong authentication mechanism. It has been recognized as a leading candidate…

cs.CR2023★ 2 cited

An analysis of scam baiting calls: Identifying and extracting scam stages and scripts

Ian Wood, Michal Kepkowski, Leron Zinatullin +2

Phone scams remain a difficult problem to tackle due to the combination of protocol limitations, legal enforcement challenges and advances in technology enabling attackers to hide…

cs.CL2023★ 1 cited

Those Aren't Your Memories, They're Somebody Else's: Seeding Misinformation in Chat Bot Memories

Conor Atkins, Benjamin Zi Hao Zhao, Hassan Jameel Asghar +2

One of the new developments in chit-chat bots is a long-term memory mechanism that remembers information from past conversations for increasing engagement and consistency of respon…

cs.SI2023★ 12 cited

A longitudinal study of the top 1% toxic Twitter profiles

Hina Qayyum, Benjamin Zi Hao Zhao, Ian D. Wood +3

Toxicity is endemic to online social networks including Twitter. It follows a Pareto like distribution where most of the toxicity is generated by a very small number of profiles an…

cs.CR2022★ 1 cited

Unintended Memorization and Timing Attacks in Named Entity Recognition Models

Rana Salal Ali, Benjamin Zi Hao Zhao, Hassan Jameel Asghar +3

Named entity recognition models (NER), are widely used for identifying named entities (e.g., individuals, locations, and other information) in text documents. Machine learning base…

cs.CR2022★ 1 cited

How Not to Handle Keys: Timing Attacks on FIDO Authenticator Privacy

Michal Kepkowski, Lucjan Hanzlik, Ian Wood +1

This paper presents a timing attack on the FIDO2 (Fast IDentity Online) authentication protocol that allows attackers to link user accounts stored in vulnerable authenticators, a s…

cs.SI2022

A deep dive into the consistently toxic 1% of Twitter

Hina Qayyum, Benjamin Zi Hao Zhao, Ian D. Wood +3

Misbehavior in online social networks (OSN) is an ever-growing phenomenon. The research to date tends to focus on the deployment of machine learning to identify and classify types…

cs.CV2021★ 3 cited

ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning

Yufei Wang, Ian D. Wood, Stephen Wan +1

Novel Object Captioning is a zero-shot Image Captioning task requiring describing objects not seen in the training captions, but for which information is available from external ob…

cs.LG2018

Modality-based Factorization for Multimodal Fusion

Elham J. Barezi, Peyman Momeni, Pascale Fung

We propose a novel method, Modality-based Redundancy Reduction Fusion (MRRF), for understanding and modulating the relative contribution of each modality in multimodal inference ta…