ETHOS: a multi-label hate speech detection dataset
Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos, Grigorios Tsoumakas
Complex and Intelligent Systems, Springer
In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined.
VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance
Spyridon Paraschos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.
LionForests: Local Interpretation of Random Forests
Ioannis Mollas, Nick Bassiliades, Ioannis Vlahavas, Grigorios Tsoumakas
NeHuAI-2020 Workshop of ECAI2020
In this paper, we provide a sequence of actions for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An explanation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
AIMLAI-XKDD at ECMLPKDD 2019
This paper explores a methodology on providing explanations for a neural network's decisions, in a local scope, through a process that actively takes into consideration the neural network's architecture on creating an instance's neighbourhood, that assures the adjacency among the generated neighbours and the instance.
Hatebusters: A Web Application For Actively Reporting Youtube Hate Speech
Antonios Anagnostou, Ioannis Mollas, Grigorios Tsoumakas
Hatebusters is a web application for actively reporting YouTube hate speech, aiming to establish an online community of volunteer citizens. Hatebusters searches YouTube for videos with potentially hateful comments, scores their comments with a classifier trained on human-annotated data and presents users those comments with the highest probability of being hate speech. It also employs gamification elements, such as achievements and leaderboards, to drive user engagement.