Search Results for author:
Found 9 papers, 3 papers with code
Towards Detecting Cascades of Biased Medical Claims on Twitter
Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health.
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs
Money laundering has become one of the most relevant criminal activities in modern societies, as it causes massive financial losses for governments, banks and other institutions.
Top-Down Deep Clustering with Multi-generator GANs
Our method is inspired by the observation that each generator of a MGAN tends to generate data that correlates with a sub-region of the real data distribution.
Fairness via AI: Bias Reduction in Medical Information
While part (3) of this work specifically focuses on the health domain, the fundamental computer science advances and contributions stemming from research efforts in bias reduction and Fairness via AI have broad implications in all areas of society.
Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study
Scientific knowledge cannot be seen as a set of isolated fields, but as a highly connected network.
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors
We evaluate the proposed model when predicting the HR of 23 subjects performing a variety of physical activities from IMU data available in public datasets (PAMAP2, PPG-DaLiA).
Characterizing (Un)moderated Textual Data in Social Systems
Our findings support that unmoderated environments may have proportionally more online hate speech.
Hate Speech Detection
Social and Information Networks
Predicting User Emotional Tone in Mental Disorder Online Communities
In this work, we analyze how discussions in Reddit communities related to mental disorders can help improve the health conditions of their users.
Selective Harvesting over Networks
We find that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as an ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect.
