Arizona State University

Microsoft Technology Licensing, LLC

Kai Shu

Huan Liu

Susan Theresa Dumais

Subhabrata Mukherjee

Milad Shokouhi

Guoqing Zheng

Deepak Mahudeswaran

Ahmed Awadalla

Suhang Wang

Ruocheng Guo

Ghazaleh Beigi

Jiliang Tang

Siqi Wu

Lu Cheng

Yasin Silva

Yilin Wang

Deborah Hall

Detecting fake news involves receiving a plurality of allegedly real news stories and allegedly fake news stories from one or more websites, receiving a plurality of user posts to a social media platform relating to the plurality of allegedly real news stories and allegedly fake news stories;

Method and apparatus for collecting, detecting and visualizing fake news

This document relates to training of machine learning models. One example method involves providing a machine learning model having a first classification layer, a second classification layer, and an encoder that feeds into the first classification layer and the second classification layer. The example method also involves obtaining first training examples having explicit labels and second training examples having inferred labels. The inferred labels are based at least on actions associated with the second training examples. The example method also involves training the machine learning model using the first training examples and the second training examples using a training objective that considers first training loss of the first classification layer for the explicit labels and second training loss of the second classification layer for the inferred labels. The method also involves outputting a trained machine learning model having the encoder and the first classification layer.

Joint learning from explicit and inferred labels

A computer-implemented framework and/or system for cyberbullying detection is disclosed. The system includes two main components: (1) A representation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time; and (2) a multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The system jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. The system includes an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.

Systems and methods for unsupervised cyberbullying detection via time-informed Gaussian mixture model

Various embodiments of a computer-implemented system which learns textual representations while filtering out potentially personally identifying data and retaining semantic meaning within the textual representations are disclosed herein.

Systems and methods for a privacy preserving text representation learning framework

Various embodiments of systems and methods for cross media joint friend and item recommendations are disclosed herein.

Systems and methods for a cross media joint friend and item recommendation framework

Detecting fake news involves analyzing a distribution of publishers who publish many news articles, analyzing a distribution of various topics relating to the published news articles, analyzing a social media context relating to the published news articles, and detecting fake news articles among the news articles based on the analysis of the distribution of publishers, the analysis of the distribution of the various topics, and the analysis of the social media context. Detecting fake news alternatively involves receiving online news articles including both fake online news articles and real online news articles, creating a hierarchical macro-level propagation network of the fake online news and real online news articles, the hierarchical macro-level propagation network comprising news nodes, social media post nodes, and social media repost nodes, creating a hierarchical micro-level propagation network of the fake online news and real online news articles, the hierarchical micro-level propagation network comprising reply nodes, analyzing structural and temporal features of the hierarchical macro-level propagation network, analyzing structural, temporal, and linguistic features of the hierarchical micro-level propagation network, and identifying fake news among the online news articles based on the analysis of the structural and temporal features of the hierarchical macro-level propagation network and the analysis of the structural, temporal, and linguistic features of the hierarchical micro-level propagation network.

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Kai Shu