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Activity Number: 301 - Natural Language Processing Applications in Defense and National Security
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Defense and National Security
Abstract #313915
Title: Neural Language Processing to Detect, Attribute, Characterize and Defend Against Digital Deception
Author(s): Svitlana Volkova*
Companies: Pacific Northwest National Laboratory
Keywords: deep learning; robustness; disinformation spread; natural language processing; multilingual, multimodal deception detection; user reactions to deceptive news

Social media has established a new era of news manipulation. Deceptive news — misleading, falsified and fabricated content — is routinely originated and spread on social platforms with the intent to create confusion and widen political and social divides. I will review computational approaches to detect deception online, explain model predictions and access robustness, measure the spread of deceptive content and quantify user reactions to it. I will start by presenting neural models and show how they can be extended to make predictions in multilingual and multimodal settings, and report model susceptibility to adversarial inputs. I will attribute and characterize user behavior while engaging with deceptive news. I will present the insights about the spread of deceptive news by characterizing the audience and measuring speed and scale of spread, to uncover who shares deceptive content, how quickly, how much and how evenly. I will analyze user reactions to deceptive news, distinguishing the reactions of users identified as bots versus humans. Finally, I flesh out a few ideas to take advantage of neural translation and generation models to enable better defense against deception.

Authors who are presenting talks have a * after their name.

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