This talk is part of the NLP Seminar Series.

I can’t Believe it’s not Better: Detecting and Quantifying Misinformation and Disinformation

Svitlana Volkova, Pacific Northwest National Laboratory
Date: 11:00 pm - 12:00 pm, Feb 28 2019
Venue: Room 104, Gates Computer Science Building


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. In this talk I will review computational approaches to identify deception online, measure its immediate spread and quantify user reactions to it. I will start by presenting linguistically-infused neural network models for deception detection and an in-depth linguistic analysis across broad deceptive news categories. These categories are grouped based on an intent to deceive, ranging from disinformation to misinformation. I will then show how neural models can be extended to make predictions in multilingual and multimodal settings, and present a tool to explain multimodal predictions, highlighting challenges and model limitations. Next, I will talk about the immediate 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. Finally, I will flesh out user reactions to deceptive news, distinguishing the reactions of users identified as bots versus humans.


Dr. Svitlana Volkova is a Senior Research Scientist in the Data Sciences and Analytics Group, National Security Directorate at Pacific Northwest National Laboratory. Dr. Volkova’s research focuses on applying natural language processing, machine learning, deep learning techniques to build novel predictive and forecasting social media analytics. Dr. Volkova’s models advance understanding, analysis, and effective reasoning about extreme volumes of dynamic, multilingual, and diverse real-world social media data. Her recent work on forecasting social media analytics includes anticipating multilingual perspective dynamics, detecting changes in language during crisis events, and forecasting future events, influenza dynamics, and weather across geolocations from social signals. Predictive analytics developed by Dr. Volkova concentrate on identifying suspicious accounts e.g., bots and trolls, and predicting deceptive news and their spread in social networks. Svitlana interned at Microsoft Research at the Natural Language Processing and Machine Learning and Perception teams. She was awarded the Google Anita Borg Memorial Scholarship in 2010 and the Fulbright Scholarship in 2008. Svitlana is a Vice Chair of the ACM Future of Computing Academy. She received her PhD in Computer Science in 2015 from Johns Hopkins University where she was affiliated with the Center for Language and Speech Processing and the Human Language Technology Center of Excellence.