Svitlana Volkova is a recognized leader in the field of computational social science and computational linguistics. Her scientific contributions and publication profile cover a range of topics on applied machine learning, deep learning, natural language processing, and social media analytics. Her research focuses on understanding, predicting, and explaining human behavior, interactions, and real-world events from open-source social data. Approaches developed by Dr. Volkova advance effective decision making and reasoning about extreme volumes of dynamic, multilingual, multimodal, and diverse real-world unstructured data.
Disciplines and Skills
Expert knowledge: social media predictive analytics, natural language processing, text analytics, machine learning, deep learning, social network analysis, data mining, probability and statistics, information retrieval, information extraction, artificial intelligence, graphs, and optimization
Programming languages: Python, Java, C#
Databases: SQL, MongoDB
ML tools: scikit-learn, keras, tensorflow, R, numpy, scipy, pandas
Languages: English (fluent), Russian, Ukrainian (native).
Johns Hopkins University, Center for Language and Speech Processing
Ph.D., Computer Science
Dissertation: Predicting Demographics and Affect in Social Networks
Committee: Benjamin Van Durme, David Yarowsky, and Philip Resnik
Kansas State University, Manhattan, Kansas, USA
M.S., Computer Science
Thesis: Entity Extraction, Animal Disease-Related Event Recognition from Web. Advisors: William H. Hsu and Doina Caragea
Petro Mohyla Black Sea State University, Mykolayiv, Ukraine
M.S. cum Laude, B.S. cum Laude, Computer Science
Thesis: Building a System for Testing Knowledge of Students in Engineering. Advisor: Yuriy Kondratenko
Affiliations and Professional Service
Pacific Northwest National Laboratory, Richland, Washington, USA
Senior Research Scientist, Data Sciences and Analytics
National Security Directorate
- DARPA SocialSim: Computational Simulation of Online Social Behavior (Principle Investigator (PI); FY18 $1.2M; FY18 – FY21: $5.5M).
- Graph-Based Deep Learning on Real-World Data (PI; FY18 $200K).
- Deception Detection and Tracking in Social and News Media (PI; FY17 $230K, FY18 $185K).
- SQUINT: Streaming Query User Interfaces (PI; FY17 $280K).
- Deep Learning of Multilingual Distributed Representations from Large Streaming Text Data
(Co-PI; FY17 $400K).
- Forecasting the Future Using Diverse Social Media Sources (Co-PI; FY17 $500K).
- FY16: Led tasks on forecasting influenza dynamics, language change, and multilingual connotation dynamics in social media.
Awards and Recognitions
2017 Vice President, ACM Future of Computing Academy
2016 Author of the Year, National Security Directorate
2016 Grace Hopper Speaker, Artificial Intelligence Track
2010 Google Anita Borg Memorial Scholarship Award
2008 Fulbright Graduate Student Scholarship Award
Classification of Social Media Postings as Trusted News or as Types of Suspicious News. S. Volkova. U.S. Patent.
Methods to Determine Likelihood of Social Media Account Deletion. E. Bell, S. Volkova. U.S. Patent.
Crowdsourced, Grounded Language for Intent Modeling in Conversational Interfaces. C. Brockett, P. Choudhury, B. Dolan, Y.C. Ju, P. Pantel, N. Mallory, S. Volkova. US20130262114 A1.
- Explaining Multimodal Deceptive News Prediction Models. S. Volkova, E. Ayton, D. Arendt, Zhuanyi Huang, and B. Hutchinson. ICWSM 2019.
- Characterizing Speed and Scale of Cryptocurrency Discussion Growth on Reddit. M. Glenski, E. Grace and S. Volkova. The Web Conference 2019. Acceptance rate: 16.2%.
- Evaluation and Validation Approaches for Simulation of Social Behavior: Challenges and Opportunities, E. Grace, L. M. Blaha, A. V. Sathanur, N. Hodas, S. Volkova, and M. Greaves. Social-Behavioral Modeling for Complex Systems. 2019.
- Vulnerable to Misinformation? Verifi! A. Karduni, I. Cho, R. Wesslen, S. Santhanam, S. Volkova, D. Arendt, S. Shaikh, and W. Dou. ACM IUI 2019. Acceptance rate: 25%.
- Towards Rapid Interactive Machine Learning: Evaluating Tradeoffs of Classification without Representation. Arendt D.L., E.G. Saldanha, R. Wesslen, S. Volkova, and W. Dou. In ACM IUI 2019. Acceptance rate: 25%.
- Propagation from Deceptive News Sources: Who Shares, How Much, How Evenly, and How Quickly? M. Glenski, T. Weninger and S. Volkova. IEEE Transactions on Computational Social Systems. (Volume: 5, Issue: 4, Dec. 2018).
- How Humans versus Bots React to Deceptive and Trusted News Sources: A Case Study of Active Users. M. Glenski, T. Weninger and S. Volkova. ASONAM’18.
- An Enriched Sentiment Analysis Dataset for Social Media in Russian. A. Rogers, A. Romanov, A. Rumshisky, S. Volkova, M. Gronas and A. Gribov. COLING’18.
- Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources. M. Glenski, T. Weninger and S. Volkova. ACL’18 (short).
- Can You Verifi This? Studying Uncertainty and Decision-Making About Misinformation using Visual Analytics. A. Karduni, I. Cho, R. Wesslen, S. Santhanam, S. Volkova, D. Arendt, S. Shaikh, and W. Dou. ICWSM’18.
- Predicting Foreign Language Usage from English-Only Social Media Posts. S. Volkova, S. Ranshous and L. Phillips. NAACL’18 (short).
- Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media. S. Volkova and J. Jang. Track on Journalism, Misinformation and Fact Checking. WWW’18.
- Forecasting Influenza-like Illness Dynamics for Military Populations using Neural Networks and Social Media. S. Volkova, E. Ayton, K. Porterfield, C. Corley. PLoS ONE 10(9): e0188941. doi: 10.1371/journal.pone.0188941.
- Interactive Machine Learning at Scale with CHISSL. D. Arendt, E. Grace, and S. Volkova. AAAI’18 (demo).
- CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data. I. Cho, R. Wesslen, S. Volkova, B. Ribarsky, and W. Dou. IEEE VIS’17.
- Truth of Varying Shades: On Political Fact-Checking and Fake News. H. Rashkin, E. Choi, J.Y. Jang, Y. Choi, S. Volkova. EMNLP’17 (short).
- Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams. L. Phillips, K. Shaffer, D. Arendt, N. Hodas, S. Volkova. 2nd Workshop on Representation Learning for NLP, ACL’17.
- Uncovering the Relationships Between Military Community Health and Affects Expressed in Social Media. S. Volkova, L.E. Charles, J. Harrison, C. Corley. EPJ Data Science Journal, 2017.
- ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media. D. Arendt, S. Volkova. ACL’17 (demo).
- Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. S. Volkova, K. Shaffer, J. Yea Jang, N. Hodas. ACL’17 (short).
- Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast. H. Rashkin, E. Bell, Y. Choi, S. Volkova. ACL’17 (short).
- Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter across Languages. S. Volkova, E. Bell. ICWSM’17.
- Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network. I. Stewart, D. Arendt, E. Bell, S. Volkova. ICWSM’17 (short).
- Contrasting Public Opinion Dynamics and Emotional Response during Crisis. S. Volkova, I. Chetviorkin, D. Arendt, B. Van Durme. SocInfo’16.
- Using Social Media to Measure Wellbeing: A Large-Scale Study of Emotional Response in Academic Discourse. S. Volkova, K. Han, C. Corley. SocInfo’16.
- Understanding Roles of Social Media in Academic Engagement and Satisfaction for Graduate Students. K. Han, S. Volkova, C. Corley. CHI’16.
- Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active during the Russian-Ukrainian Crisis. S. Volkova and E. Bell. Computational Approaches to Deception Detection, NAACL’16.
- Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast. S. Volkova, Y. Bachrach. ACL’16.
- Mining User Interests to Predict Perceived Psycho-Demographic Traits on Twitter. S. Volkova, Y. Bachrach, B. Van Durme. IEEE BigData’16.
- On Predicting Socio-Demographic Traits and Emotions in Social Networks and Implications to Online Self-Disclosure. S. Volkova, Y. Bachrach. Cyberpsychology, Behavior, and Social Networking, 2015.
- Studying User Income through Language, Behavior and Affect in Social Media. D. Preotiuc-Pietro, S. Volkova, V. Lampos, Y. Bachrach, N. Aletras. PLoS ONE 10(9), 2015.
- Using Emotions to Predict User Interest Areas in Online Social Networks. Y. Lewenberg, Y. Bachrach, S. Volkova. IEEE DSAA’15.
- Online Bayesian Models for Personal Analytics in Social Media. S. Volkova, B. Van Durme. AAAI’15.
- Inferring Latent User Properties from Texts Published in Social Media (Demo). S. Volkova, Y. Bachrach. AAAI’15.
- Improving Gender Prediction of Social Media Users via Weighted Annotator Rationales. S. Volkova, D. Yarowsky. Workshop on Personalization: Methods and Applications. NIPS’14.
- Inferring User Political Preferences from Streaming Communications. S. Volkova, G. Coppersmith, B. Van Durme. ACL’14.
- Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media. S. Volkova, T. Wilson, D. Yarowsky. EMNLP’13.
- Lightly Supervised Learning of Procedural Dialog Systems. S. Volkova, P. Choudhury, C. Quirk. B. Dolan, L. Zettlemoyer. ACL’13.
- Exploring Sentiment in Social Media: Bootstrapping Subjectivity Clues from Multilingual Twitter Streams. S. Volkova, T. Wilson, D. Yarowsky. ACL’13.
- Learning to Relate Literal and Sentimental Descriptions of Visual Properties. M. Yatskar, S. Volkova, A. Celikyilmaz, B. Dolan, L. Zettlemoyer. NAACL’13.
- CLex: A Lexicon for Exploring Color, Concept and Emotion Associations in Language. S. Volkova, B. Dolan, T. Wilson. EACL’12.