Papers by Svitlana Volkova
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian (C18-1)
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| Challenge: | RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
| Approach: | They propose to use RuSentiment to annotate social media posts in Russian with a kappa of 0.58 and a set of annotation guidelines that are extensible to other languages. |
| Outcome: | The proposed dataset is the largest in its class for Russian, with 31,185 posts annotated with Fleiss’ kappa of 0.58 (3 annotations per post). |
Predicting Foreign Language Usage from English-Only Social Media Posts (N18-2)
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| Challenge: | Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. |
| Approach: | They analyze 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English to build predictive models to infer non-English languages users speak exclusively from their tweets. |
| Outcome: | The proposed models are based on a corpus of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English . they show that content, style and syntax are the most predictive of non-English languages that users speak on Twitter. |
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies (2026.findings-acl)
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Myke C. Cohen, Mingqian Zheng, Neel Bhandari, Hsien-Te Kao, Xuhui Zhou, Daniel Nguyen, Laura Cassani, Maarten Sap, Svitlana Volkova
| Challenge: | In simulations, personality traits and AI attributes were comparatively influential, but with actual human subjects, AI attributes – particularly transparency – were much more impactful. |
| Approach: | They compare a purely simulated dataset and a parallel human subjects experiment to examine how human personality traits and AI design characteristics jointly shape interaction outcomes in imperfectly cooperative scenarios. |
| Outcome: | The results show that personality traits and AI attributes are comparatively influential in simulations, but with actual human subjects, they are much more impactful. |
Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)
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Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov
| Challenge: | Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality. |
| Approach: | They propose an approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. |
| Outcome: | The proposed method alleviates the need for ad-hoc heuristics and achieves state-of-the-art results in scientific publications and news articles. |
Evaluating Neural Model Robustness for Machine Comprehension (2021.eacl-main)
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| Challenge: | evaluating model robustness to adversarial attacks can provide deeper understanding of how deep neural networks work and what kind of linguistic information is actually captured by neural networks. |
| Approach: | They propose a method for strategic sentence-level perturbations to evaluate model robustness to adversarial attacks using character and word perturbations. |
| Outcome: | The proposed model improves model performance during adversarial attacks by using ensembles and predicts errors in adversarials. |
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (P18-2)
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| Challenge: | a new study examines how users react to news sources with different levels of credibility . a recent study found that 59% of bitly-URLs on Twitter are shared without ever being read . |
| Approach: | They develop a model to classify user reactions into one of nine types . they also measure the speed and type of reaction for trusted and deceptive news sources . |
| Outcome: | The proposed model classifies user reactions into one of nine types, such as answer, elaboration, and question, etc. |
ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions (2024.findings-emnlp)
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Chan Young Park, Shuyue Stella Li, Hayoung Jung, Svitlana Volkova, Tanu Mitra, David Jurgens, Yulia Tsvetkov
| Challenge: | VALUESCOPE is a framework that quantifies social norms and values within online communities. |
| Approach: | They propose a framework that uses language models to quantify social norms and values within online communities. |
| Outcome: | The proposed framework delineates differences in social norms and tracks evolution of norms in online communities and influence of significant external events like the U.S. presidential elections and the emergence of new sub-communities. |