Papers by Veronika Solopova
Adapting Coreference Resolution to Twitter Conversations (2020.findings-emnlp)
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| Challenge: | Existing studies on coreference resolution for Twitter texts show that performance is low. |
| Approach: | They propose to use Twitter conversations to train a system that is originally trained on OntoNotes to improve coreference resolution. |
| Outcome: | The proposed system outperforms existing systems on Twitter by 21.6%. |
Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas (2026.findings-acl)
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Lukas Stähelin, Veronika Solopova, Max Upravitelev, David Kaplan, Premtim Sahitaj, Ariana Sahitaj, Charlott Jakob, Sebastian Möller, Vera Schmitt
| Challenge: | Propaganda detection in social media is challenging due to noisy, short texts and low annotation agreements. |
| Approach: | They propose a new intent-focused taxonomy of propaganda techniques and compare it against an established, higher-agreement schema. |
| Outcome: | The proposed taxonomy outperforms existing models and reveals methodological differences hidden in base models. |
Uncovering Temporal Framing in the News (2026.acl-long)
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Tarek Mahmoud, Veronika Solopova, Premtim Sahitaj, Ariana Sahitaj, Max Upravitelev, Mervat Abassy, Hana Fatima Shaikh, Neda Foroutan, Vera Schmitt, Preslav Nakov
| Challenge: | Temporal language is used to structure meaning rather than report chronology in news discourse . a recent study focused on temporal expression extraction and temporal reasoning . |
| Approach: | They propose a taxonomy of eight temporal frames grounded in prior work on time and framing . they analyze frame prevalence, co-occurrence patterns, and lexical cues from a news corpus . |
| Outcome: | The proposed taxonomy outperforms zero-shot models at the sentence level . it shows that temporal framing is learnable at the sentences level compared to other methods . |
Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection (2024.eacl-demo)
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| Challenge: | a global crisis of trust in news is causing many to avoid the news due to low credibility and negativity. |
| Approach: | They propose to use NLP to detect pro-Kremlin propaganda and explain manipulative linguistic features and keywords to provide feedback to users' news . |
| Outcome: | The proposed solution is based on user inputs and models’ behaviour paired with questionnaire answers and has been shown to be more effective than existing models. |