Nikolaos Nikolaidis


2024

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Exploring the Usability of Persuasion Techniques for Downstream Misinformation-related Classification Tasks
Nikolaos Nikolaidis | Jakub Piskorski | Nicolas Stefanovitch
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We systematically explore the predictive power of features derived from Persuasion Techniques detected in texts, for solving different tasks of interest for media analysis; notably: detecting mis/disinformation, fake news, propaganda, partisan news and conspiracy theories. Firstly, we propose a set of meaningful features, aiming to capture the persuasiveness of a text. Secondly, we assess the discriminatory power of these features in different text classification tasks on 8 selected datasets from the literature using two metrics. We also evaluate the per-task discriminatory power of each Persuasion Technique and report on different insights. We find out that most of these features have a noticeable potential to distinguish conspiracy theories, hyperpartisan news and propaganda, while we observed mixed results in the context of fake news detection.

2023

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On Experiments of Detecting Persuasion Techniques in Polish and Russian Online News: Preliminary Study
Nikolaos Nikolaidis | Nicolas Stefanovitch | Jakub Piskorski
Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)

This paper reports on the results of preliminary experiments on the detection of persuasion techniques in online news in Polish and Russian, using a taxonomy of 23 persuasion techniques. The evaluation addresses different aspects, namely, the granularity of the persuasion technique category, i.e., coarse- (6 labels) versus fine-grained (23 labels), and the focus of the classification, i.e., at which level the labels are detected (subword, sentence, or paragraph). We compare the performance of mono- verus multi-lingual-trained state-of-the-art transformed-based models in this context.

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Multilingual Multifaceted Understanding of Online News in Terms of Genre, Framing, and Persuasion Techniques
Jakub Piskorski | Nicolas Stefanovitch | Nikolaos Nikolaidis | Giovanni Da San Martino | Preslav Nakov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a new multilingual multifacet dataset of news articles, each annotated for genre (objective news reporting vs. opinion vs. satire), framing (what key aspects are highlighted), and persuasion techniques (logical fallacies, emotional appeals, ad hominem attacks, etc.). The persuasion techniques are annotated at the span level, using a taxonomy of 23 fine-grained techniques grouped into 6 coarse categories. The dataset contains 1,612 news articles covering recent news on current topics of public interest in six European languages (English, French, German, Italian, Polish, and Russian), with more than 37k annotated spans of persuasion techniques. We describe the dataset and the annotation process, and we report the evaluation results of multilabel classification experiments using state-of-the-art multilingual transformers at different levels of granularity: token-level, sentence-level, paragraph-level, and document-level.