Nikolaos Nikolaidis, Nicolas Stefanovitch, Purificação Silvano, Dimitar Iliyanov Dimitrov, Roman Yangarber, Nuno Guimarães, Elisa Sartori, Ion Androutsopoulos, Preslav Nakov, Giovanni Da San Martino, Jakub Piskorski. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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.

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). 2023.

In this paper we report on the complexity of persuasion technique annotation in the context of a large multilingual annotation campaign involving 6 languages and approximately 40 annotators.We highlight the techniques that appear to be difficult for humans to annotate and elaborate on our findings on the causes of this phenomenon.We introduce Holistic IAA, a new word embedding-based annotator agreement metric and we report on various experiments using this metric and its correlation with the traditional Inter Annotator Agreement (IAA) metrics.However, given somewhat limited and loose interaction between annotators, i.e., only a few annotators annotate the same document subsets, we try to devise a way to assess the coherence of the entire dataset and strive to find a good proxy for IAA between annotators tasked to annotate different documents and in different languages, for which classical IAA metrics can not be applied.

This paper presents, to the best of our knowledge, the first ever publicly available annotated dataset for sentiment classification and semantic polarity dictionary for Georgian. The characteristics of these resources and the process of their creation are described in detail. The results of various experiments on the performance of both lexicon- and machine learning-based models for Georgian sentiment classification are also reported. Both 3-label (positive, neutral, negative) and 4-label settings (same labels + mixed) are considered. The machine learning models explored include, i.a., logistic regression, SVMs, and transformed-based models. We also explore transfer learning- and translation-based (to a well-supported language) approaches. The obtained results for Georgian are on par with the state-of-the-art results in sentiment classification for well studied languages when using training data of comparable size.

In this paper, we present a novel solution for the problem of text extraction from PDF documents with incorrect encoding of embedded fonts. Instead of using a full-scale optical character recognition (OCR) for such documents, our system detects problematic fonts and focuses on their restoration, rather than the actual text. It parsimoniously uses a convolutional neural network (CNN) for performing optical character recognition (OCR) of individual glyphs, classifying them to homoglyph groups. Font glyph-character mappings are recovered by combining glyph location information with a language model. We compare our approach against a fullscale OCR baseline on a new dataset of multilingual PDF files that we made available to the research community. Our evaluation on a variety of test cases shows that our approach has a consistently lower error rate and a significantly faster processing time.

ISSN 2300-5963