‘Subjectivity’ in newspaper reports on ‘controversial’ and ‘emotional’ debates: An appraisal and controversy analysis

Jakaza, Ernest; Visser, Marianna

doi:10.1080/10228195.2015.1060513

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…The PONC is a collection of online news articles from two leading Polish TV news sources: TVN24 and TVP Info. Controversial topics tend to have more intense emotional charge [76,77] and we decided to focus on them to look for news articles with examples of propaganda techniques. We prepared another subset of the PONC, i.e., a high-emotional-charge subset, in which we selected the highest value for each of the five emotions, i.e., anger, disgust, fear, happiness and sadness, and we selected the top 9 articles per emotion and per news provider.…”

Section: Polish Online News Corpus (Ponc)mentioning

confidence: 99%

Limitations of Large Language Models in Propaganda Detection Task

Szwoch,

Staszkow,

Rzepka

et al. 2024

Applied Sciences

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Propaganda in the digital era is often associated with online news. In this study, we focused on the use of large language models and their detection of propaganda techniques in the electronic press to investigate whether it is a noteworthy replacement for human annotators. We prepared prompts for generative pre-trained transformer models to find spans in news articles where propaganda techniques appear and name them. Our study was divided into three experiments on different datasets—two based on an annotated SemEval2020 Task 11 corpora and one on an unannotated subset of the Polish Online News Corpus, which we claim to be an even bigger challenge as an example of an under-resourced language. Reproduction of the results of the first experiment resulted in a higher recall of 64.53% than the original run, and the highest precision of 81.82% was achieved for gpt-4-1106-preview CoT. None of our attempts outperformed the baseline F1 score. One of the attempts with gpt-4-0125-preview on original SemEval2020 Task 11 achieved an almost 20% F1 score, but it was below the baseline, which oscillated around 50%. Part of our work that was dedicated to Polish articles showed that gpt-4-0125-preview had a 74% accuracy in the binary detection of propaganda techniques and 69% in propaganda technique classification. The results for SemEval2020 show that the outputs of generative models tend to be unpredictable and are hardly reproducible for propaganda detection. For the time being, these are unreliable methods for this task, but we believe they can help to generate more training data.

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Section: Polish Online News Corpus (Ponc)mentioning

confidence: 99%

Limitations of Large Language Models in Propaganda Detection Task

Szwoch,

Staszkow,

Rzepka

et al. 2024

Applied Sciences

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“… and the analysis of written discourses -e.g. political and parliamentary discourses (Jakaza and Visser 2014a; Jakaza and Visser 2015a; Musiyiwa and Visser 2016), newspaper discourses Visser 2013, 2015;Jakaza and Visser 2015b), and business discoursesand the work on argumentation and appraisal theory(Mugumya and Visser 2015;Jakaza and Visser 2015b, 2016a, 2016b, 2016cSabao and Visser 2016).…”

mentioning

confidence: 99%

From African languages to an African perspective on language: The work and research of Prof. Marianna W. Visser

Andrason¹

2019

SPILPLUS

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This paper discusses the contribution of Prof. Marianna W. Visser to African linguistics. I present the academic and professional trajectory of Prof. Visser and her research achievements, focusing on publications in three branches of language science: formal linguistics (morphosyntax and semantics), applied linguistics (language policy and multilingual education), and text-linguistics (appraisal theory and discourse analysis). These three areas and their respective themes are subsequently reflected in the selection of articles included in the volume, which this article also introduces.

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