Large Language Models for Propaganda Span Annotation (2024)

Abstract: The use of propagandistic techniques in online content has increased in recent years aiming to manipulate online audiences. Fine-grained propaganda detection and extraction of textual spans where propaganda techniques are used, are essential for more informed content consumption. Automatic systems targeting the task over lower resourced languages are limited, usually obstructed by lack of large scale training datasets. Our study investigates whether Large Language Models (LLMs), such as GPT-4, can effectively extract propagandistic spans. We further study the potential of employing the model to collect more cost-effective annotations. Finally, we examine the effectiveness of labels provided by GPT-4 in training smaller language models for the task. The experiments are performed over a large-scale in-house manually annotated dataset. The results suggest that providing more annotation context to GPT-4 within prompts improves its performance compared to human annotators. Moreover, when serving as an expert annotator (consolidator), the model provides labels that have higher agreement with expert annotators, and lead to specialized models that achieve state-of-the-art over an unseen Arabic testing set. Finally, our work is the first to show the potential of utilizing LLMs to develop annotated datasets for propagandistic spans detection task prompting it with annotations from human annotators with limited expertise. All scripts and annotations will be shared with the community.

## introduction malicious actors are actively exploiting online platforms to disseminate misleading content for political, social, and economic agendas @xcite @xcite . the objective of using propaganda is to generate distorted and often misleading information, which can result in heightened polarization on specific issues and division among communities. hence, it is important to automatically detect and debunk propagandistic content. the majority of relevant research has focused on either binary or multiclass and multilabel classification scenarios of the task @xcite @xcite . more recently, interest has shifted to finer-grained propaganda detection at the text span level, which is a multilabel sequence tagging task, where more than one propaganda technique can be used within the same text span @xcite @xcite @xcite . such fine-grained analysis is necessary for system explainability and improved digital media literacy among news readers. the task in its nature is complex @xcite , and the complexity is magnified by the large number of propaganda techniques that might be present (18 @xcite vs. 23 @xcite ) techniques for example). the subjective nature of the task also results in added challenges. llms showed remarkable capabilities on versatile downstream nlp tasks, and on a plethora of languages, including arabic @xcite @xcite . however, the utility of llms in span-level propaganda detection and categorization remains under-explored. therefore, we aim to leverage llms selecting the highly effective, gpt-4 (ope-nai, 2023), for the task. moreover, llms have shown to be effective aids in creating annotated datasets to train or evaluate other models in a variety of tasks @xcite . since there are many propaganda techniques to label and a need to create large and diverse datasets to train specialized models, llms might benefit the process of developing new datasets for propaganda span detection. recruiting humans to carry such large-scale annotations has been a very tedious and costly pro-cedure. our study also aims to investigate whether we could use a llm, such as gpt-4, to reduce human annotation cost and effort by either reducing the number of annotators, or hiring annotators with less expertise. finally, to further understand the value of automatic propaganda labeling with llms, we employ labels generated by the model under different setups to train specialized language models for the task. specifically, we study the following research questions: (i) is gpt-4 capable of annotating propagandistic spans effectively? (ii) can gpt-4 serve both as a general and as an expert annotator of propaganda spans? foot_1 (iii) which propaganda techniques can gpt-4 annotate best? (iv) can we effectively train specialized models for the task using gpt-4's annotations? our study makes the following contributions: when testing that specialized model on the testing subset from our in-house dataset, it degraded performance by only 13% compared to training the model with the gold labels from the training subset. da san martino et al. (2019) identified 18 distinct techniques and created a dataset by manually annotating english news articles based on them. next, they designed a multi-granular deep neural network that extracts propagandistic spans from sentences with a limited f 1 =22.58, showing how complex the task is. piskorski et al. (2023b) extended the 18 techniques into 23 and introduced a dataset in multiple languages. with these efforts, fine-grained propaganda detection in general, and over arabic content and other lower-resourced languages specifically, still requires further exploration. existing arabic datasets are limited in size and number of targeted techniques @xcite . ## dataset existing arabic datasets for span-level propaganda detection either lack text span-level annotations (e.g., araieval 2023 shared task dataset @xcite ), or cover a more limited set of propaganda techniques (e.g., @xcite ). 4 furthermore, to explore the potential of using llms as propagandistic spans annotators, a comprehensive dataset with complete human annotations is required as a gold standard. for this study, an in-house developed dataset is utilized, referred to as arpro across this work. it includes a total of 8,000 annotated paragraphs among which, 63% contain at least one propagandistic span. the paragraphs were selected from 2.8k news articles, with approximately 10k sentences, and around 277k words. it covers 14 different topics, with 'news' and 'politics' accounting for over 50% of paragraphs. we split the dataset in a stratified manner @xcite , allocating 75%, 8.5%, and 16.5% for training, development, and testing, respectively. we briefly discuss the dataset development process. a complete detail of that process is provided in our recent work @xcite . the dataset construction started from a large inhouse collection of arabic news articles sourced from over 300 arabic news media, and including over 600k articles. we sample a set of 2.8k articles following a stratified sampling approach over the news media. thus, we ensure a versatile set, featuring a variety of writing styles and topics. after automatically parsing the articles, we split them into paragraphs and eliminate ill-formed paragraphs matching any of the following conditions: (i) containing any special character repeated more than three times (e.g., %, *, etc.), (ii) not arabic as classified by langdetect, foot_4 and (iii) containing html tags. the paragraphs were de-duplicated using cosine similarity, with a @xmath0.75 indicating duplication. the resulting news paragraphs were then manually annotated using 23 propaganda techniques, adopted from an existing taxonomy @xcite . the annotation process consisted of two phases: (i) in phase 1, three annotators individually annotated each paragraph, and (ii) in phase 2, two expert annotators revised and finalized the annotations. each annotator in this phase is referred to as a consolidator. to facilitate the annotation process, a platform was developed and a comprehensive annotation guideline in the native language (arabic) was provided to annotators. additionally, several training iterations were conducted before beginning the annotation task. the annotation agreement for span-level annotation is γ = 0.546. this γ agreement metric is specifically designed for span/segment-level annotation tasks, taking into account the span boundaries (i.e., start and end) and their labels @xcite . ## propagandistic spans annotation in this section, we describe our annotation framework including the manual annotation steps used for dataset construction, and the use of gpt-4 for different annotation roles. figure 1 illustrates this framework. this section also describes a third annotation approach using fine-tuned models. ## experimental setup in this section, we describe the setup of the experiments and the evaluation approach followed to investigate the effectiveness of gpt-4 in playing different roles in the annotation process. ## results and discussion to address our research questions, we ran each of the annotation setup prompts ( as shown in incorrect start and end indices. in addition to detecting propaganda techniques, the model was required to provide the text spans matching these techniques (in the "annotator" and "selector" roles). since a span might occur multiple times in a paragraph, with different context and propagandistic technique, the model should also specify the start and end indices of these spans. we observed that although gpt-4 can correctly provide labels and extract associated text spans, it frequently generated indices not matching the corresponding spans in a paragraph. this lead to mismatch between the start and end indices of spans as compared to gold labels (as figure 2 shows). to overcome this problem, we a apply a postprocessing step by assigning for each predicted span, the start and end indices of its first occurrence in a paragraph. agreement with consolidators. we delve deeper into the quality of the model's annotations by comparing two values: (a) the agreement of the initial, less-experienced, annotators with the consolidators and (b) gpt-4 agreement with the consolidators (after start indices correction). as figure 3 shows, gpt-4 has notably higher agreement with consolidators compared to initial annotators. it demonstrates a 38% improved agreement when playing the role of a consolidator. these values demonstrate that gpt-4 achieves comparable or better agreement with the expert consolidators as compared to less experienced human annotators. moreover, it shows that the model is learning from the given initial annotations to produce improved annotations, closer to the consolidators' performance. gpt-4 annotate best? we looked at the top five per-technique agreement levels (γ) of the model's labels versus gold labels ( it is interesting to see that gpt-4 was highly effective in annotation of the "doubt" technique, which contradicts with a recent ranking of annotation difficulty of the same taxonomy, derived from humans' performance, in the same task across a multilingual dataset @xcite . however, its strong performance with the other two techniques is inline with the aforementioned ranking. the model's ability to annotate "loaded language" is particularly useful, as it is the most prevalent technique in the dataset, appearing 7.9k times in the training split under investigation. replacing human consolidators by gpt-4 to annotate for that technique can save tremendous time and cost. we believe these agreement levels give further evidence of the strong potential of employing gpt-4 as a propaganda span annotator, at least for some techniques. this analysis also provides data needed to inform decisions on which stages of annotation we can inject llms like gpt-4. ## conclusions in this study, we first investigate gpt-4's ability to play different roles in detecting propagandistic spans and annotating them in arabic news paragraphs. we investigate if gpt-4 can be used as an annotator when provided with sets of information of varied richness, which represents an increased cost and effort in hiring human annotators. moreover, we study the value of gpt-4's labels when used to train specialized models for the task. our experimental results suggest that providing more information significantly improves the model's annotation performance and agreement with human expert consolidators. the study also reveals the great potential of the model to replace consolidators, for some propaganda techniques. finally, we find that we can train effective models using labels provided by gpt-4 when acting as a consolidator. we offer an in-depth analysis of the model's performance across various annotation stages, facilitating a more informed adoption of this annotation approach. future research will explore additional models and learning setups.