Detecting Propaganda Techniques in English News Articles using Pre-trained Transformers

Abdullah, Malak; Altiti, Ola; Obiedat, Rasha

doi:10.1109/icics55353.2022.9811117

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…Although we report an accuracy of just 25.12% using the SemEval-2022 dataset, which is significantly lower than the accuracy scores achieved in the related work of Abdullah et al [7], we were able to obtain this with a number of constraints and with no optimisation at all. We also spotted several cases where the human labelling could be disputed and where the results from the model provide insights that the labellers missed.…”

Section: Discussioncontrasting

confidence: 73%

“…Previous works, such as those by Abedalla et al [5] and Abdullah et al [7], use well-known machine learning methods, such as Convolutional Neural Networks (CNNs) and Long Short-term Memory models (LSTMs), to detect propaganda techniques in news articles. These approaches are limited by their inability to provide an explanation behind the tags that are made, and reasonable performance can only be achieved when using a binary classification method.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Propaganda in News Articles Using Large Language Models

2024

Eng OA

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The proliferation of media channels as a result of the information age has ushered in a new era of communication and access to information. However, this increased accessibility has also opened up new avenues for propaganda and the manipulation of public opinion. With the recent release of OpenAI's artificial intelligence chatbot, ChatGPT, users and the media are increasingly discovering and reporting on its range of novel capabilities. The most notable of these, such as answering technical questions, stem from its ability to perform advanced natural language processing and text generation. In this paper, we aim to assess the feasibility of using the underlying technology behind ChatGPT, Large Language Models (LLMs), to detect features of propaganda in news articles. The features we consider leverage the work of Martino et al., who define a list of 18 distinct propaganda techniques. For example, they outline the 'straw man' technique, which refers to the use of 'refuting an argument that was not presented' [1]. Based on these techniques, we develop a refined prompt that is coupled with news articles from Russia Today (RT), a prominent state-controlled news network, and from the labelled SemEval-2020 Task 11 dataset [2]. The prompt and article content are then sent to OpenAI’s gpt-3.5-turbo model to determine which propaganda techniques are present and to make a final judgement on whether the article is propaganda or not. We then qualitatively analyse a subset of the resulting output to determine whether LLMs can be used effectively in this way. With the results of the study, we aim to uncover whether such technologies show promise in detecting propaganda, and what sort of prompts lead to the most useful output. This has the potential to be useful for media consumers, for example, who could use our prompts to detect signs of propaganda in the articles they read.

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Section: Discussioncontrasting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Detecting Propaganda in News Articles Using Large Language Models

2024

Eng OA

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“…Rumor is pieces of information that are unverified at the time of posting and can be verified later; in other words, they can’t be opinions or feelings ( Oshikawa, Qian & Wang, 2020 ). Propaganda is a biased or exaggerated story that aims to manipulate readers to advance a specific agenda ( Da San Martino et al, 2019 ; Abdullah, Altiti & Obiedat, 2022 ).…”

Section: Preliminariesmentioning

confidence: 99%

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations

Obeidat¹,

Gharaibeh²,

Abdullah³

et al. 2022

PeerJ Computer Science

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Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for instance, is crucial for the success of the global vaccination programs and thus achieving herd immunity. To combat the proliferation of COVID-19-related misinformation, considerable research efforts have been and are still being dedicated to building and sharing COVID-19 misinformation detection datasets and models for Arabic and other languages. However, most of these datasets provide binary (true/false) misinformation classifications. Besides, the few studies that support multi-class misinformation classification deal with a small set of misinformation classes or mix them with situational information classes. False news stories about COVID-19 are not equal; some tend to have more sinister effects than others (e.g., fake cures and false vaccine info). This suggests that identifying the sub-type of misinformation is critical for choosing the suitable action based on their level of seriousness, ranging from assigning warning labels to the susceptible post to removing the misleading post instantly. We develop comprehensive annotation guidelines in this work that define 19 fine-grained misinformation classes. Then, we release the first Arabic COVID-19-related misinformation dataset comprising about 6.7K tweets with multi-class and multi-label misinformation annotations. In addition, we release a version of the dataset to be the first Twitter Arabic dataset annotated exclusively with six different situational information classes. Identifying situational information (e.g., caution, help-seeking) helps authorities or individuals understand the situation during emergencies. To confirm the validity of the collected data, we define three classification tasks and experiment with various machine learning and transformer-based classifiers to offer baseline results for future research. The experimental results indicate the quality and validity of the data and its suitability for constructing misinformation and situational information classification models. The results also demonstrate the superiority of AraBERT-COV19, a transformer-based model pretrained on COVID-19-related tweets, with micro-averaged F-scores of 81.6% and 78.8% for the multi-class misinformation and situational information classification tasks, respectively. Label Powerset with linear SVC achieved the best performance among the presented methods for multi-label misinformation classification with micro-averaged F-scores of 76.69%.

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“…Significant technology corporations and social media platforms have proclaimed plans to employ content representatives due to public outcry and recognition that one platform inspires the spread of propaganda, false information, and fake news, as well as well-known insights that these platforms are responsible for these things [23]. Abdullah et al [24] developed a hybrid deep-learning model with the cutting-edge RoBERTa pre-trained language model to identify propaganda in 411 news items. Vlad et al [25] employed neural network models with basic linguistic patterns to identify propaganda at the sentence level in a dataset of 350 news stories.…”

Section: Related Workmentioning

confidence: 99%

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

et al. 2023

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Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking.

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Detecting Propaganda Techniques in English News Articles using Pre-trained Transformers

Cited by 8 publications

References 18 publications

Detecting Propaganda in News Articles Using Large Language Models

Detecting Propaganda in News Articles Using Large Language Models

Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

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