Detecting Propaganda Techniques in Code-Switched Social Media Text
Submission Type: Regular Long Paper
Submission Track: Sentiment Analysis, Stylistic Analysis, and Argument Mining
Submission Track 2: Multilinguality and Linguistic Diversity
Keywords: propaganda detection, code-switching, low-resource languages, multilinguality, roman-urdu, natural language processing
TL;DR: We propose a novel task of detecting propaganda techniques in code-switched data, create an annotated corpus for it, and confirm through analysis that directly modeling multilinguality yields better results than translation of code-switched text.
Abstract: Propaganda is a form of communication intended to influence the opinions and the mindset of the public to promote a particular agenda. With the rise of social media, propaganda has spread rapidly, leading to the need for automatic propaganda detection systems. Most work on propaganda detection has focused on high-resource languages, such as English, and little effort has been made to detect propaganda for low-resource languages. Yet, it is common to find a mix of multiple languages in social media communication, a phenomenon known as code-switching. Code-switching combines different languages within the same text, which poses a challenge for automatic systems. Considering this premise, we propose a novel task of detecting propaganda techniques in code-switched text. To support this task, we create a corpus of 1,030 texts code-switching between English and Roman Urdu, annotated with 20 propaganda techniques at fragment-level. We perform a number of experiments contrasting different experimental setups, and we find that it is important to model the multilinguality directly rather than using translation as well as to use the right fine-tuning strategy. We plan to publicly release our code and dataset.
Submission Number: 2023
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