BPGC at S em E val-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features Based Ensemble Learning (2020)

Abstract: Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Task-11 consists of two sub-tasks, namely, Span Identification - given any news article, the system tags those specific fragments which contain at least one propaganda technique; and Technique Classification - correctly classify a given propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach. For sub-task 2, we use an ensemble of BERT and logistic regression classifiers with linguistic features. Our results reveal that the linguistic features are the strong indicators for covering minority classes in a highly imbalanced dataset.

## introduction propaganda is biased information that deliberately propagates a particular ideology or political orientation @xcite . propaganda aims to influence the public's mentality and emotions, targeting their reciprocation due to their personal beliefs @xcite . news propaganda is a sub-type of propaganda that manipulates lies, semi-truths, and rumors in the disguise of credible news @xcite . the emphasis on this manipulation differentiates propaganda and its various classes from each other and free verbalization @xcite . news propaganda can lead to the mass circulation of misleading information, shared agenda, conflicts, religious or ethnic reasons, and can further even lead to violence and terrorism. due to massive size, high velocity, rich online user interaction, and diversity, the manual identification of propaganda techniques is overwhelmingly impractical. hence, the development of a generalized system for propaganda detection in news articles is a vital task for security analysts and society @xcite . in semeval2020-task11, @xcite propose a corpus of 550 news articles for propaganda detection. each article is annotated with propaganda spans belonging to 14 propaganda techniques. the annotation is performed at the fragment level. the task of propaganda detection is divided into two sub-tasks; span identification (sequence labeling) and technique classification (sequence classification). span identification sub-task aims to detect the propagandist spans of text in the news articles. whereas, the technique classification sub-task aims to classify the propaganda spans into various propaganda techniques. the work presented in this paper aims to provide independent approaches for both sub-tasks 1 and 2. most recent approaches for propaganda detection task use pre-trained transformer models. this work propose a multi-granularity knowledge sharing model built on top of the embeddings extracted from these transformer models for the propaganda span detection sub-task. further, we show the effectiveness of ensembling linguistic features-based machine learning classifiers with these transformer models covering the minority-classes for the technique classification sub-task. ## background before the ascension of the detection of fake news and propaganda in nlp, @xcite introduced the automatic detection of lying in text. in this work, the authors proposed three datasets based on abortion, death penalty, and best friends and approached the multi-class classification problem. the amazon turk service performed the annotations for these datasets. they used support vector machine and naive bayes classifiers for conducting their experiments. on the other hand, @xcite had employed knowledge graphs for computational fact-checking and utilizing data amassed from wikipedia. detection of propaganda in news articles was advocated by @xcite and @xcite . the former proposed the use of long short-term memory (lstm) models and machine learning methods for deception detection and classification to different types of news, trusted, satire, hoax, and propaganda. the latter presented proppy foot_0 the first publicly available real-world and real-time propaganda detection system for online news. fake news and propaganda have been getting more attention recently @xcite @xcite . previous research has leveraged bert @xcite to extract features for fake news detection @xcite . the constrained resources and lack of annotated corpora are considered to be an immense challenge for researchers in this field. @xcite proposes a new dataset with text articles annotated on the fragment level with one of 18 given propaganda techniques. to classify them, they also employ bert models for this high granularity task. given the relatively low count of some of these 18 techniques, they merged similar underrepresented techniques into superclasses like bandwagon, reductio ad hitlerum, exaggeration, minimisation, name calling, labelling, and whataboutism, straw men, red herring and eliminate obfuscation, intentional vagueness, confusion to compile 14 classes @xcite . ## methodology in this section, we explain our approach and the road-map to our conclusion. a detailed explanation of our implementations is provided in section 4. inspired by @xcite , we consider three granularities of propaganda detection: document-level, sentence/fragment-level, and token-level. the span identification sub-task focuses on high-granularity propaganda detection through a token-level sequence labeling task. whereas, the technique classification sub-task focuses on sentence/fragment level classification. even though both the sub-tasks are restricted to a single granularity, the context from other granularities can be used by defining auxiliary objectives for each granularity.