Question: What is the recent study of propaganda detection prior to this paper?
Evidence: Most of the existing works on propaganda detection focus on identifying propaganda at the news article level, or even at the news outlet level with the assumption that each of the articles of the suspected propagandistic outlet are propaganda BIBREF5, BIBREF6.
Here we study two tasks that are more fine-grained, specifically propaganda detection at the sentence and phrase (fragment) levels BIBREF4. This fine-grained setup aims to train models that identify linguistic propaganda techniques rather than distinguishing between the article source styles.
BIBREF4 EMNLP19DaSanMartino were the first to propose this problem setup and release it as a shared task. Along with the released dataset, BIBREF4 proposed a multi-granularity neural network, which uses the deep bidirectional transformer architecture known as BERT, which features pre-trained context-dependent embeddings BIBREF0. Their system takes a joint learning approach to the sentence- and phrase-level tasks, concatenating the output representation of the less granular (sentence-level) task with the more fine-grained task using learned weights.
In this work we also take the BERT model as the basis of our approach and focus on the class imbalance as well as the lack of similarity between training and test data inherent to the task.
It is worth reflecting on the nature of the shared task dataset (PTC corpus) and its structural correspondence (or lack thereof) to some of the definitions of propaganda mentioned in the introduction. First, propaganda is a social phenomenon and takes place as an act of communication BIBREF19, and so it is more than a simple information-theoretic message of zeros and ones—it also incorporates an addresser and addressee(s), each in phatic contact (typically via broadcast media), ideally with a shared denotational code and contextual surround(s) BIBREF20.
As such, a dataset of decontextualised documents with labelled sentences, devoid of authorial or publisher metadata, has taken us at some remove from even a simple everyday definition of propaganda. Our models for this shared task cannot easily incorporate information about the addresser or addressee; are left to assume a shared denotational code between author and reader (one perhaps simulated with the use of pre-trained word embeddings); and they are unaware of when or where the act(s) of propagandistic communication took place. This slipperiness is illustrated in our example document (Fig. FIGREF13): note that while Sentences 3 and 7, labelled as propaganda, reflect a propagandistic attitude on the part of the journalist and/or publisher, Sentence 4—also labelled as propaganda in the training data—instead reflects a “flag-waving" propagandistic attitude on the part of U.S. congressman Jeff Flake, via the conventions of reported speech BIBREF21. While reported speech often is signaled by specific morphosyntactic patterns (e.g. the use of double-quotes and “Flake said") BIBREF22, we argue that human readers routinely distinguish propagandistic reportage from the propagandastic speech acts of its subjects, and to conflate these categories in a propaganda detection corpus may contribute to the occurrence of false positives/negatives.
Most of the existing works on propaganda detection focus on identifying propaganda at the news article level, or even at the news outlet level with the assumption that each of the articles of the suspected propagandistic outlet are propaganda (Rashkin etal., 2017; Barron-Cedeno etal., 2019). Here we study two tasks that are more fine-grained, specifically propaganda detection at the sentence and phrase (fragment) levels (Da San Martino et al., 2019). This fine-grained setup aims to train models that identify linguistic propaganda techniques rather than distinguishing between the article source styles. Da San Martino et al. (2019) were the first to propose this problem setup and release it as a shared task. Along with the released dataset, Da San Martino et al. (2019) proposed a multi-granularity neural network, which uses the deep bidirectional transformer architecture known as BERT, which features pre-trained contextdependent embeddings (Devlin et al., 2018). Their system takes a joint learning approach to the sentence- and phrase-level tasks, concatenating the output representation of the less granular (sentence-level) task with the more fine-grained task using learned weights.
Answer: Previously most of the existing works on propaganda detection focus on identifying propaganda at the news article level, or even at the news outlet level with the assumption that each of the articles of the suspected propagandistic outlet are propaganda (Rashkin etal., 2017; Barron-Cedeno etal., 2019). Later two tasks that are more fine-grained came up, at the sentence and phrase (fragment) levels (Da San Martino et al., 2019). Also Da San Martino et al. (2019) were the first to propose this problem setup and release it as a shared task.