NL ytics FKIE at S em E val-2021 Task 6: Detection of Persuasion Techniques In Texts And Images (2021)

Abstract: The following system description presents our approach to the detection of persuasion techniques in texts and images. The given task has been framed as a multi-label classification problem with the different techniques serving as class labels. The multi-label classification problem is one in which a list of target variables such as our class labels is associated with every input chunk and assumes that a document can simultaneously and independently be assigned to multiple labels or classes. In order to assign class labels to the given memes, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) as a neural network architecture for token and sequence classification. Starting off with a pre-trained model for language representation we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps. To incorporate image features in the multi-modal setting, we rely on the pre-trained VGG-16 model architecture.

## introduction social networks provide opportunities to conduct disinformation campaigns for organizations as well as individual actors. the proliferation of disinformation online, has given rise to a lot of research on automatic fake news detection. semeval-2021 task 6 considers disinformation as a communication phenomenon. by detecting the use of various persuasion techniques in communication, it takes into account not only the content but also how a subject matter is communicated. the goal of the shared task is to build models for identifying such techniques in the textual content of a meme only (two subtasks) and in a multimodal setting in which both the textual and the visual content are to be analysed together (one subtask). the shared task defines the following subtasks: subtask 1 given only the "textual content" of a meme, identify which of the 20 techniques are used in it. this is a multilabel classification problem. subtask 2 given only the "textual content" of a meme, identify which of the 20 techniques are used in it together with the span(s) of text covered by each technique. this is a multilabel sequence tagging task. the task is the combination of the two subtasks of the semeval 2020 task 11 on "detecting propaganda techniques in news articles". note that subtask 1 is a simplified version of subtask 2 in which the spans covered by each technique is not supposed to be provided. subtask 3 given a meme, identify which of the 22 techniques are used both in the textual and visual content of the meme (multimodal task). this is a multilabel classification problem. in this work, we covered our approach on both technique classification (tc) tasks (subtask 1 and subtask 3) detecting the type of communication technique used in a given message. to build models, the first subtask assumes purely textual content as inputs, whereas the third is designed in multimodal setting in which both the textual and the visual content are to be analysed together. below, we describe the systems built for these two subtasks. at the core of our systems is roberta @xcite , a pre-trained model based on the transformer architecture @xcite . although we did not manage to participate in the second subtask, we will describe our solution below for the sake of completeness. finally, we will address some limitation of the general settings of this shared task. ## related work the goal of the shared task is to investigate automatic techniques for identifying various rhetorical and psychological techniques in online disinformation campaigns. a comprehensive survey on fake news has been presented by @xcite . based on the structure of data reflecting different aspects of communication, they identified four different perspectives on fake news: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. the shared task emphasizes communicative styles that systematically co-occur with persuasive intentions of (political) media actors. similar to de @xcite , propaganda and persuasion is considered as an expression of political communication content and style. hence, beyond the actual subject of communication, the way it is communicated is gaining importance. we build our work on top of this foundation by first investigating content-based approaches for information discovery and then open up our focus to dissemination mechanisms. traditional information discovery methods are based on content: documents, terms, and the relationships between them @xcite . they can be considered as a general information extraction (ie) methods, automatically deriving structured information from unstructured and/or semi-structured machine-readable documents. communities of researchers contributed various techniques from machine learning, information retrieval, and computational linguistics to the different aspects of the information extraction problem. from a computer science perspective, existing approaches can be roughly divided into the following categories: rulebased, supervised, and semi-supervised. in our case, we followed the supervised approach by reframing the complex language understanding task as a simple classification problem. text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. by using natural language processing (nlp), text classifiers can automatically analyze human language texts and then assign a set of predefined tags or categories based on their content. historically, the evolution of text classifiers can be divided into three stages: (1) simple lexiconor keyword-based classifiers, (2) classifiers using distributed semantics, and (3) deep learning classifiers with advanced linguistic features. ## dataset the dataset to this task is provided by @xcite . furthermore, there is a related shared task "semeval 2020 task 11 on detecting propaganda techniques in news articles" (martino et al., 2020) since it serves as the basis for the second sub-task. in particular, the second subtask is the combination of the two subtasks of the previous task. finally, there is a recent survey on computational propaganda detection by da san @xcite . ## our approach in this section, we provide a general overview of our approaches to the three subtasks. subtasks 1 and 3 are both given as multilabel classification problems, whereas subtaks 2 is given as a multilabel sequence tagging task. ## conclusion and future work we described our approach for the semeval-2021 task 6 on detection of persuasion techniques in text and images. we employed roberta-based neural architectures, additional crf layers, and a cross-modality framework for learning the connections between image and text in a multi-modal transformer architecture. in future work, we plan to investigate more recent neural architectures for language representation such as t5 @xcite and @xcite . in case of the multimodal setting, it might also be useful to evaluate alternative model architectures such as resnet @xcite to improve image representation. furthermore, we expect great opportunities for transfer learning from the areas such as argumentation mining @xcite and offensive language detection @xcite . to deal with data scarcity as a general challenge in natural language processing, we examine the application of concepts such as active learning, semi-supervised learning @xcite as well as weak supervision @xcite .