Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes. (2022)

Abstract: Identifying good and evil through representations of victimhood, heroism, and villainy (i.e., role labeling of entities) has recently caught the research community’s interest. Because of the growing popularity of memes, the amount of offensive information published on the internet is expanding at an alarming rate. It generated a larger need to address this issue and analyze the memes for content moderation. Framing is used to show the entities engaged as heroes, villains, victims, or others so that readers may better anticipate and understand their attitudes and behaviors as characters. Positive phrases are used to characterize heroes, whereas negative terms depict victims and villains, and terms that tend to be neutral are mapped to others. In this paper, we propose two approaches to role label the entities of the meme as hero, villain, victim, or other through Named-Entity Recognition(NER), Sentiment Analysis, etc. With an F1-score of 23.855, our team secured eighth position in the Shared Task @ Constraint 2022.

## introduction the availability of smartphones and the internet has caught the interest of today's youth in social media. these applications provide a large platform for users to communicate with the outside world and share their thoughts and opinions. with these advantages comes a disadvantage: many people exploit the platform to spread offensive content on social media under the guise of freedom of expression @xcite . this incendiary material is usually directed towards a single person, a small group of people, a religious group, or a community. people create offensive content and aggressively spread it over social media @xcite . for many purposes, including commercial and political benefit, this type of information is created @xcite . this type of communication can dis-turb societal harmony and spark riots. it also has the ability to have a negative psychological impact on readers. it has the potential to harm people's emotions and behavior @xcite . as a result, identifying such content is crucial. further, researchers, politicians, and investors are working to build a reliable method for dissecting the dangerous memes present over the internet. framing allows a communication source to portray and describe a problem within a "field of meaning" by employing conventional narrative patterns and cultural references @xcite . by connecting with readers' existing knowledge, cultural narratives, and moral standards, framing helps to construct events @xcite . it can portray the characters in a story as heroes, villains, or victims, making it easier for the audience to anticipate and comprehend their attitudes, beliefs, decisions, and actions. narrative frames can be found in various media, including memes, films, literature, and the news. narrators use emotionality to plainly distinguish between good and evil through vivid descriptions of victimization, heroism, and villainy, which is a major feature of the popular storytelling culture @xcite . positive adjectives are used to portray heroes, whereas negative terms depict victims and villains. in popular culture, heroes represent bravery, great accomplishments, or other noble attributes, whereas villains represent malicious intents, conspiring, and other undesirable characteristics @xcite . to summarise, narrative frames are essential for understanding new situations in terms of prior ones and therefore making sense of the causes, events, and consequences. the standard method for detecting frames of the narrative is by examining the semantic relationships between the various elements in the meme about the events it portrays. understanding the events in a narrative and the roles that the entities in that meme play in those events, on the other hand, is a complex, tough, and computationally expensive task. thus, rather than determining all of the specific events and event types described in the meme, as well as the semantic relationships among the entities involved in those events in great detail, we propose methodologies in which the entities are analyzed at a much higher level of abstraction, specifically in terms of whether they hold the qualities of heroes, victims, villains, or none as conveyed by the terms used to characterize them. as a result, we arrive at a rather basic realization. the terms nearest to each entity are evaluated for their sentiment polarity or closeness to associated terms with heroes, villains, or victims. ## literature review the topic of entity role detection from narrative has recently piqued the interest of several corporate and academic researchers in recent times. however, there were just a few efforts to extract knowledge and present it from newspaper articles that especially utilized the newspaper article bodies to derive meaning, focusing on the headline @xcite @xcite . but there have been hardly any attempts to identify the entities that had been exalted, demonized, or victimized @xcite . instead, studies were conducted to see how satire delivered through the means of internet memes affects brand image (christopher kontio). however, no existing approach has been able to handle harmful content identification in multimodal data employing the role labeling notion. in this paper, the emphasis is on detecting which entities are vilified, glorified or victimized in a meme by assuming the frame of reference from the meme author's perspective @xcite . given a meme and an entity, the task is to determine the role of each entity detected in the meme as hero or villain or victim or other. the constraint 1 https://constraint-lcs2.github.io/ here is that the meme has to be analyzed from the perspective of the author of the meme @xcite . ## methodology this study has proposed two submissions based on two different methods. in the first method, we perform entity recognition then sentiment analysis. in the second method, we perform entity recognition and then use wu-palmer similarity (s. bird, 2009) to calculate similarity scores of entities with each of the roles, i.e., hero, villain, victim, and other. ## results in the competition, teams were ranked based on macro f1-score across all the classes. the suggested method and model secured the eighth position in the competition for the task of dissecting harmful memes for semantic role-labeling of entities. the model performs well in the role labeling task. however, in some cases, the model under per-sl. no username / team name f1 score forms in identifying the categories due to the difficulty in capturing some of the attributes or traits related to the roles. as a result, the overall systems' macro f1-score has been low at 23.855. in addition, the ensembling of multiple nlp sub-tasks also have contributed to the decrease of the f1-score of the system. the systems' performance can be further improved by modeling those nlp sub-tasks in the proposed methods using better parameters which could potentially increase the score. the current system implementations use nlp techniques such as entity recognition, sentiment analysis, and word sets and dictionaries, all of which have shown promising results in the role labeling task. across all classes, the existing system implementation produced a good f1 score. however, as the model is based on simple proximity measures, it has issues when dealing with ocr text that contains composite grammatical structures such as indirect speech, passive voice etc. in this experiment, the n-words window size used for data processing is @xmath0. as a result, there is potential