RESUMEN
Memes can sway people's opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the 'hero', the 'villain', and the 'victim' in the meme, if any. We utilize HVVMemes - a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4% over the best baseline and 1% over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes. © 2023 Association for Computational Linguistics.
RESUMEN
We present the findings of the shared task at the CONSTRAINT 2022 workshop on "Hero, Villain, and Victim: Dissecting Harmful Memes for Semantic Role Labeling of Entities." The task aims to delve deeper into meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7,000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or other. The shared task attracted 105 registered participants, but eventually only nine of them made official submissions. The most successful systems used ensembles combining textual and multimodal models, with the best system achieving an F1-score of 58.67.
RESUMEN
A wide array of sustainability definitions is available in literature but insufficiently support defining the sustainability of startups. To overcome this gap, the study synthesized the concept of sustainability from various angles. It identified constructs based on which 13 experts from academia and industry were interviewed, asking for their opinion on the concept. The respondents were approached through LinkedIn and mail for personal interviews. A semi-structured questionnaire was used as the research instrument, and interviews were held virtually. The interviews were transcribed further for thematic content analysis. The findings of the study suggested that the definition of sustainability in the context of a startup can be constructed on the following themes: value creation along with wealth creation, innovative business model, economic and social gains, ability to survive on own funds, resilience, consistent coverage of the cost of operations, overheads & on-costs, spatial and temporal extent. The paper is an original contribution that defines the path for future research in the area to explore factors affecting a startup's sustainability to reduce startups' high mortality rate. © 2022, Associated Management Consultants Pvt. Ltd. All rights reserved.