Evaluation of Semantic Change of Harm-Related Concepts in Psychology
The paper focuses on diachronic evaluation of semantic changes of harm-related concepts in psychology. More specifically, we investigate a hypothesis that certain concepts such as {`}addiction{''}, {}bullying{''}, {}harassment{''}, {}prejudice{''}, and {}trauma{''} became broader during the last four decades. We evaluate semantic changes using two models: an LSA-based model from Sagi et al. (2009) and a diachronic adaptation of word2vec from Hamilton et al. (2016), that are trained on a large corpus of journal abstracts covering the period of 1980{--} 2019. Several concepts showed evidence of broadening. {}Addiction{''} moved from physiological dependency on a substance to include psychological dependency on gaming and the Internet. Similarly, {}harassment{''} and {}trauma{''} shifted towards more psychological meanings. On the other hand, {`}bullying{''} has transformed into a more victim-related concept and expanded to new areas such as workplaces.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments
Computational social science research has made advances in machine learning and natural language processing that support content moderators in detecting harmful content. These advances often rely on training datasets ann…
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation
Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A practical solution is to selectively remov…
SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation
Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe content. Existing unlearning/editing-bas…
DenoisingVideo GenerationCHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks
With the great success of networks, it witnesses the increasing demand for the interpretation of the internal network mechanism, especially for the net decision-making logic. To tackle the challenge, the Concept-harmoniz…
Decision MakingMass Concept Erasure in Diffusion Models with Concept Hierarchy
The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress specific concepts while preserving general ge…