ABSTRACT
The understanding of contemporary economic geographies and global "social facts" represent the best challenge for Burawoy's public sociology. Scholars wonder whether sociology can successfully address our time's problems, keeping its spirit of service to the community alive. Indeed, recent political and economic events require formulating a new sociological imagination that is more creative, open, and accessible to the general public. In this paper, we use some of the most significant intersections of Mills' work, between history and personal biography, to highlight the sociological imagination's significant role in understanding the present. We use practical cases of applying the concept of sociological imagination close to us, such as the recent COVID-19 pandemic.
ABSTRACT
In this paper we propose a deep learning model based on graph machine learning (i.e. Graph Attention Convolution) and a pretrained transformer language model (i.e. ELECTRA). Our model was developed to detect harmful tweets about COVID-19 and was used to tackle subtask 1C (harmful tweet detection) at the CheckThat!Lab shared task organized as part of CLEF 2022. In this binary classification task, our proposed model reaches a binary F1 score (positive class label, i.e. harmful tweet) of 0.28 on the test set. We demonstrate that our approach outperforms the official baseline by 8% and describe our model as well as the experimental setup and results in detail. We also refer to limitations of the approach and future research directions. © 2022 Copyright (c) 2022 for this paper by its authors.