论文标题
抑郁，滥用毒品或信息丰富：在Covid-19爆发期间新闻暴露的知识感知研究
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak
论文作者
论文摘要
19009年大流行对世界各地人民的生活产生了严重的不利影响。 Covid-19加剧了社区范围内的抑郁症，并导致因锁定而孤立的个体造成的毒品滥用增加。此外，除了向公众提供信息内容外，在新闻广播方面，媒体对Covid-19危机的不断报道，发表的文章以及社交媒体上的信息共享对压力水平（进一步提高抑郁症和药物使用）的滚雪球影响，这是由于未确定的未来而产生的。在该职位上，我们提出了一个新的框架，用于评估抑郁症，药物滥用和抑郁症的时空性进展以及美国不同州基础新闻内容的信息。我们的框架采用了基于注意力的转移学习技术，将在社交媒体领域学习的知识应用于媒体曝光的目标领域。为了从网络上流媒体新闻内容中提取与COVID-19通信相关的新闻文章，我们使用神经语义解析和背景知识基础，以一系列称为语义过滤的步骤。我们对来自变形金刚（BERT）模型的双向编码器表示的三种变体实现了有希望的初步结果。我们将我们的发现与美国心理健康的报告进行了比较，结果表明，我们的微调BERT模型的表现要比Vanilla Bert更好。我们的研究可以通过提供有关Covid-19及其区域影响的可行见解，从而使流行病学家受益。此外，我们的解决方案可以基于用户的情感语调，以抑制性，滥用药物和信息性的规模衡量，以将新闻量身定制为最终用户应用程序。
The COVID-19 pandemic is having a serious adverse impact on the lives of people across the world. COVID-19 has exacerbated community-wide depression, and has led to increased drug abuse brought about by isolation of individuals as a result of lockdown. Further, apart from providing informative content to the public, the incessant media coverage of COVID-19 crisis in terms of news broadcasts, published articles and sharing of information on social media have had the undesired snowballing effect on stress levels (further elevating depression and drug use) due to uncertain future. In this position paper, we propose a novel framework for assessing the spatio-temporal-thematic progression of depression, drug abuse, and informativeness of the underlying news content across the different states in the United States. Our framework employs an attention-based transfer learning technique to apply knowledge learned on a social media domain to a target domain of media exposure. To extract news articles that are related to COVID-19 communications from the streaming news content on the web, we use neural semantic parsing, and background knowledge bases in a sequence of steps called semantic filtering. We achieve promising preliminary results on three variations of Bidirectional Encoder Representations from Transformers (BERT) model. We compare our findings against a report from Mental Health America and the results show that our fine-tuned BERT models perform better than vanilla BERT. Our study can benefit epidemiologists by offering actionable insights on COVID-19 and its regional impact. Further, our solution can be integrated into end-user applications to tailor news for users based on their emotional tone measured on the scale of depressiveness, drug abusiveness, and informativeness.
