A Novel Stance based Sampling for Imbalanced Data
A Novel Stance based Sampling for Imbalanced Data
复制标题
一种新的基于立场的不平衡数据采样
DOI：
10.14569/ijacsa.2022.0130157
复制
发表时间：
2022
影响因子：
0.9
通讯作者：
Sahil Bondre
中科院分区：
文献类型：
--
作者：
I. Agarwal;Dipti P Rana;Aemie Jariwala;Sahil Bondre
当世界正遭受冠状病毒大流行（COVID-19）的困扰时，与 Infodemic 的平行斗争也在发生，网上假新闻的扩散也在发生。在新冠肺炎 (COVID-19) 全球大流行期间，假新闻的传播会带来危险的后果。这是这项研究背后的驱动力。依赖从互联网或社交媒体获得的不正确信息可能是致命的。根据世界卫生组织的一项调查，在此期间至少有 800 人因 COVID-19 错误信息而丧生，凸显了假新闻的准确自动分类。然而，用于分类的数据是不平衡的。互联网拥有大量真实的医疗保健新闻，而有关 COVID-19 医疗保健的假新闻并不丰富。这种不平衡会导致错误的分类。本文研究了文本采样的替代方法。在本文中，我们提出了一种基于立场的抽样方法来平衡新闻数据。利用新闻标题和内容之间的差异来选择性地采样数据点并纠正不平衡。主要发现是，所提出的基于立场的抽样策略在不同程度的不平衡下一致地提高了分类任务的性能。所提出的技术可以更好地检测医疗保健领域的误导性新闻。 © 2022，国际高级计算机科学与应用杂志。版权所有。
While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place. The spread of fake news during this global pandemic COVID-19 has dangerous consequences. This is the driving force behind this study. Relying on incorrect information obtained from the internet or social media can be fatal. According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news. However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant. This imbalance leads to incorrect classification. The paper studies alternative approaches to text sampling. In this paper, we propose a stance based sampling method for balancing news data. The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance. The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance. The proposed techniques can better detect misleading news in the health care sector. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.
CoAID：COVID-19 医疗保健错误信息数据集
DOI：
--
发表时间：
2020
期刊：
arXiv
影响因子：
--
作者：
Cui, Limeng;Lee, Dongwon
通讯作者：
Lee, Dongwon
使用双向条件编码进行姿态检测
DOI：
10.48550/arxiv.1606.05464
发表时间：
2016
期刊：
arXiv e-prints
影响因子：
--
作者：
Augenstein Isabelle
通讯作者：
Augenstein Isabelle