当前位置:
X-MOL 学术
›
IEEE Internet Comput.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
When the Bad Is Good and the Good Is Bad: Understanding Cyber Social Health Through Online Behavioral Change
IEEE Internet Computing ( IF 3.2 ) Pub Date : 2021-04-30 , DOI: 10.1109/mic.2021.3059262 Ugur Kursuncu 1 , Hemant Purohit 2 , Nitin Agarwal 3 , Ugur Kursuncu 4
IEEE Internet Computing ( IF 3.2 ) Pub Date : 2021-04-30 , DOI: 10.1109/mic.2021.3059262 Ugur Kursuncu 1 , Hemant Purohit 2 , Nitin Agarwal 3 , Ugur Kursuncu 4
Affiliation
Following the Special Issue on Cyber Social Health: Part 1 in the January/February 2021 issue, in this issue, we highlight another five papers that were accepted based on the quality of the analysis, results, and presentation. In “Towards Hate Speech Detection at Large via Deep Generative Modeling,” the authors developed an approach to improve supervised hate speech detection on social media by creating a large dataset of hate speech from a small seed set. They introduced a big ground truth dataset and assessed the generalizability of models to the variability in communications with hate speech. This work attempts to overcome the lack of diversity and improve coverage in the input dataset, and the data imbalance. The authors employ GPT-2 fine-tuned on the existing labeled datasets, to generate a larger diverse hate speech dataset. They also perform a comparative analysis on the inductive biases of DL methods during training on individual hate-speech datasets.
中文翻译:
当坏是好,好是坏:通过在线行为改变了解网络社会健康
继《网络社会健康专刊:2021年1月/ 2月》第1部分之后,本期我们重点介绍了另外五篇基于分析,结果和陈述质量而被接受的论文。在“通过深度生成模型实现仇恨语音检测的大范围”中,作者开发了一种方法,该方法可以通过从一个小的种子集中创建一个庞大的仇恨语音数据集来改进社交媒体上的监督式仇恨语音检测。他们引入了一个庞大的地面真相数据集,并评估了模型与仇恨言论交流中变异性的普遍性。这项工作试图克服多样性的不足,并提高输入数据集中的覆盖率和数据不平衡性。作者在现有的标记数据集上使用经过微调的GPT-2,以生成更大的多样化仇恨语音数据集。
更新日期:2021-05-04
中文翻译:
当坏是好,好是坏:通过在线行为改变了解网络社会健康
继《网络社会健康专刊:2021年1月/ 2月》第1部分之后,本期我们重点介绍了另外五篇基于分析,结果和陈述质量而被接受的论文。在“通过深度生成模型实现仇恨语音检测的大范围”中,作者开发了一种方法,该方法可以通过从一个小的种子集中创建一个庞大的仇恨语音数据集来改进社交媒体上的监督式仇恨语音检测。他们引入了一个庞大的地面真相数据集,并评估了模型与仇恨言论交流中变异性的普遍性。这项工作试图克服多样性的不足,并提高输入数据集中的覆盖率和数据不平衡性。作者在现有的标记数据集上使用经过微调的GPT-2,以生成更大的多样化仇恨语音数据集。