通过IPS、MF、NCF和BPR的比较了解假新闻在社交媒体上的传播

Haotong Xin, Yimin Wei, Tianda Fan, Shang Peng, Haohua Liu, Junxiang Su
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引用次数: 0

摘要

近年来,假新闻检测领域有了长足的发展。假新闻的传播影响了人们的日常生活,降低了真实新闻的价值,有时还会损害人们的形象。这一现象引起了我们的注意,所以我们有兴趣做出一些努力来减少它的负面影响。我们关注的是,如果所有的新闻都被用户阅读,用户是否会传播新闻(从因果推理的角度)。我们使用负抽样来避免现实数据集中只有正反馈的问题。然后对IPS、MF、NCF和BPR进行比较,找出最能帮助我们解决这个问题的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding of Fake News Dissemination on Social Media by Comparing IPS, MF, NCF and BPR
In these years, there are dramatic development in the fake news detection field. The spread of fake news influences people's daily life, reduces the value of real news, and sometimes blemishes people's images. This phenomenon has raised our attention, so we are interested in making some effort to reduce the negative impact of it. We focus on the point that whether the users will spread the news if all the news is read by the users (from the causal inference aspect). We use negative sampling to avoid the problem that there is only positive feedback in the real-world dataset. Then we compare IPS, MF, NCF and BPR to discover the best to help us to solve this question.
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