Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments
Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments
复制标题
从病毒式传播的角度审视假新闻：情感、共鸣和情绪的相互作用
DOI：
--
复制
发表时间：
2022
影响因子：
--
通讯作者：
Shahid Mustafa
中科院分区：
文献类型：
--
作者：
Krishnadas Nanath;Supriya Kaitheri;S. Malik;Shahid Mustafa
目的
本文旨在从病毒式传播理论的视角考察影响假新闻预测的因素。本文着眼于情感驱动的内容，情感共鸣，主题建模和新闻文章的语言特征的混合，以预测假新闻的概率。
设计/方法/方式
我们选择了一个包含12,000多篇文章的数据集来开发一个假新闻检测模型。机器学习算法和自然语言处理技术被用来高效地处理大数据。基于词汇的情感分析提供了文章中使用的八种情感。使用主题建模（五个主题）提取主题集群，而情感分析提供了标题和文本之间的共鸣。将语言特征添加到编码结果中，以开发用于测试显著变量的逻辑回归预测模型。其他机器学习算法也被执行和比较。
结果
结果显示，文本中的积极情绪降低了新闻是假的概率。调查还发现，非法活动和犯罪相关内容等耸人听闻的内容与假新闻有关。新闻标题和表现出类似情绪的文字被发现是假的可能性较低。研究发现，字数较多的新闻标题和字数较少的内容对假新闻检测有显著影响。
实际影响
如今，一些系统和社交媒体平台正在尝试实施假新闻检测方法来过滤内容。这项研究从病毒理论的角度提供了令人兴奋的参数，可以帮助开发自动化的假新闻检测器。
独创性/价值
虽然有几项研究探讨了假新闻检测，但这项研究使用了病毒理论的新视角。它还引入了新的参数，如情感共鸣，可以帮助预测假新闻。本研究处理广泛的数据集，并使用高级自然语言处理来自动化开发预测模型的编码技术。
Purpose
The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.
Design/methodology/approach
A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.
Findings
The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.
Practical implications
Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.
Originality/value
While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.
迈向自动假新闻检测：新闻文章中的跨级别立场检测
DOI：
--
发表时间：
2018
期刊：
--
影响因子：
--
作者：
Conforti C
通讯作者：
Conforti C
影响因子：
3.2
作者：
Molina, Maria D.;Sundar, S. Shyam;Lee, Dongwon
通讯作者：
Lee, Dongwon