Bhardwaj Akashdeep, Bharany Salil, Kim SeongKi
School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Heliyon. 2024 Aug 10;10(16):e36049. doi: 10.1016/j.heliyon.2024.e36049. eCollection 2024 Aug 30.
Social networking platforms have become one of the most engaging portals on the Internet, enabling global users to express views, share news and campaigns, or simply exchange information. Yet there is an increasing number of fake and spam profiles spreading and disseminating fake information. There have been several conscious attempts to determine and distinguish genuine news from fake campaigns, which spread malicious disinformation among social network users. Manual verification of the huge volume of posts and news disseminated via social media is not feasible and humanly impossible. To overcome the issue, this research presents a framework to use sentiment analysis based on emotions to investigate news, posts, and opinions on social media. The proposed model computes the sentiment score of content-based entities to detect fake or spam and detect Bot accounts. The authors also present an investigation of fake news campaigns and their impact using a machine learning algorithm with highly accurate results as compared to other similar methods. The results presented an accuracy of 99.68 %, which is significantly higher as compared to other methodologies delivering lower accuracy.
社交网络平台已成为互联网上最具吸引力的门户之一，使全球用户能够表达观点、分享新闻和活动，或仅仅是交流信息。然而，越来越多的虚假和垃圾账号在传播和散布虚假信息。人们已经进行了几次有意识的尝试，以确定并区分真实新闻与虚假活动，这些虚假活动在社交网络用户中传播恶意虚假信息。通过社交媒体传播的海量帖子和新闻进行人工验证是不可行的，也是人力无法做到的。为了克服这个问题，本研究提出了一个框架，利用基于情感的情感分析来调查社交媒体上的新闻、帖子和观点。所提出的模型计算基于内容的实体的情感得分，以检测虚假或垃圾信息，并检测机器人账号。作者还使用一种机器学习算法对虚假新闻活动及其影响进行了调查，与其他类似方法相比，该算法具有高度准确的结果。结果显示准确率为99.68%，与其他准确率较低的方法相比，这一准确率要高得多。
