• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert systems with applications >A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks
【24h】

A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks

机译：Twitter研究调查：数据模型，图形结构，情感分析和攻击

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Twitter is the third most popular worldwide Online Social Network (OSN) after Facebook and Instagram. Compared to other OSNs, it has a simple data model and a straightforward data access API. This makes it ideal for social network studies attempting to analyze the patterns of online behavior, the structure of the social graph, the sentiment towards various entities and the nature of malicious attacks in a vivid network with hundreds of millions of users. Indeed, Twitter has been established as a major research platform, utilized in more than ten thousands research articles over the last ten years. Although there are excellent review and comparison studies for most of the research that utilizes Twitter, there are limited efforts to map this research terrain as a whole. Here we present an effort to map the current research topics in Twitter focusing on three major areas: the structure and properties of the social graph, sentiment analysis and threats such as spam, bots, fake news and hate speech. We also present Twitter's basic data model and best practices for sampling and data access. This survey also lays the ground of computational techniques used in these areas such as Graph Sampling, Natural Language Processing and Machine Learning. Along with existing reviews and comparison studies, we also discuss the key findings and the state of the art in these methods. Overall, we hope that this survey will help researchers create a clear conceptual model of Twitter and act as a guide to expand further the topics presented.
机译：Twitter是Facebook和Instagram之后的第三个最受欢迎的全球在线社交网络（OSN）。与其他OSN相比，它具有简单的数据模型和简单的数据访问API。这使得它成为企图分析在线行为的模式，社会图的结构，对各个实体的情绪以及数亿位用户中生动网络中的恶意攻击性质的理想选择。实际上，Twitter已被确定为一个主要的研究平台，在过去十年中使用了超过十万的研究文章。虽然对于利用Twitter的大多数研究具有很好的审查和比较研究，但努力将此研究的地形映射到整个研究。在这里，我们努力绘制Twitter上的目前的研究主题，重点关注三个主要领域：社会图，情感分析和威胁等的结构和属性，如垃圾邮件，机器人，假新闻和仇恨言论。我们还提供了Twitter的基本数据模型和用于采样和数据访问的最佳实践。该调查还将这些领域的计算技术奠定了基础，例如图形采样，自然语言处理和机器学习。随着现有审查和比较研究，我们还在这些方法中讨论了主要发现和本领域的状态。总的来说，我们希望本调查有助于研究人员创建一个明确的Twitter概念模型，并作为扩展进一步提出的主题的指南。

著录项

  • 来源
    《Expert systems with applications》 |2021年第2期|114006.1-114006.25|共25页
  • 作者

    Despoina Antonakaki; Paraskevi Fragopoulou; Sotiris Ioannidis;

  • 作者单位

    Institute of Computer Science (ICS) of the Foundation for Research and Technology - Hellas (FORTH) N. Plastira 100 Vassilika Vouton GR-700 13 Heraklion Crete EL 090101655 Greece;

    Institute of Computer Science (ICS) of the Foundation for Research and Technology - Hellas (FORTH) N. Plastira 100 Vassilika Vouton GR-700 13 Heraklion Crete EL 090101655 Greece;

    Technical University of Crete University Campus Akrotiri Chania 73100 Crete EL 090034024 Greece Institute of Computer Science (ICS) of the Foundation for Research and Technology - Hellas (FORTH) N. Plastira 100 Vassilika Vouton GR-700 13 Heraklion Crete EL 090101655 Greece;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Social networks; Twitter; Survey; Social graph; Sentiment analysis; Spam; Bots; Fake news; Hate speech;

    机译：社交网络;推特;民意调查;社会图;情绪分析;垃圾邮件;机器人;假新闻;仇恨言论;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号