在社交媒体上使用深度学习对谣言检测和预防的调查
Barsha Pattanaik1, Sourav Mandal1, Rudra M Tripathy1
1School of Computer Science and Engineering, XIM University, Bhubaneswar, Odisha 752050 India.
概括
这项调查考察了深度学习技术,以打击在线虚假新闻和谣言. 它强调了当前的模型,数据集,并确定了开发有效的谣言控制策略的差距.
科学领域:
- 人工智能的人工智能
- 社交媒体分析 社交媒体分析
- 信息科学 信息科学 信息科学
背景情况:
- 数字时代看到错误信息的广泛传播,包括假新闻和谣言,通过社交媒体平台.
- 虚假信息的传播对社会信任,个人声誉和国家合法性构成重大风险.
- 有效的谣言控制是缓解在线错误信息有害影响的关键数字优先事项.
研究的目的:
- 进行对基于深度学习的最新技术进行全面调查,以控制谣言 (检测和预防).
- 确定当前的谣言检测,跟踪和打击工作中的关键区别,研究差距和挑战.
- 对标准数据集的深度学习模型的有效性进行批判性评估,并探索谣言真实性和立场分类.
主要方法:
- 对基于深度学习的谣言控制研究进行系统的文献综述.
- 分析和比较各种深度学习模型用于谣言检测和预防.
- 使用标准社交媒体数据集对模型有效性的评估.
- 探索相关方法:谣言真实性分类,立场分类,追踪和打击.
主要成果:
- 突出了用于社交媒体谣言检测的尖端深度学习模型.
- 在最近的数据集上批判性地评估这些模型的性能.
- 总结了相关的数据集,并提供了详细的信息和分析,用于未来的研究.
结论:
- 确定了谣言控制领域的重大研究差距和挑战.
- 强调需要开发早期和有效的谣言检测和预防方法.
- 为未来的研究方向提供了基础,以打击在线虚假信息.
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