Chakrabarti Purnadip, Malvi Eish, Bansal Shubhi, Kumar Nagendra
Indian Institute of Technology Indore, Indore, 453552 India.
Soc Netw Anal Min. 2023;13(1):21. doi: 10.1007/s13278-023-01024-9. Epub 2023 Jan 11.
Social media has gained huge importance in our lives wherein there is an enormous demand of getting high social popularity. With the emergence of many social media platforms and an overload of information, attaining high popularity requires efficient usage of hashtags, which can increase the reachability of a post. However, with little awareness about using appropriate hashtags, it becomes the need of the hour to build an efficient system to recommend relevant hashtags which in turn can enhance the social popularity of a post. In this paper, we thus propose a novel method hashTag RecommendAtion for eNhancing Social popularITy to recommend context-relevant hashtags that enhance popularity. Our proposed method utilizes the trending nature of hashtags by using post keywords along with the popularity of users and posts. With the prevalent evaluation techniques of this field being quite unreliable and non-uniform, we have devised a novel evaluation algorithm that is more robust and reliable. The experimental results show that our proposed method significantly outperforms the current state-of-the-art methods.
社交媒体在我们的生活中变得极其重要，人们对获得高社交人气有着巨大需求。随着众多社交媒体平台的出现以及信息过载，要获得高人气就需要有效使用主题标签，这可以增加帖子的可达性。然而，由于对使用合适主题标签的认识不足，当下迫切需要构建一个高效系统来推荐相关主题标签，进而提高帖子的社交人气。因此，在本文中，我们提出了一种新颖的方法——用于增强社交人气的主题标签推荐（hashTag RecommendAtion for eNhancing Social popularITy），以推荐能提高人气的上下文相关主题标签。我们提出的方法通过使用帖子关键词以及用户和帖子的人气来利用主题标签的趋势特性。鉴于该领域普遍的评估技术相当不可靠且不统一，我们设计了一种更稳健、可靠的新颖评估算法。实验结果表明，我们提出的方法显著优于当前的最先进方法。
