找到适合提问的社交媒体网站

Zhen Yang, Isaac Jones, Xia Hu, Huan Liu
{"title":"找到适合提问的社交媒体网站","authors":"Zhen Yang, Isaac Jones, Xia Hu, Huan Liu","doi":"10.1145/2808797.2809391","DOIUrl":null,"url":null,"abstract":"Social media has become a part of our daily life and we use it for many reasons. One of its uses is to get our questions answered. Given a multitude of social media sites, however, one immediate challenge is to pick the most relevant site for a question. This is a challenging problem because (1) questions are usually short, and (2) social media sites evolve. In this work, we propose to utilize topic specialization to find the most relevant social media site for a given question. In particular, semantic knowledge is considered for topic specialization as it can not only make a question more specific, but also dynamically represent the content of social sites, which relates a given question to a social media site. Thus, we propose to rank social media sites based on combined search engine query results. Our algorithm yields compelling results for providing a meaningful and consistent site recommendation. This work helps further understand the innate characteristics of major social media platforms for the design of social Q&A systems.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"333 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Finding the right social media site for questions\",\"authors\":\"Zhen Yang, Isaac Jones, Xia Hu, Huan Liu\",\"doi\":\"10.1145/2808797.2809391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media has become a part of our daily life and we use it for many reasons. One of its uses is to get our questions answered. Given a multitude of social media sites, however, one immediate challenge is to pick the most relevant site for a question. This is a challenging problem because (1) questions are usually short, and (2) social media sites evolve. In this work, we propose to utilize topic specialization to find the most relevant social media site for a given question. In particular, semantic knowledge is considered for topic specialization as it can not only make a question more specific, but also dynamically represent the content of social sites, which relates a given question to a social media site. Thus, we propose to rank social media sites based on combined search engine query results. Our algorithm yields compelling results for providing a meaningful and consistent site recommendation. This work helps further understand the innate characteristics of major social media platforms for the design of social Q&A systems.\",\"PeriodicalId\":371988,\"journal\":{\"name\":\"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)\",\"volume\":\"333 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2808797.2809391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2808797.2809391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

社交媒体已经成为我们日常生活的一部分,我们使用它的原因有很多。它的一个用途是让我们的问题得到回答。然而,考虑到众多的社交媒体网站,一个直接的挑战是为一个问题选择最相关的网站。这是一个具有挑战性的问题,因为(1)问题通常很短,(2)社交媒体网站在不断发展。在这项工作中,我们建议利用主题专业化来找到与给定问题最相关的社交媒体网站。其中,语义知识被考虑用于主题专门化,因为它不仅可以使问题更具体,还可以动态地表示社交网站的内容,将给定的问题与社交媒体网站联系起来。因此,我们建议基于组合搜索引擎查询结果对社交媒体网站进行排名。我们的算法产生令人信服的结果,提供有意义和一致的网站推荐。这项工作有助于进一步了解各大社交媒体平台的固有特征,从而设计社交问答系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finding the right social media site for questions
Social media has become a part of our daily life and we use it for many reasons. One of its uses is to get our questions answered. Given a multitude of social media sites, however, one immediate challenge is to pick the most relevant site for a question. This is a challenging problem because (1) questions are usually short, and (2) social media sites evolve. In this work, we propose to utilize topic specialization to find the most relevant social media site for a given question. In particular, semantic knowledge is considered for topic specialization as it can not only make a question more specific, but also dynamically represent the content of social sites, which relates a given question to a social media site. Thus, we propose to rank social media sites based on combined search engine query results. Our algorithm yields compelling results for providing a meaningful and consistent site recommendation. This work helps further understand the innate characteristics of major social media platforms for the design of social Q&A systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信