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数据分析与知识发现  2020, Vol. 4 Issue (8): 1-14     https://doi.org/10.11925/infotech.2096-3467.2020.0454
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基于社交媒体的话题演变研究综述*
刘倩,李晨亮()
武汉大学国家网络安全学院 武汉 430075;中国电子科技集团公司航天信息应用技术重点实验室 石家庄 050081
A Survey of Topic Evolution on Social Media
Liu Qian,Li Chenliang()
School of Cyber Science and Engineering, Wuhan University, Wuhan 430075, China)(CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China
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摘要 

【目的】对近年来基于社交媒体的话题演变研究进行分析和总结,介绍相关分析技术。【文献范围】使用关键词"Social"和"Topic Evolution"在DBLP和Semantic Scholar搜集相关文献,并使用关键词"话题演变"在CNKI 数据库进行搜集,最后利用引用网络进行补充,经过筛选一共引用83篇文献。【方法】根据研究对象以及话题提取的方法对话题演变技术进行分析评述。【结果】将话题演变技术分为两个大类,6个小类,并对话题未来演变趋势进行预测分析。【局限】未对算法引入时间的方式进行详细对比分析。【结论】本文对社交媒体中的话题演变的技术进行分析总结,并发现该研究面临的挑战和未来的方向。

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刘倩
李晨亮
关键词 社交媒体话题演变趋势预测    
Abstract

[Objective] This paper analyzes and summarizes recent researches about topic evolution on social media, and mainly introduces the relevant analysis techniques. [Coverage] Relevant literatures were collected in DBLP, Semantic Scholar and CNKI with the use of keywords "Social" and "Topic Evolution". Finally, a total of 83 representative literatures were cited. [Methods] According to the research objects and the methods of topic extraction, the topic evolution techniques are analyzed. [Results] The techniques are divided into two categories and six subcategories, and the prediction of the topic’s trend is analyzed. [Limitations] We didn’t discuss the detailed comparative analysis of the way these techniques introduce time. [Conclusions] This paper analyzed and summarized the techniques of topic evolution on social media, and found the challenges and future directions of this research.

Key wordsSocial Media    Topic Evolution    Trend Prediction
收稿日期: 2020-03-21      出版日期: 2020-06-09
ZTFLH:  TP393  
基金资助:*本文系国家自然科学基金项目"基于深度学习的零样本和小样本文本过滤技术研究"(61872278);中国电子科技集团公司航天信息应用技术重点实验室开放基金项目的研究成果之一(SXX18629T022)
通讯作者: 李晨亮     E-mail: cllee@whu.edu.cn
引用本文:   
刘倩, 李晨亮. 基于社交媒体的话题演变研究综述*[J]. 数据分析与知识发现, 2020, 4(8): 1-14.
Liu Qian, Li Chenliang. A Survey of Topic Evolution on Social Media. Data Analysis and Knowledge Discovery, 2020, 4(8): 1-14.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0454      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I8/1
Fig.1  基于7种演变事件的演变模型
方法 引入时间方式 话题
数目
演变类型 演变
结构
演变事件
Wang等[18] 按文本顺序 固定 强度 线性 三种
Sasaki等[19] 先时间离散化 固定 强度和内容 线性
Liang等[20] 流式文本 固定 内容 线性
Alam等[21] 对时间建模 固定 强度 线性
Huang等[23] 先时间离散化 固定 内容 线性 三种
Abulaish等[12] 先时间离散化 固定 强度和内容 非线性 5种
Zhang等[28] 先时间离散化 不固定 内容 线性
Lu等[30] 先时间离散化 不固定 内容 线性
Table 1  基于概率主题模型的方法总结
类型 方法 引入时间方式 话题数目 演变类型 演变结构 演变事件
基于非负矩阵分解 Saha等[32] 先时间离散化 不固定 强度和内容 线性 两种
Chen等[33] 先时间离散化 不固定 强度和内容 线性 三种
Bahargam等[34] 对时间建模 固定 强度 线性
Kalyanam等[36] 先时间离散化 固定 强度和内容 线性
Zhang等[10] 先时间离散化 不固定 内容 线性 三种
基于社区发现 Lu等[39] 后时间离散化 不固定 内容 线性
Liu等[41] 后时间离散化 不固定 内容 非线性 4种
Fedoryszak等[43] 先时间离散化 不固定 强度和内容 线性
Hashimoto等[44] 先时间离散化 固定 强度和内容 线性 两种
基于增量聚类 Cai等[49] 流式文本 不固定 强度 非线性 4种
Ozdikis等[53] 先时间离散化 不固定 强度 线性
Comito等[54] 流式文本 不固定 强度 线性
Table 2  基于非概率主题模型的方法总结
Fig.2  时间线和故事脉络的生成步骤
类型 方法 生成摘要方式 生成演变结构方式
时间线 Zhou等[56] 按关键词和时间排序 时间顺序
Wang等[58] 按体现子话题变化的程度排序 时间顺序
Chang等[59] 构造特征进行排序 时间顺序
故事
脉络
Dehghani等[8] 利用HITS算法和WMDS算法 最小生成树算法
Sun等[63] 寻找支配集 斯坦纳树算法
Guo等[64] 寻找支配集和最大化次模函数,同时能够生成图片摘要 利用内容相似度和时间相近度判断文本之间的关系
Ansah等[65] 用社区、词分布以及时间戳表示一个子事件 利用社区相似度、时间相近度以及话题的一致性判断文本之间的关系
Goyal等[66] 利用基于LSTM的编码器-解码器模型生成 使用合并的方式生成层次的结构
Table 3  针对预定事件的研究方法总结
方法 提高查询效果的方式 生成演变
结构方式
演变
结构
Lin等[67] 利用动态伪相关反馈进行查询扩展 利用斯坦纳树算法 非线性
Endo等[68] 利用伪相关反馈进行查询扩展 按时间顺序 线性
Zhao等[69] 利用社交关系寻找与查询词相关的词进行查询扩展 按时间顺序 线性
Tonon等[70] 联合外部知识库在知识图谱上进行SPARQL查询从而实现查询扩展 按时间顺序 线性
Bhardwa等[71] 利用词嵌入和时间上的词共现关系实现查询扩展 按时间顺序 线性
Brigadir等[72] 计算文本里所有词汇的向量表示,然后利用查询词与文本之间的相似度进行检索,省去了查询扩展 按时间顺序 线性
Table 4  针对用户查询的方法总结
方法 预测方式 预测对象 预测内容
Lu等[73] 利用异同移动平均线技术计算动量 潜在话题 上升和下降趋势
Liu等[74] 构造特征然后使用概率模型分类、估计参数 潜在话题 是否会热门以及到达热门的时间
Ma等[75] 构造特征然后使用概率模型分类 事件 是否会热门
Wang等[76] 利用高斯混合分布计算再次热门的概率 潜在话题 是否再次热门
Zhang等[77] 将流行度建模为文本、兴趣和历史流行度的线性函数 事件 流行度
Fang等[78] 使用Beta分布对流行度建模 标签话题 流行度
Wang等[79] 利用社区情感能量和话题的流行度之间的线性相关性进行预测 潜在话题 流行度
Wu等[80] 使用RNN预测流行度 事件 流行度
Chen等[81] 利用双向GRU、CNN和注意力机制对流行度的影响因素进行编码 事件 流行度
Huang等[82] 利用LSTM和CNN对流行度的影响因素编码 标签话题 活跃时间
Yu等[83] 使用LSTM和注意力机制对流行度的影响因素编码 标签话题 到达尖峰所需的时间
Table 5  话题演变趋势预测的研究方法总结
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