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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (8): 1-14    DOI: 10.11925/infotech.2096-3467.2020.0454
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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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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     
Received: 21 March 2020      Published: 09 June 2020
ZTFLH:  TP393  
Corresponding Authors: Li Chenliang     E-mail: cllee@whu.edu.cn

Cite this article:

Liu Qian, Li Chenliang. A Survey of Topic Evolution on Social Media. Data Analysis and Knowledge Discovery, 2020, 4(8): 1-14.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0454     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I8/1

Evolution Model Based on Seven Evolution Events
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
Summary of Methods Based on Probabilistic Topic Model
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
Summary of Methods Based on Non-Probabilistic Topic Model
Steps of Timeline and Storyline Generation
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
Summary of Methods for Predetermined Events
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
Summary of Methods for User Queries
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
Summary of Methods for Predicting the Trend of Topic Evolution
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