A Survey of Research on Microblog Popularity Prediction

doi:10.11871/jfdc.issn.2096-742X.2023.02.010

Abstract

Abstract:

[Objective] This paper is to conduct a multi-angle survey of the existing research on microblog popularity prediction, discuss the shortcomings of the existing approaches, foresee the future development trend, and provide a reference for follow-on researches. [Coverage] The paper sorts out and summarizes relevant literatures both in China and abroad in recent five years. [Methods] The paper first introduces the definition of popularity prediction and popularity calculation methods. Then the research methods of popularity prediction are analyzed from three aspects: characteristics, time sequence, and user behavior. An extensive study is conducted on the key technologies of popularity prediction. Finally, the problems of the existing methods and the prospect are summarized. [Results] Feature-based popularity prediction methods are widely used because of they are well customized. The method combining deep learning and ensemble learning is becoming the mainstream approach. [Limitations] As the dataset of the individual research is not publicly available, this study cannot make a horizontal comparison of all algorithms for the level of improvement against a unified standard. [Conclusions] Microblog popularity prediction is significant for public opinion monitoring, commercial marketing, and content promotion, etc. In the era of ever-increasing popularity of social media, the research on popularity prediction will be further promoted.

LI Yan,HE Hongbo,WANG Runqiang. A Survey of Research on Microblog Popularity Prediction[J]. Frontiers of Data and Computing, 2023, 5(2): 119-135, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.02.010.

Figures/Tables 6

Table 1

Table 2

Table 3

Table 4

Commonly used evaluation index for popularity prediction- regression problem"

指标名称	英文简称	作用	公式
决定系数	R2	表示预测数据和真实数据之间拟合的程度	$1 - ∑ i = 1 m y i - y^i 2 ∑ i = 1 m y i - y - 2$
平均绝对误差	MAE	反映预测值误差的实际情况	$1 m ∑ i = 1 m y i - y^i$
平均绝对百分误差	MAPE	表示预测值和真实值之间平均偏差百分比	$1 m ∑ i = 1 m y i - y^i y i$
均方误差	MSE	用于评价数据的变化程度	$1 m ∑ i = 1 m y i - y^i 2$
均方根误差	RMSE	衡量预测值和真实值之间的偏差	$1 m ∑ i = 1 m y i - y^i 2$

Table 4

Table 5

Commonly used evaluation index for popularity prediction- classification problems"

指标名称	英文简称	作用	公式
准确率	Accuracy	表示正确预测的数量占总样本的百分比	$A = (T P + T N) * 100 % T P + T N + F P + F N$
精确率	Precision	表示被正确地划分为正例的样本与预测结果为正例的样本数的比值	$P = T P * 100 % T P + F P$
召回率	Recall	表示有多少正例被判断为正例	$R = T P * 100 % T P + F N$
F1值	F1 Score	表示精确率和召回率的加权调和平均	$F 1 = 2 * R * P R + P$
受试者工作特征曲线	ROC	以FP Rate为横轴，TP Rate为纵轴的曲线	$F P R = F P F P + T N$ $T P R = T P T P + F N$
ROC曲线下面积	AUC	ROC曲线对应的面积	$A U C = ∑ i = 1 p R a n k i - M M + 1 2 M * N$

Table 5

Table 6

References 84

[1]	肖显. 从抗击疫情看政务新媒体传播策略[J]. 青年记者, 2020, 80(30): 62-63.
[2]	Ji G, Zhu Y, Niu Y, et al. Classification and Evaluation for Microblog Popularity Prediction[C]. Journal of Phy-sics: Conference Series, IOP Publishing, 2021, 1883 (1): 012014.
[3]	Gao X, Cao Z, Li S, et al. Taxonomy and evaluation for microblog popularity prediction[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2019, 13(2): 1-40.
[4]	Nia Z M, Khayyambashi M R. Improving content pop-ularity prediction with k-means clustering and deep-belief networks[J]. Multimedia Tools and Applications, 2021, 80(10): 15745-15764. doi: 10.1007/s11042-020-10463-x
[5]	新浪微博. 热门微博管理规范(试行版)[EB/OL]. [2014-10-17]. https://weibo.com/p/100160-376-671072-4380562.
[6]	王晓萌, 方滨兴, 张宏莉, 王星. TSL:基于连接强度的Facebook消息流行度预测模型[J]. 通信学报, 2019, 40(10):1-9. doi: 10.11959/j.issn.1000-436x.2019207
[7]	胡颖, 胡长军, 傅树深, 黄建一. 流行度演化分析与预测综述[J]. 电子与信息学报, 2017, 39(04):805-816.
[8]	郭庆光. 传播学教程(第1版)[M]. 北京: 中国人民大学出版社, 2005: 53.
[9]	刘定一, 沈阳阳, 詹天明, 刘亚军, 应毅. 融合微博热点分析和LSTM模型的网络舆情预测方法[J]. 江苏大学学报(自然科学版), 2021, 42(05):546-553.
[10]	冯新淇, 张琨, 任奕豪, 谢彬, 赵静. 一种基于RLDA主题模型的特征提取方法[J]. 计算机与数字工程, 2017, 45(10):1980-1985.
[11]	Gao X, Zheng Z, Chu Q, et al. Popularity prediction for single tweet based on heterogeneous bass model[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(5): 2165-2178.
[12]	刘钰. 政务新媒体用户的使用行为分析[D]. 天津大学, 2018.
[13]	清博智能. 清博指数榜单公式[EB/OL]. https://www.gsdata.cn/solution/zsmx.
[14]	Meng Q. Topic popularity prediction of online social network based on single objective evolution[J]. Inter-national Journal of Autonomous and Adaptive Commu-nications Systems, 2020, 13(4): 371-388.
[15]	李勇. 基于两层聚类的微博热点话题发现算法研究[J]. 自动化技术与应用, 2021, 40(11):45-50.
[16]	陈梦秋, 周安民. 基于SVM的新浪热门微博预测[J]. 现代计算机(专业版), 2017, 34(09):23-27.
[17]	郑志蕴, 江国林, 张行进, 王振飞, 李钝. 基于多特征的热门微博预测算法研究[J]. 小型微型计算机系统, 2017, 38(03):494-498.
[18]	于海, 吕晴晴, 时鹏, 王铮, 胡长军. 基于在线社交网络事件库多因素耦合的流行度预测方法[J]. 天津大学学报(自然科学与工程技术版), 2020, 53(12):1272-1280.
[19]	王新乐, 杨文峰, 廖华明, 王永庆, 刘悦, 俞晓明, 程学旗. 基于多维度特征的主题标签流行度预测[J]. 山东大学学报(理学版), 2020, 55(01):94-101.
[20]	Hoang T B N, Mothe J. Predicting information diffusion on Twitter-Analysis of predictive features[J]. Journal of computational science, 2018, 28(1): 257-264. doi: 10.1016/j.jocs.2017.10.010
[21]	Wang K, Bansal M, Frahm J M. Retweet wars: Tweet popularity prediction via dynamic multimodal regression[C]. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, 2018: 1842-1851.
[22]	Zhu H, Yun X, Han Z. Weibo popularity prediction method based on propagation acceleration[J]. Journal of Computer Research and Development, 2018, 55(6): 1282-1293.
[23]	He Z, He Z, Wu J, et al. Feature construction for posts and users combined with LightGBM for social media popularity prediction[C]. Proceedings of the 27th ACM International Conference on Multimedia, 2019: 2672-2676.
[24]	Zhao J, Li J, Sui L, et al. Microblog Popularity Prediction Based on Multimodal Feature Fusion[C]. 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), IEEE, 2020: 475-481.
[25]	Jia K, Zhang X. Micro-blog Retweeting Prediction Based on Combined-Features and Random Forest[C]. CCF Conference on Computer Supported Cooperative Work and Social Computing. Springer, Singapore, 2019: 429-440.
[26]	Lv J, Liu W, Zhang M, et al. Multi-feature fusion for pre-dicting social media popularity[C]. Proceedings of the 25th ACM international conference on Multimedia, 2017: 1883-1888.
[27]	Meghawat M, Yadav S, Mahata D, et al. A multimodal approach to predict social media popularity[C]. 2018 IEEE conference on multimedia information processing and retrieval (MIPR), IEEE, 2018: 190-195.
[28]	Gayberi M, Oguducu S G. Popularity prediction of posts in social networks based on user, post and image features[C]. Proceedings of the 11th International Conference on Management of Digital EcoSystems, 2019: 9-15.
[29]	Liu T, Zhong Y, Chen K. Interdisciplinary study on popularity prediction of social classified hot online events in China[J]. Telematics and Informatics, 2017, 34(3): 755-764.
[30]	Luo Y, Wang F, Zhao F, et al. A framework for policy information popularity prediction in new media[C]. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), IEEE, 2019: 209-211.
[31]	Cerqueira V, Torgo L, Smailović J, et al. A comparative study of performance estimation methods for time series forecasting[C]. 2017 IEEE international conference on data science and advanced analytics (DSAA), IEEE, 2017: 529-538.
[32]	Chen Xiaoliang, Lan Xiang, Wan Jihong, Lu Peng, Yang Ming, Pancioni Luca. Evolutionary Prediction of Nonst-ationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics[J]. Discrete Dynamics in Nature and Society, 2021, 2021(1): 1-19.
[33]	Xiao C, Liu C, Ma Y, et al. Time sensitivity-based popu-larity prediction for online promotion on Twitter[J]. Information Sciences, 2020, 525(16): 82-92. doi: 10.1016/j.ins.2020.03.056
[34]	Xiao Y, Xie X, Li Q, et al. Nonlinear dynamics model for social popularity prediction based on multivariate chaotic time series[J]. Physica A: Statistical Mechanics and its Applications, 2019, 525(13): 1259-1275. doi: 10.1016/j.physa.2019.04.110
[35]	Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, and Tao Mei. Sequential prediction of social media popularity with deep temporal context networks[C]. InProceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017, 427(1):3062-3068.
[36]	Baosong M, Chenguang S. Prediction models for net-work multi-source dissemination of information based on multivariate chaotic time series[C]. 2017 3rd IEEE International Conference on Computer and Communi-cations (ICCC), IEEE, 2017: 767-771.
[37]	Sermsai R, Laohakiat S. Analysis and Prediction of Temporal Twitter Popularity Using Dynamic Time War-ping[C]. 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, 2019: 176-180.
[38]	Song Y, Li A, Quan Y. Topics' popularity prediction based on ARMA model[C]. Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence, 2018: 68-72.
[39]	Wu Q, Yang C, Zhang H, et al. Adversarial training model unifying feature driven and point process perspectives for event popularity prediction[C]. Proceedings of the 27th ACM International conference on information and knowledge management, 2018: 517-526.
[40]	张志扬, 张凤荔, 谭琪, 王瑞锦. 基于深度学习的信息级联预测方法综述[J]. 计算机科学, 2020, 47(07):141-153.
[41]	Wang H, Li Y, Feng Z, et al. ReTweeting analysis and prediction in microblogs: An epidemic inspired approach[J]. China Communications, 2013, 10(3): 13-24.
[42]	Cao Q, Shen H, Gao J, et al. Popularity prediction on social platforms with coupled graph neural networks[C]. Proceedings of the 13th International Conference on Web Search and Data Mining, 2020: 70-78.
[43]	Zhu H, Wang M. A Novel Reposting Prediction Method Based on Quantified Microblog Hotness in Sina Weibo[C]. Proc. International Conference on Computer Science and Application Engineering (Csae), 2017:13-18.
[44]	Quan Y, Jia Y, Zhou B, et al. Repost prediction incorp-orating time-sensitive mutual influence in social networks[J]. Journal of Computational Science, 2018, 28(5): 217-227. doi: 10.1016/j.jocs.2017.11.015
[45]	Li Y, Jin H, Yu X, et al. Intelligent prediction of private information diffusion in social networks[J]. Electronics, 2020, 9(5): 1-16. doi: 10.3390/electronics9010001
[46]	Xiao Y, Li J, Zhu Y, et al. User Behavior prediction of social hotspots based on multimessage interaction and neural network[J]. IEEE Transactions on Computational Social Systems, 2020, 7(2): 536-545. doi: 10.1109/TCSS.6570650
[47]	Chen F, Tan W H. Marked self-exciting point process modelling of information diffusion on Twitter[J]. The Annals of Applied Statistics, 2018, 12(4): 2175-2196.
[48]	Ma S, Feng L, Lai C H. Mechanistic modelling of viral spreading on empirical social network and popularity prediction[J]. Scientific reports, 2018, 8(1): 1-10.
[49]	Shang J, Huang S, Zhang D, et al. RNe2Vec: information diffusion popularity prediction based on repost network embedding[J]. Computing, 2021, 103(2): 271-289. doi: 10.1007/s00607-020-00858-x
[50]	Aghababaei S, Makrehchi M. Activity-based Twitter sampling for content-based and user-centric prediction models[J]. Human-centric Computing and Information Sciences, 2017, 7(1): 1-20. doi: 10.1186/s13673-016-0083-0
[51]	Han Z, Tang Z, He B. Improved Bass model for pre-dicting the popularity of product information posted on microblogs[J]. Technological Forecasting and Social Change, 2022, 176(3): 121458. doi: 10.1016/j.techfore.2021.121458
[52]	Cao Q, Shen H, Gao H, et al. Predicting the popularity of online content with group-specific models[C]. Proc-eedings of the 26th International Conference on World Wide Web Companion, 2017: 765-766.
[53]	Bao Z, Liu Y, Zhang Z, et al. Predicting popularity via a generative model with adaptive peeking window[J]. Physica A: Statistical Mechanics and its Applications, 2019, 522(10): 54-68. doi: 10.1016/j.physa.2019.01.132
[54]	Tan W H, Chen F. Predicting the popularity of tweets using internal and external knowledge: an empirical Bayes type approach[J]. AStA Advances in Statistical Analysis, 2021, 105(2): 335-352. doi: 10.1007/s10182-021-00390-z
[55]	Zhang H. Research on information popularity prediction of multimedia network based on fast K proximity alg-orithm[J]. International Journal of Autonomous and Adaptive Communications Systems, 2020, 13(2): 103-115. doi: 10.1504/IJAACS.2020.109808
[56]	Klubička F, Fernandez R. Examining a hate speech corpus for hate speech detection and popularity predi-ction[J]. arXiv preprint arXiv:1805.04661, 2018.
[57]	Zhang Y, Xu Z, Yang Q. Predicting popularity of mess-ages in twitter using a feature-weighted model[J]. .
[58]	Kristiyanti D A, Umam A H. Prediction of Indonesia presidential election results for the 2019-2024 period using twitter sentiment analysis[C]. 2019 5th Internat-ional Conference on New Media Studies (CONMEDIA), IEEE, 2019: 36-42.
[59]	Chen J, Sun B, Li H, et al. Deep ctr prediction in display advertising[C]. Proceedings of the 24th ACM intern-ational conference on Multimedia, 2016: 811-820.
[60]	武维, 李泽平, 杨华蔚, 林川, 王忠德. 融合内容特征和时序信息的深度注意力视频流行度预测模型[J]. 计算机应用, 2021, 41(07):1878-1884.
[61]	Li C, Ma J, Guo X, et al. Deepcas: An end-to-end pred-ictor of information cascades[C]. Proceedings of the 26th international conference on World Wide Web, 2017: 577-586.
[62]	Chen G, Kong Q, Xu N, et al. NPP: A neural popularity prediction model for social media content[J]. Neurocom-puting, 2019, 333(11): 221-230.
[63]	Yu H, Hu Y, Shi P. A prediction method of peak time popularity based on twitter hashtags[J]. IEEE Access, 2020, 8(1): 61453-61461. doi: 10.1109/Access.6287639
[64]	Zhang Y, Jatowt A. Image tweet popularity prediction with convolutional neural network[C]. European Confer-ence on Information Retrieval, Springer, Cham, 2019: 803-809.
[65]	Liao D, Xu J, Li G, et al. Popularity prediction on online articles with deep fusion of temporal process and content features[C]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(01): 200-207.
[66]	Stokowiec W, Trzciński T, Wołk K, et al. Shallow reading with deep learning: Predicting popularity of online content using only its title[C]. International Symposium on Me-thodologies for Intelligent Systems, Springer, Cham, 2017: 136-145.
[67]	Liu Y, Zhao J, Xiao Y. C-RBFNN: A user retweet beh-avior prediction method for hotspot topics based on im-proved RBF neural network[J]. Neurocomputing, 2018, 275(5): 733-746. doi: 10.1016/j.neucom.2017.09.015
[68]	Zhang Z, Chen T, Zhou Z, et al. How to become instag-ram famous: Post popularity prediction with dualatte-ntion[C]. 2018 IEEE international conference on big data (big data), IEEE, 2018: 2383-2392.
[69]	李国成, 陆俊, 王赟, 黄瑞, 刘谋海. 基于Bagging二次加权集成的孤立森林窃电检测算法[J]. 电力系统自动化, 2022, 46(02):92-100.
[70]	Leo Breiman. Random Forests[J]. Machine Learning, 2001, 45(1) : 5-32. doi: 10.1023/A:1010933404324
[71]	Chen T Q, Guestrin C. XGBoost: a scalable tree boos-ting system[C]. Proceedings of the ACM SIGKDD Inter-national Conference on Knowledge Discovery and Data Mining, 2016: 785-794.
[72]	Qiu X, Zuo Y, Liu G. ETCF: An ensemble model for CTR prediction[C]. 2018 15th International Conference on Service Systems and Service Management (ICSSSM), IEEE, 2018: 1-5.
[73]	Chen J, Liang D, Zhu Z, et al. Social media popularity prediction based on visual-textual features with XGB-oost[C]. Proceedings of the 27th ACM International Conference on Multimedia, 2019: 2692-2696.
[74]	Tang J, Xu X, Qiu J. Model Construction and Evaluation of Microblog News Popularity Prediction[C]. 2021 7th Annual International Conference on Network and Infor-mation Systems for Computers (ICNISC), IEEE, 2021: 457-464.
[75]	Wang J, Lou C, Yu R, et al. Research on hot micro-blog forecast based on XGBOOST and random forest[C]. International Conference on Knowledge Science, Engine-ering and Management, Springer, Cham, 2018: 350-360.
[76]	Carta S, Podda A S, Recupero D R, et al. Popularity pred-iction of instagram posts[J]. Information, 2020, 11(9): 453-453. doi: 10.3390/info11090453
[77]	Kang P, Lin Z, Teng S, et al. Catboost-based framework with additional user information for social media popu-larity prediction[C]. Proceedings of the 27th ACM interna-tional conference on multimedia, 2019: 2677-2681.
[78]	Deshpande D. Prediction & evaluation of online news popularity using machine intelligence[C]. 2017 Intern-ational Conference on Computing, Communication, Con-trol and Automation (ICCUBEA), IEEE, 2017: 1-6.
[79]	Lin Z, Huang F, Li Y, et al. A layer-wise deep stacking model for social image popularity prediction[J]. World Wide Web, 2019, 22(4): 1639-1655. doi: 10.1007/s11280-018-0590-1
[80]	朱海龙, 云晓春, 韩志帅. 基于传播加速度的微博流行度预测方法[J]. 计算机研究与发展, 2018, 55(06):1282-1293.
[81]	Cao Q, Shen H, Cen K, et al. DeepHawkes: Bridging the gap between prediction and understanding of information cascades[C]. Proceedings of the 26th ACM International Conference on Information and Know-ledge Management, USA:ACM, 2017:1149-1158.
[82]	包恒彬, 马玉鹏, 杨奉毅, 韩云飞. 基于时空注意力机制的加油站级客流量预测[J]. 计算机工程, 2021, 47(04):291-297.
[83]	王萧萧, 王亭雯, 马玉玲, 范佳奕, 崔超然. 基于深度森林的P2P网贷借款人信用风险评估方法[J]. 计算机科学, 2021, 48(S2):429-434.
[84]	周志华. 机器学习(第1版)[M]. 北京: 清华大学出版社, 2016: 28-32.

特征类别	常见子特征
发博用户特征	认证、地区、标签、生日、注册时长、年龄、粉丝数、关注数、发博数、近期活跃度、过往被转评赞数、影响力等
博文内容特征	信息量、主题、情感倾向、语义、外链/表情/图片数量、是否包含图片/视频/链接/长微博/表情、是否原创、是否含话题标签、是否涉及明星/名人/影响力用户等
信息传播特征	传播层级、出入度、传播速度、传播子网体量大小等
时间特征	发布日期、发布时刻、时间差等
其他	图片/视频的视觉特征、话题所在领域背景等

文献编号	特征类别	关键子特征	有效性评估
[17]	发博用户特征	粉丝数()、过往转评赞数()	特征综合>发博用户特征>博文内容特征
[17]	博文内容特征	词频()、是否包含图片/视频/长微博()、是否包含表情(*)	特征综合>发博用户特征>博文内容特征
[20]	发博用户特征	粉丝数(*)、关注数()、过往点赞数()、所属组群数(*)	特征综合后有效
	博文内容特征	是否包含图片(*)
	时间特征	发布时间(*)
[25]	发博用户特征	粉丝数(*)、发博数()、过往转评赞数()、关注数()	特征综合>发博用户特征>博文内容特征
[25]	博文内容特征	微博平均长度(*)、情感词数量()、标签数()、@的用户数()	特征综合>发博用户特征>博文内容特征
[56]	发博用户特征	粉丝数()、关注数()、发博数(*)	特征综合>发博用户特征>博文内容特征
[56]	博文内容特征	是否被回复(***)	特征综合>发博用户特征>博文内容特征
[72]	发博用户特征	粉丝数()、关注数()	特征综合>发博用户特征>其他>博文内容特征
	博文内容特征	标签数(*)、概念()、标题()、子类别(*)
	其他	图像视觉特征(*)
[77]	发博用户特征	ID(*)、粉丝数()、过往总浏览量(**)	特征综合>发博用户特征>博文内容特征
[77]	博文内容特征	标签数(*)、概念()、图片数()、标题长度(*)	特征综合>发博用户特征>博文内容特征
[79]	发博用户特征	过往平均浏览量(**)、所属组群数()	特征综合>发博用户特征>博文内容特征>时间特征>其他
	博文内容特征	标签数()、文本长度()、标题长度(**)
	时间特征	发布时间(*)
	其他	图像视觉特征(*)

热度预测方法	特点	优势	劣势
基于特征的热度预测方法	静态、从博文视角出发、热度多为传播效果进入终态/稳定态时的热度	（1）研究成果较多，可参考性强；（2）针对性和定制性较强，可覆盖学者期望的所有特征。	（1）特征模型需要人工构建，耗时耗力，且特征与热度之间的相关性决定了模型的预测效果；（2）静态特征易受到社交网络中突发事件的冲击；（3）预测结果为热度的宏观数值，没有关注微观上个人的行为干预。
基于时序的热度预测方法	动态、从事件发展视角出发、体现的是传播过程的变化规律	（1）预测效果和精度普遍较好；（2）可针对未来某一时间节点进行预测，实用性强；（3）用过往观测数据预测未来时刻数据，可解释性较强。	（1）对于前期时序数据观测记录与整合工作要求较高；（2）观测时间区间本身的局限性会影响预测模型的效果；（3）对于未来时序的预测中，短时效果会优于长时效果。
基于用户行为的热度预测方法	从传播底层实现视角出发，强调用户之间的交互影响和传播行为	（1）具有较强的理论基础；（2）考虑用户行为的复杂性、动态性与多样性。	（1）该方法中如信息级联和传染病模型过于强调“邻居”带来的影响，极易陷入理想的理论模型中，与现实观测效果偏差较大；（2）传播规模较大时，底层行为网络的构建与计算会增大模型开销；（3）以传播范围的形式体现热度水平，应用场景有限。

算法性质	文献编号	算法模型	研究特点	研究方法	任务类别	评价指标
传统机器学习	[56]	LR、SVM	聚焦特定情感倾向下的热度预测问题	特征	分类	ACC、F1
传统机器学习	[57]	FWM	引入特征加权机制	特征	分类	P、R、F1
深度学习	[35]	DTCN	将时间上下文和时间注意力结合	时序	回归	MAE、 SRC
	[39]	PreNets	创新的利用对抗模型寻找特征和时序点过程两种思维模式的平衡	特征、时序	回归	MAPE、Kendall
	[49]	RNe2Vec	可用于解决潜在用户关系网络未知的信息扩散热度预测问题	特征、用户行为	分类	ACC、P、R、F1
	[65]	DFTC	适用于事件传播任何阶段的热度预测	特征、时序	分类	ACC、F1
	[66]	BiLSTM	首次实现仅采用标题信息完成热度预测	特征	分类	ACC
集成学习	[72]	XGBoost	实现图片视觉特征、文本内容特征和用户特征的综合	特征	回归	MAE、 SRC
集成学习	[79]	LDS	用探测器根据特征性能自动设置集成模型深度	特征	回归	MAE、MSE