TSL：基于连接强度的Facebook消息流行度预测模型

王晓萌; 方滨兴; 张宏莉; 王星

doi:10.11959/j.issn.1000-436x.2019207

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TSL：基于连接强度的Facebook消息流行度预测模型

学术论文 | 更新时间：2024-06-05

- TSL：基于连接强度的Facebook消息流行度预测模型
- TSL:predicting popularity of Facebook content based on tie strength
- 通信学报 2019年40卷第10期页码：1-9
- 作者机构：
  
  1. 哈尔滨工业大学计算机网络与信息安全技术研究中心，黑龙江哈尔滨 150001
  2. 广州大学网络空间先进技术研究院，广东广州 510006
- 作者简介：
  
  [ "王晓萌（1987- ），男，黑龙江哈尔滨人，哈尔滨工业大学博士生，主要研究方向为在线社交网络、信息传播预测、舆情安全等。" ]
  [ "方滨兴（1960- ），男，江西万年人，中国工程院院士，哈尔滨工业大学教授、博士生导师，主要研究方向为计算机网络与信息安全理论与技术、并行计算等。" ]
  [ "张宏莉（1973- ），女，吉林榆树人，博士，哈尔滨工业大学教授、博士生导师，主要研究方向为网络与信息安全、网络测量与建模、网络计算、并行处理等。" ]
  [ "王星（1981- ），男，重庆人，博士，哈尔滨工业大学助理研究员，主要研究方向为网络与信息安全、网络舆情监控、知识迁移。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB0803305);国家重点研发计划基金资助项目(2016QY03D0501)
- DOI：10.11959/j.issn.1000-436x.2019207
  中图分类号： TP393
- 网络出版日期：2019-10，
  
  纸质出版日期：2019-10-25
- 稿件说明：
移动端阅览
王晓萌, 方滨兴, 张宏莉, 等. 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.

Xiaomeng WANG, Binxing FANG, Hongli ZHANG, et al. TSL:predicting popularity of Facebook content based on tie strength[J]. Journal on communications, 2019, 40(10): 1-9.
王晓萌, 方滨兴, 张宏莉, 等. 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.

Xiaomeng WANG, Binxing FANG, Hongli ZHANG, et al. TSL:predicting popularity of Facebook content based on tie strength[J]. Journal on communications, 2019, 40(10): 1-9. DOI： 10.11959/j.issn.1000-436x.2019207.

摘要

在线社交网络的迅速发展使信息呈现爆炸式增长，然而不同消息的流行度存在较大差异，对其准确预测一直是领域内的研究难点。流行度预测的任务是根据消息传播早期过程中涌现的特征预测其未来的传播趋势，现有基于传播网络特征与拟合函数的预测模型难以解决预测准确率低的问题，因此借助社会学中的弱连接理论，引入连接强度的概念，并融合消息传播早期的流行度构建多元线性回归方程，提出了一种针对 Facebook 知名主页的消息流行度的预测模型TSL。通过在Facebook真实数据集（含154万次转发）上与其他具有代表性的基准模型进行比较，实验表明TSL模型可以对消息的最终转发流行度进行有效预测，预测性能优于同类方法。

Abstract

The rapid development of online social networks leads to an explosion of information

however

there are great differences in the popularity of different messages

and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore

with the help of the weak ties theory in sociology

the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models

an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model

and the performance is better than the existing similar methods.

关键词

Keywords

references

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