TSL:predicting popularity of Facebook content based on tie strength
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 popul...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-10-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019207/ |
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| _version_ | 1841539335193624576 |
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| author | Xiaomeng WANG Binxing FANG Hongli ZHANG Xing WANG |
| author_facet | Xiaomeng WANG Binxing FANG Hongli ZHANG Xing WANG |
| author_sort | Xiaomeng WANG |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2ed0e7146ca249f3b95be524aed287bc |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2ed0e7146ca249f3b95be524aed287bc2025-01-14T07:17:51ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-10-01401959730017TSL:predicting popularity of Facebook content based on tie strengthXiaomeng WANGBinxing FANGHongli ZHANGXing WANGThe 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019207/online social networksweak tiespopularityinformation diffusion |
| spellingShingle | Xiaomeng WANG Binxing FANG Hongli ZHANG Xing WANG TSL:predicting popularity of Facebook content based on tie strength Tongxin xuebao online social networks weak ties popularity information diffusion |
| title | TSL:predicting popularity of Facebook content based on tie strength |
| title_full | TSL:predicting popularity of Facebook content based on tie strength |
| title_fullStr | TSL:predicting popularity of Facebook content based on tie strength |
| title_full_unstemmed | TSL:predicting popularity of Facebook content based on tie strength |
| title_short | TSL:predicting popularity of Facebook content based on tie strength |
| title_sort | tsl predicting popularity of facebook content based on tie strength |
| topic | online social networks weak ties popularity information diffusion |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019207/ |
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