Social media user geolocalization based on multiple mention relationships
Aiming at the problem that the existing joint user geolocalization methods based on social media text and social relationships do not sufficiently mine the location correlation between heterogeneous data in social media, a social media user geolocalization method based on multiple mention relationsh...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2020-12-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020229/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539285499510784 |
|---|---|
| author | Yaqiong QIAO Xiangyang LUO Jiangtao MA Chenliang LI Meng ZHANG Ruixiang LI |
| author_facet | Yaqiong QIAO Xiangyang LUO Jiangtao MA Chenliang LI Meng ZHANG Ruixiang LI |
| author_sort | Yaqiong QIAO |
| collection | DOAJ |
| description | Aiming at the problem that the existing joint user geolocalization methods based on social media text and social relationships do not sufficiently mine the location correlation between heterogeneous data in social media, a social media user geolocalization method based on multiple mention relationships was proposed.First, a heterogeneous network was constructed by comprehensively considering the mention relationship between users, the user's mention relationship with location indicative words, and the user's mention relationship with geographic nouns.Then, a network simplification strategy was proposed to construct a user-location heterogeneous network that connects users live nearby more closely based on the common mention relationship.After that, a biased random walk algorithm was proposed for the graph node sampling to fully explore the network structure and alleviate the sparsity problem of known locations.Finally, a neural network classifier based on a multilayer perceptron was used to infer the user's location.Experimental results on three representative Twitter data sets of GEOTEXT, TW-US and TW-WORLD show that the proposed method can significantly improve the user geolocalization accuracy. |
| format | Article |
| id | doaj-art-8153baf70de14aafab88257329289b91 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-8153baf70de14aafab88257329289b912025-01-14T07:21:17ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-12-0141728159739091Social media user geolocalization based on multiple mention relationshipsYaqiong QIAOXiangyang LUOJiangtao MAChenliang LIMeng ZHANGRuixiang LIAiming at the problem that the existing joint user geolocalization methods based on social media text and social relationships do not sufficiently mine the location correlation between heterogeneous data in social media, a social media user geolocalization method based on multiple mention relationships was proposed.First, a heterogeneous network was constructed by comprehensively considering the mention relationship between users, the user's mention relationship with location indicative words, and the user's mention relationship with geographic nouns.Then, a network simplification strategy was proposed to construct a user-location heterogeneous network that connects users live nearby more closely based on the common mention relationship.After that, a biased random walk algorithm was proposed for the graph node sampling to fully explore the network structure and alleviate the sparsity problem of known locations.Finally, a neural network classifier based on a multilayer perceptron was used to infer the user's location.Experimental results on three representative Twitter data sets of GEOTEXT, TW-US and TW-WORLD show that the proposed method can significantly improve the user geolocalization accuracy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020229/social mediaheterogeneous networkuser geolocalizationmention relationship |
| spellingShingle | Yaqiong QIAO Xiangyang LUO Jiangtao MA Chenliang LI Meng ZHANG Ruixiang LI Social media user geolocalization based on multiple mention relationships Tongxin xuebao social media heterogeneous network user geolocalization mention relationship |
| title | Social media user geolocalization based on multiple mention relationships |
| title_full | Social media user geolocalization based on multiple mention relationships |
| title_fullStr | Social media user geolocalization based on multiple mention relationships |
| title_full_unstemmed | Social media user geolocalization based on multiple mention relationships |
| title_short | Social media user geolocalization based on multiple mention relationships |
| title_sort | social media user geolocalization based on multiple mention relationships |
| topic | social media heterogeneous network user geolocalization mention relationship |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020229/ |
| work_keys_str_mv | AT yaqiongqiao socialmediausergeolocalizationbasedonmultiplementionrelationships AT xiangyangluo socialmediausergeolocalizationbasedonmultiplementionrelationships AT jiangtaoma socialmediausergeolocalizationbasedonmultiplementionrelationships AT chenliangli socialmediausergeolocalizationbasedonmultiplementionrelationships AT mengzhang socialmediausergeolocalizationbasedonmultiplementionrelationships AT ruixiangli socialmediausergeolocalizationbasedonmultiplementionrelationships |