Abstract
With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Intelligence and Security Informatics |
| Subtitle of host publication | Security and Big Data, ISI 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 113-118 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509067275 |
| DOIs | |
| State | Published - Aug 8 2017 |
| Event | 15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017 - Beijing, China Duration: Jul 22 2017 → Jul 24 2017 |
Other
| Other | 15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 7/22/17 → 7/24/17 |
Fingerprint
Keywords
- cross-platform
- similarity computation
- user mapping
- word2vec
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality
Cite this
Mapping users across social media platforms by integrating text and structure information. / Sun, Song; Li, Qiudan; Yan, Peng; Zeng, Dajun.
2017 IEEE International Conference on Intelligence and Security Informatics: Security and Big Data, ISI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 113-118 8004884.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Mapping users across social media platforms by integrating text and structure information
AU - Sun, Song
AU - Li, Qiudan
AU - Yan, Peng
AU - Zeng, Dajun
PY - 2017/8/8
Y1 - 2017/8/8
N2 - With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.
AB - With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.
KW - cross-platform
KW - similarity computation
KW - user mapping
KW - word2vec
UR - http://www.scopus.com/inward/record.url?scp=85030223395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030223395&partnerID=8YFLogxK
U2 - 10.1109/ISI.2017.8004884
DO - 10.1109/ISI.2017.8004884
M3 - Conference contribution
AN - SCOPUS:85030223395
SP - 113
EP - 118
BT - 2017 IEEE International Conference on Intelligence and Security Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
ER -