Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation
The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues. Identifiability can be divided into two: subjective identifiability, which is based on psychological perceptions (i.e., mental space), and objective identifiability, which is ba...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-09-01
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| Series: | Data Science and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764924000699 |
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| _version_ | 1849228312102043648 |
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| author | Xi Chen Hao Ding Jian Mou Yuping Zhao |
| author_facet | Xi Chen Hao Ding Jian Mou Yuping Zhao |
| author_sort | Xi Chen |
| collection | DOAJ |
| description | The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues. Identifiability can be divided into two: subjective identifiability, which is based on psychological perceptions (i.e., mental space), and objective identifiability, which is based on social media data (i.e., information space). This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique. The findings, based on data from Weibo, a Chinese social media platform, indicate that the top seven features and values for predicting social media identifiability include blog pictures (0.21), blog location (0.14), birthdate (0.12), location (0.10), blog interaction (0.10), school (0.08), and interests and hobbies (0.07). The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants. Based on the degree of deviation between the two, users can be divided into four categories—normal, conservative, active, and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure. This study provides insights into the development of privacy protection strategies based on social media data classification. |
| format | Article |
| id | doaj-art-9a2f8d99e5b447b39f183ca79fa61753 |
| institution | Kabale University |
| issn | 2666-7649 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj-art-9a2f8d99e5b447b39f183ca79fa617532025-08-23T04:49:18ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492025-09-018327028310.1016/j.dsm.2024.12.005Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigationXi Chen0Hao Ding1Jian Mou2Yuping Zhao3Management Science Department, School of Business and Tourism Management, Yunnan University, Kunming, 650091, China; Soft Science and Systems Science Research Center, School of Business and Tourism Management, Yunnan University, Kunming, 650091, ChinaManagement Science Department, School of Business and Tourism Management, Yunnan University, Kunming, 650091, China; Soft Science and Systems Science Research Center, School of Business and Tourism Management, Yunnan University, Kunming, 650091, ChinaSchool of Business, Pusan National University, Busan, 46241, Republic of Korea; Corresponding author.General Education Center, Communication University of China, Beijing, 100024, ChinaThe identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues. Identifiability can be divided into two: subjective identifiability, which is based on psychological perceptions (i.e., mental space), and objective identifiability, which is based on social media data (i.e., information space). This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique. The findings, based on data from Weibo, a Chinese social media platform, indicate that the top seven features and values for predicting social media identifiability include blog pictures (0.21), blog location (0.14), birthdate (0.12), location (0.10), blog interaction (0.10), school (0.08), and interests and hobbies (0.07). The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants. Based on the degree of deviation between the two, users can be divided into four categories—normal, conservative, active, and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure. This study provides insights into the development of privacy protection strategies based on social media data classification.http://www.sciencedirect.com/science/article/pii/S2666764924000699IdentifiabilitySocial mediaMental spaceInformation spaceSupervised machine learningPrivacy and security |
| spellingShingle | Xi Chen Hao Ding Jian Mou Yuping Zhao Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation Data Science and Management Identifiability Social media Mental space Information space Supervised machine learning Privacy and security |
| title | Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation |
| title_full | Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation |
| title_fullStr | Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation |
| title_full_unstemmed | Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation |
| title_short | Understanding user’s identifiability on social media: a supervised machine learning and self-reporting investigation |
| title_sort | understanding user s identifiability on social media a supervised machine learning and self reporting investigation |
| topic | Identifiability Social media Mental space Information space Supervised machine learning Privacy and security |
| url | http://www.sciencedirect.com/science/article/pii/S2666764924000699 |
| work_keys_str_mv | AT xichen understandingusersidentifiabilityonsocialmediaasupervisedmachinelearningandselfreportinginvestigation AT haoding understandingusersidentifiabilityonsocialmediaasupervisedmachinelearningandselfreportinginvestigation AT jianmou understandingusersidentifiabilityonsocialmediaasupervisedmachinelearningandselfreportinginvestigation AT yupingzhao understandingusersidentifiabilityonsocialmediaasupervisedmachinelearningandselfreportinginvestigation |