Identifying Chinese social media users' need for affect from their online behaviors
The need for affect (NFA), which refers to the motivation to approach or avoid emotion-inducing situations, is a valuable indicator of mental health monitoring and intervention, as well as many other applications. Traditionally, NFA has been measured using self-reports, which is not applicable in to...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1045279/full |
| _version_ | 1797956446892064768 |
|---|---|
| author | Hong Deng Hong Deng Nan Zhao Nan Zhao Yilin Wang Yilin Wang |
| author_facet | Hong Deng Hong Deng Nan Zhao Nan Zhao Yilin Wang Yilin Wang |
| author_sort | Hong Deng |
| collection | DOAJ |
| description | The need for affect (NFA), which refers to the motivation to approach or avoid emotion-inducing situations, is a valuable indicator of mental health monitoring and intervention, as well as many other applications. Traditionally, NFA has been measured using self-reports, which is not applicable in today's online scenarios due to its shortcomings in fast, large-scale assessments. This study proposed an automatic and non-invasive method for recognizing NFA based on social media behavioral data. The NFA questionnaire scores of 934 participants and their social media data were acquired. Then we run machine learning algorithms to train predictive models, which can be used to automatically identify NFA degrees of online users. The results showed that Extreme Gradient Boosting (XGB) performed best among several algorithms. The Pearson correlation coefficients between predicted scores and NFA questionnaire scores achieved 0.25 (NFA avoidance), 0.31 (NFA approach) and 0.34 (NFA total), and the split-half reliabilities were 0.66–0.70. Our research demonstrated that adolescents' NFA can be identified based on their social media behaviors, and opened a novel way of non-intrusively perceiving users' NFA which can be used for mental health monitoring and other situations that require large-scale NFA measurements. |
| first_indexed | 2024-04-10T23:49:23Z |
| format | Article |
| id | doaj.art-a0eae0fae6ed4485ad85f3e05267eb03 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-10T23:49:23Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-a0eae0fae6ed4485ad85f3e05267eb032023-01-10T21:10:45ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-01-011010.3389/fpubh.2022.10452791045279Identifying Chinese social media users' need for affect from their online behaviorsHong Deng0Hong Deng1Nan Zhao2Nan Zhao3Yilin Wang4Yilin Wang5Institute of Psychology, Chinese Academy of Sciences, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaInstitute of Psychology, Chinese Academy of Sciences, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaInstitute of Psychology, Chinese Academy of Sciences, Beijing, ChinaDepartment of Psychology, University of Chinese Academy of Sciences, Beijing, ChinaThe need for affect (NFA), which refers to the motivation to approach or avoid emotion-inducing situations, is a valuable indicator of mental health monitoring and intervention, as well as many other applications. Traditionally, NFA has been measured using self-reports, which is not applicable in today's online scenarios due to its shortcomings in fast, large-scale assessments. This study proposed an automatic and non-invasive method for recognizing NFA based on social media behavioral data. The NFA questionnaire scores of 934 participants and their social media data were acquired. Then we run machine learning algorithms to train predictive models, which can be used to automatically identify NFA degrees of online users. The results showed that Extreme Gradient Boosting (XGB) performed best among several algorithms. The Pearson correlation coefficients between predicted scores and NFA questionnaire scores achieved 0.25 (NFA avoidance), 0.31 (NFA approach) and 0.34 (NFA total), and the split-half reliabilities were 0.66–0.70. Our research demonstrated that adolescents' NFA can be identified based on their social media behaviors, and opened a novel way of non-intrusively perceiving users' NFA which can be used for mental health monitoring and other situations that require large-scale NFA measurements.https://www.frontiersin.org/articles/10.3389/fpubh.2022.1045279/fullneed for affectsocial mediaonline behaviormental healthmachine learningExtreme Gradient Boosting |
| spellingShingle | Hong Deng Hong Deng Nan Zhao Nan Zhao Yilin Wang Yilin Wang Identifying Chinese social media users' need for affect from their online behaviors Frontiers in Public Health need for affect social media online behavior mental health machine learning Extreme Gradient Boosting |
| title | Identifying Chinese social media users' need for affect from their online behaviors |
| title_full | Identifying Chinese social media users' need for affect from their online behaviors |
| title_fullStr | Identifying Chinese social media users' need for affect from their online behaviors |
| title_full_unstemmed | Identifying Chinese social media users' need for affect from their online behaviors |
| title_short | Identifying Chinese social media users' need for affect from their online behaviors |
| title_sort | identifying chinese social media users need for affect from their online behaviors |
| topic | need for affect social media online behavior mental health machine learning Extreme Gradient Boosting |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.1045279/full |
| work_keys_str_mv | AT hongdeng identifyingchinesesocialmediausersneedforaffectfromtheironlinebehaviors AT hongdeng identifyingchinesesocialmediausersneedforaffectfromtheironlinebehaviors AT nanzhao identifyingchinesesocialmediausersneedforaffectfromtheironlinebehaviors AT nanzhao identifyingchinesesocialmediausersneedforaffectfromtheironlinebehaviors AT yilinwang identifyingchinesesocialmediausersneedforaffectfromtheironlinebehaviors AT yilinwang identifyingchinesesocialmediausersneedforaffectfromtheironlinebehaviors |