Fostering college students’ mental well-being: the impact of social networking site utilization on emotion management and regulation

With the widespread proliferation of the Internet, social networking sites have increasingly become integrated into the daily lives of university students, leading to a growing reliance on these platforms. Several studies have suggested that this emotional dependence on social networking sites stems from unmet psychological needs. Meanwhile, social rejection has been identified as a prevalent phenomenon that exacerbates the deficiency of individual psychological needs. However, existing research on aspect-level sentiment analysis among college students within social networking sites faces challenges such as inadequate feature extraction, ineffective handling of data noise, and the neglect of complex interactions in multimodal data. To address these issues, this paper introduces a novel approach, the Multi-Granular View Dynamic Fusion Model (MVDFM), developed from both coarse-grained and fine-grained perspectives. MVDFM extracts multi-granular view features from textual and visual content, incorporating a dynamic gating self-attention mechanism. Additionally, it proposes a three-view decomposition higher-order pooling mechanism for a two-stage dynamic fusion of these features. Experimental results demonstrate the model's effectiveness, achieving accuracy and F1 values of 78.78% and 74.48% on the Twitter-2015 dataset, and 73.89% and 72.47% on the Twitter-2017 dataset, respectively. This efficient supervision enables the extraction of deep semantic information from multimodal data generated by college students on social networking sites. The model adeptly mines pertinent information related to target aspect-based words, enhancing the efficacy of aspect-level emotion prediction. Furthermore, it facilitates an effective exploration of the intricate interplay between social rejection, monitoring on social networking sites, the fear of missing out, and dependence on social networking sites, ultimately aiding university students in regulating their emotional management.

Authors

Institutions

Publication Details

Journal
BMC Psychology
Published
2024-11-21
DOI
https://doi.org/10.1186/s40359-024-02186-7
Citations
3
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
1.04
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fostering college students’ mental well-being: the impact of social networking site utilization on emotion management and regulation

Yong Han, Zhifen Xu
3 citations
BMC Psychology
Sentiment Analysis and Opinion Mining
1.04
article

Fostering college students’ mental well-being: the impact of social networking site utilization on emotion management and regulation

Yong Han, Zhifen Xu
article en
3 citations

Abstract

With the widespread proliferation of the Internet, social networking sites have increasingly become integrated into the daily lives of university students, leading to a growing reliance on these platforms. Several studies have suggested that this emotional dependence on social networking sites stems from unmet psychological needs. Meanwhile, social rejection has been identified as a prevalent phenomenon that exacerbates the deficiency of individual psychological needs. However, existing research on aspect-level sentiment analysis among college students within social networking sites faces challenges such as inadequate feature extraction, ineffective handling of data noise, and the neglect of complex interactions in multimodal data. To address these issues, this paper introduces a novel approach, the Multi-Granular View Dynamic Fusion Model (MVDFM), developed from both coarse-grained and fine-grained perspectives. MVDFM extracts multi-granular view features from textual and visual content, incorporating a dynamic gating self-attention mechanism. Additionally, it proposes a three-view decomposition higher-order pooling mechanism for a two-stage dynamic fusion of these features. Experimental results demonstrate the model's effectiveness, achieving accuracy and F1 values of 78.78% and 74.48% on the Twitter-2015 dataset, and 73.89% and 72.47% on the Twitter-2017 dataset, respectively. This efficient supervision enables the extraction of deep semantic information from multimodal data generated by college students on social networking sites. The model adeptly mines pertinent information related to target aspect-based words, enhancing the efficacy of aspect-level emotion prediction. Furthermore, it facilitates an effective exploration of the intricate interplay between social rejection, monitoring on social networking sites, the fear of missing out, and dependence on social networking sites, ultimately aiding university students in regulating their emotional management.

BMC PsychologyVol. 12(1)
Xinyang Normal University (CN)
Quality Education
Openalex Percentile: Top 18%
Sentiment Analysis and Opinion Mining
1.04
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Fostering college students’ mental well-being: the impact of social networking site utilization on emotion management and regulation — Yong Han, Zhifen Xu · BMC Psychology (2024) | TGRS Research Map | TGRS