The Status and Influencing Factors of Perceived Social Support Among Adolescents: An Analysis Based on Weibo Big Data

Gulimire Isak¹,², Xiayu Du¹,², Binyu Wang¹,², Jiaojiao Jia¹,², Jiao Li¹,², Menglu Dou¹,², Tieyu Duan¹,², Qian Xiong¹,², Zhihong Ren¹,²,³*

¹(Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan 430079, China)
²(Key Laboratory of Human Development and Mental Health of Hubei Province, School of Psychology, Central China Normal University, Wuhan 430079, China)
³(School of Psychology, Liaoning Normal University, Dalian 116029, China)

Abstract:
[Objective] This study aimed to explore a Weibo-based approach to assessing adolescents' perceived social support and to examine its demographic variations.
[Methods] Approximately 300,000 Weibo posts from adolescents aged 12–18 across China were collected via Python. A perceived social support lexicon was developed encompassing four dimensions—emotional, informational, instrumental, and appraisal support. Word frequency analysis was conducted using the validated Chinese text analysis software "Wenxin System," followed by multidimensional statistical analyses based on gender, age, and region.
[Results] The constructed lexicon consisted of 1,312 validated entries. Significant differences in perceived social support were found across demographic groups: adolescents in western and northeastern regions showed lower levels than those in eastern and central regions, and males scored slightly higher than females.
[Limitations] Data were limited to publicly available Weibo texts, potentially affecting sample representativeness. In addition, the lexicon method may not fully capture deeper psychological semantics.
[Conclusion] Social media text analysis provides an effective tool for evaluating perceived social support among adolescents and offers practical value for early psychological risk detection.

Keywords: Perceived social support, Weibo text analysis, Lexicon construction, Adolescent

In the contemporary era of highly developed digital social media, language as a crucial manifestation of individual psychological states has gained increasing attention in psychological research. Early language psychology primarily relied on expressive writing analysis \cite{1}, but over recent decades, advances in computational linguistics and natural language processing have enabled researchers to recognize that quantitative analysis of word categories can reveal the psychological mechanisms underlying texts \cite{2},\cite{3}. Particularly, the Linguistic Inquiry and Word Count (LIWC) tool developed by Pennebaker and colleagues has played a significant role in research on the relationship between language use and psychological states, facilitating the expansion of language psychology into real-world contexts \cite{4},\cite{5}. In the Chinese context, the development of dictionaries such as CLIWC \cite{6} and SCLIWC \cite{7} has adapted LIWC tools for analyzing Chinese social media corpora, advancing Chinese psychological text analysis.

Meanwhile, social media platforms like Weibo provide unprecedented ecological data resources for studying individual psychology. As an open social platform with over 500 million monthly active users, Weibo users continuously express emotions, viewpoints, and social interaction needs through text, comments, and reposts, offering researchers high-frequency, low-threshold samples of natural behavior \cite{8},\cite{9}. Based on this, predictive models of psychological states through big data language behavior have been established and widely applied in personality, emotion, and mental health research \cite{10}-\cite{12}.

In psychological research, "perceived social support" is a key variable for understanding how individuals cope with stress and maintain mental health \cite{13},\cite{14}. It refers to an individual's subjective experience of receiving care, help, and understanding from others \cite{15}. While traditional measurement tools such as PSSS \cite{15} and SSRS \cite{16} are widely used, they rely on self-report questionnaires and cannot capture real-time perceptions of support in daily social interactions. Therefore, combining social media text analysis with computational lexicon technology to construct a Weibo-based "perceived social support dictionary" not only addresses the limitations of traditional methods but also provides a new pathway for detecting dynamic social support among adolescents.

Adolescents, as a group undergoing rapid physical and psychological development, face complex interpersonal adaptation and social identity construction challenges, often accompanied by academic pressure and interpersonal conflicts \cite{17}. Social media has become not only an important platform for them to express emotions and seek identity but may also carry rich social support signals. Existing research has found that perceived social support levels significantly impact adolescents' mental health, academic performance, and suicide risk \cite{18},\cite{19}. However, these studies mostly rely on questionnaire data, making it difficult to cover broader adolescent populations and monitor changing trends in real time.

Consequently, this study attempts to construct a perceived social support dictionary tailored for adolescents based on social media text and use it to conduct quantitative analysis of social support levels in Weibo posts. Dictionary construction will comprehensively utilize manual annotation, expert rating, and computational models to collaboratively screen key vocabulary, and classify them according to the four dimensions of social support theory (emotional, appraisal, instrumental, and informational support) \cite{4}. On the technical implementation level, we will combine LIWC's closed-vocabulary analysis with open-corpus mining strategies to explore how to capture the characteristics of online social support expression in Weibo contexts \cite{12},\cite{3}. Regarding data sources, we will use Weibo crawler technology to collect publicly available text data from adolescent users, and through data cleaning and preprocessing, extract high-quality samples for modeling. Ultimately, the study will examine differences in perceived social support across regional, gender, and age groups, providing data support and strategic recommendations for adolescent social support intervention and mental health promotion.

Through this research, we aim not only to enrich the existing measurement tool system for perceived social support but also to promote a paradigm shift in psychological research methods from "questionnaire-driven" to "data-driven," expanding the application depth of language behavior research in educational and social psychology.

Study 1: Development and Validation of the Perceived Social Support Dictionary

In Study 1, we constructed a perceived social support dictionary to measure Weibo users' perceived social support levels. We extracted support-related words from authoritative questionnaires and dictionaries including Modern Chinese Dictionary, Modern Chinese Content Word Collocation Dictionary, Public Welfare and Development English-Chinese Bilingual Dictionary, and Dictionary of Social Welfare. All words were rated and filtered to exclude duplicates and low-frequency terms. The final dictionary comprised 1,312 words.

Since the 1970s, social support has been formally established and utilized in psychiatric literature. Perceived social support is derived from the broader concept of social support, making it essential to first understand social support's definition. Researchers define social support as assistance provided by others or social relationship networks when individuals encounter challenges beyond their personal capacity to cope \cite{20}. As research progressed, scholars reached consensus that social support is a multifaceted concept primarily divisible into two categories: actually received social support and perceived social support \cite{21}.

The operational definition of perceived social support in this study is: Perceived social support refers to the support that individuals can feel from family, friends, teachers, and others, which can bring individuals a satisfactory emotional state and form a relatively stable trait under the long-term influence of this state \cite{13}.

(1) Dictionary Construction

This study investigated perceived social support levels in social media through the following dictionary construction steps:

Initial word selection. Following standard lexicon compilation methods, we selected words related to perceived social support from authoritative questionnaires and dictionaries \cite{22},\cite{23}. We utilized commonly used questionnaires for individual social support measurement: Perceived Social Support Scale (PSSS, \cite{13}), Network of Relationships Inventory (NRI, \cite{26}), Multidimensional Scale of Perceived Social Support (MSPSS, \cite{15}), Social Support Rating Scale (SSRS, \cite{14}), Adolescent Social Support Rating Scale \cite{27}, Adolescent Online Social Support Questionnaire \cite{28}, Simplified Two-Way Social Support Scale \cite{24}, and Social Support Scale \cite{29}. Combined with Modern Chinese Dictionary, Modern Chinese Content Word Collocation Dictionary, Public Welfare and Development English-Chinese Bilingual Dictionary, and Dictionary of Social Welfare \cite{25}, we obtained 1,808 words related to perceived social support from these sources.

Filtering irrelevant words. We recruited five psychology graduate students to participate in dictionary construction. The initially selected words were rated by these five graduate students. Before the task, we provided them with detailed training. After understanding the definition and manifestations of perceived social support, participants were asked to independently evaluate all initial words. Words related to perceived social support received a "pass" rating, meaning they conveyed support; otherwise, a "fail" rating was given. Words selected through three rounds of evaluation were retained on the list. After deleting 496 irrelevant words, a total of 1,312 words remained.

Expanding remaining words. We manually added as many synonyms as possible based on existing dictionaries. Newly added words were evaluated by the five psychology graduate students, with words passing three rounds of selection retained in the list.

Deleting low-frequency and duplicate words. Dictionary validity assessment involved calculating the frequency of each word's appearance in the Weibo database. We constructed a Weibo database for China's 31 provinces/municipalities/autonomous regions using posts from active Weibo users between January 2010 and May 2022. This was accomplished using public APIs. When downloading posts, necessary filtering conditions were set to ensure content validity. We initially selected active users on Weibo who had registered for at least one year and posted at least 500 tweets. We narrowed the sample to accounts with fewer than 3,000 followers to focus on ordinary users rather than celebrities, professional bloggers, or organizations. We also excluded retweets from analysis, keeping only original tweet samples, as retweets do not represent users' own expressions. We randomly collected 1/10,000 of the content from the Weibo database and calculated the frequency of dictionary words. After deleting irrelevant and duplicate words, the dictionary was updated to contain 1,312 words.

(2) Validity Analysis

① Measurement of Perceived Social Support. We employed word frequency analysis to obtain perceived social support data from the Weibo database, which contained posts from active Sina Weibo users across 31 provinces/municipalities/autonomous regions in China from January 2010 to May 2024.

First, we used the "TextMind" system developed by the Computational Cyberpsychology Laboratory of the Institute of Psychology, Chinese Academy of Sciences, to segment Weibo posts and calculate word frequencies. This system can divide text into independent words with linguistic characteristics according to Chinese grammatical rules. Next, we used the perceived social support dictionary to calculate the frequency of support-related words in each region, represented by the number of dictionary words divided by the total number of words in posts. Word frequency described the level of perceived social support in different regions across different periods. After this processing, we obtained perceived social support data for 31 provinces/municipalities/autonomous regions in China from January 2010 to May 2022.

② Validity Analysis Procedure. We used the consistency between word frequency and manual ratings to explore whether word frequency truly reflected the public's perceived social support level.

First, we additionally recruited five psychology graduate students to evaluate Weibo texts and tested the consistency of their ratings on perceived social support across 50 Weibo texts. Before the task, we provided them with professional training to ensure everyone understood the definition of perceived social support. We randomly selected 50 Weibo posts from the database for evaluation. Subsequently, we asked them to independently score these 50 Weibo texts using a five-point Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree) to assess the degree to which the texts conveyed perceived social support. For further validity assessment, we randomly selected another 500 Weibo texts from the database. Five psychology graduate students rated the degree of perceived social support presented in the texts. Each person rated 100 Weibo posts, yielding 500 rated posts. We used the perceived social support dictionary for word frequency analysis and calculated the Pearson correlation between manual ratings and word frequency for each text.

2.2 Results

[FIGURE:1] Dictionary Compilation Flowchart

The results showed that the five psychology graduate students demonstrated high consistency when rating perceived social support across 50 Weibo texts (r = 0.897, p < 0.001), indicating their ratings were valid. For the 500 Weibo texts, a significant positive relationship was observed between the score for each text and word frequency (r = 0.266, p < 0.001), which meets general criteria for moderate correlation \cite{30}, demonstrating the dictionary's reliability.

[TABLE:1] Examples from the Perceived Social Support Dictionary: trust, consolation, guidance, teaching, bonus, allowance, praise, commendation

Study 2: The Status and Influencing Factors of Perceived Social Support Among Adolescents

Using the perceived social support dictionary constructed in Study 1, we measured perceived social support levels among adolescents aged 12–18 across China, establishing the first social media support level database covering 31 provinces and municipalities.

3.1 Data Crawling

This study employed Python-based crawling to obtain Sina Weibo data, mining original Weibo posts from adolescent users born between 2006 and 2013 based on their registered account information. The system automatically identified vocabulary related to social support, with 598 lexical items drawn from the perceived social support dictionary compiled in Study 1. Subsequently, the TextMind segmentation system was used for word frequency statistics to derive Weibo text scores at the social support level.

(1) Data Cleaning. Non-original Weibo posts were removed from the raw data, along with information containing easily confused words, empty texts, non-Chinese characters, numbers, punctuation marks, and internet emoticons. Content unrelated to perceived social support, such as hypotheses, food, celebrities, and voting, was also deleted. Simultaneously, user screening was conducted based on account activation (registered for at least one year with over 100 original posts) and user type (fewer than 3,000 followers). Ultimately, 300,000 valid Weibo posts were obtained.

(2) Data Grouping and Stratification. Based on age information, adolescent users aged 12–18 were selected and divided into three subgroups: 12–14 years, 15–16 years, and 17–18 years. Based on regional information, social support scores were calculated for each prefecture-level region. Additionally, gender and monthly information were used to analyze gender differences and temporal trends in adolescent social support.

3.2 Provincial-Level Analysis of Social Support Perception

Based on the cleaned data, we extracted blog post location information to analyze adolescent perceived social support levels across 31 provinces, autonomous regions, and municipalities in mainland China (note: this study's data did not cover Hong Kong Special Administrative Region, Macao Special Administrative Region, or Taiwan Province). As shown in Table 2 [TABLE:2], provinces such as Sichuan, Hubei, and Fujian scored relatively high, while Tibet, Qinghai, and Liaoning scored lower. Provincial score differences were related to regional economic development, psychological aid resource allocation, and cultural expression patterns.

For example, Sichuan's "Tianfu Youth Psychological Escort Program" enhanced support experiences through cultural adaptation, whereas Tibet and other regions faced significant "support blind spots" due to weak digital resources and lack of support channels. Furthermore, in Shandong, Henan, and other areas influenced by clan culture, topics such as "family honor" appeared frequently, potentially masking real support needs.

[TABLE:2] Adolescent Perceived Social Support Levels by Province, Autonomous Region, and Municipality in Mainland China

3.3 Age Difference Analysis

Analysis of support perception across different age groups of 12–18-year-old adolescents revealed a fluctuating trend: a significant increase from ages 12 to 13, possibly due to enhanced dependence on external support after entering junior high school; relative stability during ages 13–16; peak perception levels at age 17, followed by a slight decline, perhaps due to role transitions and enhanced independence. Minimum values were all 0, indicating that some adolescents did not explicitly express or perceive social support on social platforms. As shown in Table 3 [TABLE:3]:

[TABLE:3] Perceived Social Support Levels Among 12–18-Year-Old Adolescents

[FIGURE:4] Trend Chart of Changes in Adolescent Perceived Social Support Levels

3.4 Regional Difference Analysis

Based on the National Bureau of Statistics' regional classification standards, we analyzed adolescent perception levels across four major regions: eastern, central, western, and northeastern. Results showed that the central region scored highest, while the northeast scored lowest. The western region exhibited the largest standard deviation, reflecting uneven internal development. The central region built a relatively strong social support network through parallel development of clan support and policy promotion, whereas the northeast experienced severe loss of social capital and weakened social networks. Although the western region's overall score was relatively high, distribution was extremely uneven, with obvious urban-rural differences, as shown in Tables 4 [TABLE:4], 5 [TABLE:5], and 6 [TABLE:6]. Notably, all regions showed samples with perceived values of 0, suggesting that marginalized adolescents remain in a state of social support disembedding, requiring precise early warning and intervention strategies.

[TABLE:4] Adolescent Perceived Social Support Levels in Eastern, Western, Central, and Northeastern Regions of China

[TABLE:5] Difference Tests of Adolescent Perceived Social Support Levels in Eastern, Western, Central, and Northeastern Regions: ±0.02579 ±0.02848 ±0.02918 ±0.03098

[TABLE:6] Post-hoc Tests of Adolescent Perceived Social Support Levels in Eastern, Western, Central, and Northeastern Regions: 95% Confidence Interval (I) Region (J) Region Mean diff (I-J) 0.000 0.000 0.000 0.000 p < 0.05, p < 0.01, p < 0.001 0.000*

3.5 Gender Difference Analysis

As shown in Table 7 [TABLE:7], independent samples t-test results indicated that male adolescents scored significantly higher than females on perceived social support (t = 115.393, p < 0.001, d = 0.115). This may be related to males being more likely to participate in social activities and more inclined to publicly express needs. However, this finding also suggests that female adolescents' support needs may be more implicit, requiring enhanced identification mechanisms in intervention design.

[TABLE:7] Gender Differences in Adolescent Perceived Social Support Scores

Based on Weibo big data, this study systematically investigated the measurement and current status of adolescent perceived social support, aiming to enhance understanding and intervention capabilities regarding adolescent social support conditions by constructing a dictionary tool applicable to social media contexts. The discussion proceeds as follows:

4.1 The Value and Significance of Perceived Social Support Dictionary Construction

Traditional research on perceived social support has primarily relied on questionnaire measurements. Although these methods offer certain reliability and validity guarantees, they have limitations in dynamic monitoring and ecological validity. Particularly in digital contexts, adolescents' psychological states are often expressed through social media, which traditional tools struggle to capture semantically. This study systematically reviewed perceived social support theories and measurement tools and constructed a four-dimensional Weibo perceived social support dictionary (1,312 entries) based on the Chinese context. The dictionary comprehensively covers social support semantics from emotional, informational, instrumental, and appraisal dimensions.

The construction process emphasized rigorous multi-source data integration and expert evaluation, with internal consistency and external validity indicators demonstrating high reliability and validity. Compared with previous emotion dictionaries or general text analysis tools, this dictionary is more thematically targeted, providing a generalizable technical pathway for digital measurement of perceived social support and offering a feasible model for subsequent natural language processing applications in mental health assessment.

4.2 Differential Characteristics and Explanations of Adolescent Perceived Social Support

Using Weibo text data and dictionary analysis methods, the study found significant differences in adolescent perceived social support levels across multiple dimensions including province, region, age, and gender:

(1) Provincial and regional differences: Adolescents in economically developed eastern provinces (e.g., Jiangsu, Guangdong) showed higher perceived social support levels than those in underdeveloped western provinces (e.g., Tibet, Qinghai). From Bronfenbrenner's social ecological systems theory perspective, this can be understood as macro-system resource endowment differences leading to functional differences in micro-support systems \cite{31}. Regions with abundant educational resources and well-developed social service systems construct positive social support ecologies, while resource-scarce regions face "ecological system nesting ruptures" that weaken the integrity of adolescents' support perceptions.

(2) Age differences: The findings revealed distinct developmental stage characteristics. Adolescents aged 12–15 scored higher on perceived support due to strong needs for peer relationship construction, while the 16–18 age group experienced relative declines due to institutional pressures (college entrance, employment, etc.). Consistent with Erikson's developmental stage theory, the "supply-demand imbalance" of social support becomes a potential risk factor for psychological distress in middle adolescence \cite{32}.

(3) Gender differences: Female adolescents generally showed higher perceived social support levels than males, yet their mental health problems were more pronounced. This relates to females' greater emphasis on emotional expression and social connection \cite{33}, suggesting that interventions require stratified design from a gender perspective, such as strengthening emotional expression channels for female groups while focusing on emotional regulation and psychological resilience training for male adolescents.

In summary, this study reveals the social structural attributes and developmental sensitivity characteristics of perceived social support through multidimensional differences, providing data support and theoretical basis for targeted psychological interventions.

4.3 Research Limitations

Despite methodological innovations, this study has several limitations. First, constrained by the linguistic environment and the openness of Weibo text corpora, the dictionary's timeliness and coverage require continuous updating and optimization; internet slang, unstructured expressions, and emoticons have not yet been fully incorporated into identification systems.

Second, Weibo data collection was limited by platform interfaces and historical data deletion, resulting in certain sample coverage biases that cannot fully represent all adolescent groups. Moreover, due to anonymity characteristics, it is difficult to control for potential effects of individual difference variables such as personality traits and family background on text expression.

Additionally, perceived social support expression has cultural embeddedness, but the current dictionary is primarily based on the Chinese context and lacks multi-language and multi-cultural adaptation, leaving its cross-cultural applicability to be further expanded and validated.

4.4 Future Directions

Future research can expand in the following directions: First, cross-platform multimodal data fusion, comparing Weibo data with other social platforms (e.g., WeChat, TikTok) and combining multimodal information such as images, expressions, and interactive behaviors to enhance contextual awareness of social support.

Second, longitudinal monitoring and event response mechanism research, dynamically tracking changes in adolescent perceived social support under different contexts such as natural disasters, pandemics, and campus safety incidents.

Third, cultural adaptation and international comparative studies, expanding multilingual versions based on dictionary construction to promote cross-cultural applicability exploration of perceived social support measurement tools and conducting comparative studies of adolescent perceived social support between China and other countries.

Fourth, integrated application of intervention strategies, using dictionary measurement results to push personalized social support resources for individuals or groups, exploring a "digital identification—psychological intervention" closed-loop mechanism to assist in building adolescent mental health systems.

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Corresponding author: Zhihong Ren, E-mail: ren@ccnu.edu.cn

Author Contribution Statement:
Gulimire Isak and Xiayu Du proposed the research proposition and designed the study; Gulimire Isak, Xiayu Du, Binyu Wang, Jiaojiao Jia, Jiao Li, Menglu Dou, Tieyu Duan, and Qian Xiong participated in research implementation, including data collection and organization; Gulimire Isak, Xiayu Du, Binyu Wang, and Jiaojiao Jia were responsible for data cleaning and analysis; Gulimire Isak and Xiayu Du wrote the initial draft; Gulimire Isak was responsible for revising and finalizing the manuscript; Zhihong Ren provided research guidance and overall supervision, and reviewed and approved the overall structure and logic of the paper.