Social big data analysis of future signals for bullying in South Korea: Application of general strain theory

Song, Jong Tae; Han, Yoonsun; Kim, Kwanghyun; Song, Tae Min

doi:10.1016/j.tele.2020.101472

Cited by 11 publications

(5 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study further deepen our understanding of food insecurity and bullying victimization, including the impact of food insecurity and predictors of bullying victimization, which would provide implications for theory and practice. The findings of the current study are consistent with conceptual frameworks assuming individual's SES potentially serves as a strong predictor of bullying victimization (18,21). Although theories, such as social capital theory (21), have mentioned that social status could predict bullying involvement, they stress more on bullying perpetration than bullying victimization.…”

Section: Theoretical and Practical Implicationssupporting

confidence: 87%

“…Based on these theories, socioeconomic status (SES) may be an important factor in explaining bullying. Specifically, grounded in general strain theory, experiencing economic problems was identified as a strong signal of bullying by social big data analysis ( 18 ). According to social disorganization theory, environments with a high concentration of poverty are more likely to experience disorganization and may increase the risk of developing attitudes and behaviors that are related to bullying ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Food Insecurity and Bullying Victimization Among 170,618 Adolescents in 59 Countries

et al. 2021

View full text Add to dashboard Cite

Background: Bullying victimization can impose long-lasting impacts on adolescent's current and subsequent well-being. Understanding the correlates of bullying victimization and how to prevent its occurrence is an urgent need. Food insecurity, an indicator of low socioeconomic status, may be related to bullying victimization. However, research on the association between food insecurity and bullying victimization is limited. Using a representative global sample, this study aimed to investigate the association between food insecurity and bullying victimization in adolescents and whether the association varied between country income levels, sexes, and age groups.Methods: Using cross-sectional, school-based data by 170,618 adolescents in 59 countries from the Global School-based Student Health Survey, multivariable logistic regression and meta-analysis were conducted to investigate the overall, country-stratified, sex-stratified, and age-stratified associations.Results: The prevalence of bullying victimization was 33.3%. In the total sample, adolescents with food insecurity showed significantly higher odds for bullying victimization than those without no food insecurity with a pooled odds ratio (OR) being 1.37 (1.28, 1.47). Further, the association was stronger in higher-income countries, females, and older adolescents.Conclusions: Bullying victimization is prevalent among global adolescents with food insecurity being a significant correlate. The identification of adolescents with perceptions of food insecurity and remedying this condition may be important to reduce the prevalence of bullying. This highlights the need to design and implement sex- and age-specific interventions focusing on remedying food insecurity and bullying victimization among in-school adolescents by taking country income levels into account.

show abstract

Section: Theoretical and Practical Implicationssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Food Insecurity and Bullying Victimization Among 170,618 Adolescents in 59 Countries

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We then calculated the degree of visibility (DoV), and the degree of diffusion (DoD) [37]. The following equations are used to calculate DoV and DoD.…”

Section: Future Signal Analysismentioning

confidence: 99%

“…NN: total number of documents; TF: term frequency; DF: document frequency; tw: time weight (time weight of 0.05 was applied); n: entire time segment; j: time point; "{1 − tw × (n − j)}" is a function that weakens its influence as time passes, thus determining the scale of the time weight [37]. Keyword emergence map (KEM) and Keyword Issue Map (KIM) were examined by visualizing the frequency of emergence and the time-weighted rate of the increase in topics [35,37].…”

Section: Future Signal Analysismentioning

confidence: 99%

Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data

Song

Jin

Song

et al. 2023

IJERPH

Self Cite

View full text Add to dashboard Cite

COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion. To explore future signals, we analyzed the term frequency and document frequency of key factors occurring in the tweets, analyzing the degree of visibility and degree of diffusion. Depression, digestive symptoms, inspection, diagnosis kits, and stay home obesity had high frequencies. The increase in the degree of visibility was higher than the median value, indicating that the signal became stronger with time. The degree of visibility of the mean word frequency was high for disinfectant, healthcare, and mask. However, the increase in the degree of visibility was lower than the median value, indicating that the signal grew weaker with time. Infodemic had a higher degree of diffusion mean word frequency. However, the mean degree of diffusion increase rate was lower than the median value, indicating that the signal grew weaker over time. As the general flow of signal progression is latent signal → weak signal → strong signal → strong signal with lower increase rate, it is necessary to obtain active response strategies for stay home, inspection, obesity, digestive symptoms, online shopping, and asymptomatic.

show abstract

“…TFij is the total occurrence frequency of a keyword i in the period j, NNj is the total number of articles in the period j, whereas n is the number of periods, and tw is the time-weight [20].…”

Section: Degree Of Visibility (Dov)mentioning

confidence: 99%

Big-data Analytics: Exploring the Well-being Trend in South Korea Through Inductive Reasoning

2021

KSII TIIS

View full text Add to dashboard Cite

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning -namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

show abstract

Social big data analysis of future signals for bullying in South Korea: Application of general strain theory

Cited by 11 publications

References 81 publications

Food Insecurity and Bullying Victimization Among 170,618 Adolescents in 59 Countries

Food Insecurity and Bullying Victimization Among 170,618 Adolescents in 59 Countries

Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data

Big-data Analytics: Exploring the Well-being Trend in South Korea Through Inductive Reasoning

Contact Info

Product

Resources

About