OMG U #Cyberbully! An Exploration of Public Discourse About Cyberbullying on Twitter

An NLP-assisted Bayesian time-series analysis for prevalence of Twitter cyberbullying during the COVID-19 pandemic

COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data were also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.

Clinical Efficacy of Immunoglobulin Combined with Glucocorticoids in the Treatment of Oculomotor Myasthenia Gravis in Children and the Effect on Serum Immunity

To investigate the effects of treatment with immunoglobulin on clinical outcomes and immune function in children with oculomotor myasthenia gravis. The clinical data of 100 pediatric patients with oculomotor myasthenia gravis treated in our hospital from January 2019 to December 2021 were selected as the subjects of this retrospective study and divided into a comparison group and a treatment group according to the different treatment methods. The comparison group was treated with glucocorticoids, and the treatment group was treated with immunoglobulin on the basis of the comparison group. The differences in the serum indexes, the effects of immune function, and the clinical efficacy of the two groups were observed and compared. It was found the comparison of immunoglobulin G (IgG), immunoglobulin A (IgA), and immunoglobulin M (IgM) after treatment was significantly different and lower in the treatment group than in the comparison group; the comparison of CD4+, CD3+, CD4+/CD8+, and NK cells after treatment was significantly different and higher in the treatment group than in the comparison group. The effective rate of 98.00% in the treatment group was significantly higher than that of 76.00% in the comparison group, and the difference was statistically significant. The clinical efficacy of the two groups showed that the fever, cough, sputum, myasthenia gravis crisis, and gastrointestinal reactions in the treatment group were significantly lower than those in the comparison group. The study indicates that comparative study of children with oculomotor myasthenia gravis treated with immunoglobulin combined with glucocorticoids is more effective, effectively improving the immune level of patients and reducing adverse reactions.

Xenophobic Bullying and COVID-19: An Exploration Using Big Data and Qualitative Analysis

Extant literature suggests that xenophobic bullying is intensified by isolated national or global events; however, the analysis of such occurrences is methodologically limited to the use of self-reported data. Examining disclosures of racist bullying episodes enables us to contextualize various perspectives that are shared online and generate insights on how COVID-19 has exacerbated the issue. Moreover, understanding the rationale and characteristics present in xenophobic bullying may have important implications for our social wellbeing, mental health, and inclusiveness as a global community both in the short and long term. This study employs a mixed-method approach using Big Data techniques as well as qualitative analysis of xenophobic bullying disclosures on Twitter following the spread of COVID-19. The data suggests that about half of the sample represented xenophobic bullying. The qualitative analysis also found that 64% of xenophobic bullying-related tweets referred to occasions that perpetuated racist stereotypes. Relatedly, the rationale for almost 75% of xenophobic bullying incidents was due to being Chinese or Asian. The findings of this study, coupled with anti-hate reports from around the world, are used to suggest multipronged policy interventions and considerations of how social media sites such as Twitter can be used to curb the spread of misinformation and xenophobic bullying.

How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media

The rise of cyberbullying has been of great concern for the general public. This study aims to explore public attitudes towards cyberbullying on Chinese social media. Cognition and emotion are important components of attitude, and this study innovatively used text analysis to extract the cognition and emotion of the posts. We used a web crawler to collect 53,526 posts related to cyberbullying in Chinese on Sina Weibo in a month, where emotions were detected using the software “Text Mind”, a Chinese linguistic psychological text analysis system, and the content analysis was performed using the Latent Dirichlet Allocation topic model. Sentiment analysis showed the frequency of negative emotion words was the highest in the posts; the frequency of anger, anxiety, and sadness words decreased in turn. The topic model analysis identified three common topics about cyberbullying: critiques on cyberbullying and support for its victims, rational expressions of anger and celebrity worship, and calls for further control. In summary, this study quantitatively reveals the negative attitudes of the Chinese public toward cyberbullying and conveys specific public concerns via three common topics. This will help us to better understand the demands of the Chinese public so that targeted support can be proposed to curb cyberbullying.

Bullying-Related Tweets: a Qualitative Examination of Perpetrators, Targets, and Helpers

Bullying literature notes that aside from the dyadic relationship of target and perpetrator, there are other participant roles in the bullying process including those that reinforce the perpetrator and those that stand up for the target. Most examinations of bullying roles have relied on self-reported data, which suffer from key limitations such as response and recall bias. Twitter data provides a way to overcome these limitations and extend our current understanding of bullying roles. The current study provides one of the first qualitative examinations of tweets to analyze the disclosure and sharing of bullying-related online and offline episodes. Through a qualitative content analysis, the study examines 780 tweets to analyze the descriptions and characteristics of three participant roles: the perpetrator, target, and helper. The results provide multidimensional insights into the context and relationships between bullying roles. The results reveal that each of the bullying role players tweet to share varying perspectives and the discussions transcend beyond just online exchanges. The results also confirm that Twitter is used not only as a channel for bullying but also as a tool for connection between the different role players. Implications of how Twitter can be leveraged to promote anti-bullying initiatives to educate and inform users about bullying, while also helping build resilience and emotional regulation, are discussed. Additionally, the study also has implications for artificial intelligence and can help to build improved classifiers to detect bullying-related discourse and content online.

Bullying and Peer Victimization of Minority Youth: Intersections of Sexual Identity and Race/Ethnicity

BACKGROUND

Youth with multiple minority identities, such as those who are both sexual minority (eg, lesbian, gay, bisexual) and racial/ethnic minority (eg, Black, Latino) may be at increased risk for bullying and peer victimization.

METHODS

Youth Risk Behavior Surveillance data (2011-2017) were analyzed (N = 114,881; 50.8% girls; mean age = 15.7 years, SD = 0.03). We used chi-square tests and sex-stratified multiple linear regression models to examine sexual identity and racial/ethnic differences and the intersection between sexual identity and race/ethnicity across 3 forms of bullying and peer victimization, co-occurrence of traditional and electronic bullying, and any type of bullying or peer victimization.

RESULTS

Sexual minority youth reported higher odds of bullying and peer victimization than heterosexual youth. White youth reported higher odds of bullying than racial/ethnic minority youth. In intersectional analyses, all sexual minority and racial/ethnic minority boys, and bisexual racial/ethnic minority girls were at higher risk for bullying and peer victimization compared to heterosexual peers of the same race/ethnicity.

CONCLUSIONS

This study of a large diverse sample of youth advances our understanding of vulnerability to bullying and peer victimization among youth with multiple minority identities. This research can inform policy initiatives and interventions to prevent peer victimization of vulnerable youth.

Inferring Opinions and Behavioral Characteristics of Gay Men with Large Scale Multilingual Text from Blued

Gay men in many countries are increasingly using geosocial networking applications (GSN apps), thus offering new opportunities for understanding them. This paper provides a comprehensive content analysis of posts and opinions on Blued, the world's largest gay social networking dating app, to infer and compare opinions and behavioral characteristics of gay men in different countries. Machine learning and linguistic programming approaches were used to extract themes and analyze sentiments of posts. The results show that the majority of posts are related to daily life activities, and less are related to sensitive topics. While most posts are positive or neutral, negative emotions, including anxiety, anger, and sadness, are mainly distributed in posts related to self-identification and sexual behaviors in China and to relationships in other countries. Voting items indicate that only 50.52% of the participants will take regular HIV tests while 50.2% would have casual sex when they are single. Additionally, 35.8% of the participants may try drugs when invited by friends. Our findings suggest an opportunity and necessity for researchers and public health practitioners to use open source data on GSN apps and other social medias to inform HIV interventions and to promote social inclusion for sexual minorities.