Bullying discourse on Twitter: An examination of bully-related tweets using supervised machine learning

Identification and evaluation of educational technology trends from 2004 to 2022: Evidence based on computers in human behavior and horizon report

The increasing influence of technology on education has attracted considerable attention. This study aims to determine the current status and development trends of educational technologies. At first, we used COOC, HistCite, and VOSviewer to systematically review 1562 educational articles published in Computers in Human Behavior (CHB) from 2004 to 2022. Based on bibliometrics, this study identified publication trends, research forces, collaboration, key articles, and research themes. Then, we visualized the technologies predicted by 30 Horizon Reports and combined them with CHB educational research to evaluate the accuracy of the identified trends. The results revealed an immediate influence of AI technology, extended reality and digital resources on education, a moderate influence of educational tools and games, and a delayed influence of data management and maker technology. In addition, human psychology and behavior in technological environment may be important themes in the future. In conclusion, this study not only proposes a comparative analysis of leading reports and representative literature, but also provides guidance for future research and development in educational technology.

Identification of cyber harassment and intention of target users on social media platforms

Due to Coronavirus diseases in 2020, all the countries departed into lockdown to combat the spread of the pandemic situation. Schools and institutions remain closed and students' screen time surged. The classes for the students are moved to the digital platform which leads to an increase in social media usage. Many children had become sufferers of cyber harassment which includes threatening comments on young students, sexual torture through a digital platform, people insulting one another, and the use of fake accounts to harass others. The rising effort on automated cyber harassment detection utilizes many AI-related components Natural language processing techniques and machine learning approaches. Though machine learning models using different algorithms fail to converge with higher accuracy, it is much more important to use significant natural language processes and efficient classifiers to detect cyberbullying comments on social media. In this proposed work, the lexical meaning of the text is analysed by the conventional scheme and the word order of the text is performed by the Fast Text model to improve the computational efficacy of the model. The intention of the text is analysed by various feature extraction methods. The score for intention detection is calculated using the frequency of words with a bully-victim participation score. Finally, the proposed model's performance is measured by different evaluation metrics which illustrate that the accuracy of the model is higher than many other existing classification methods. The error rate is lesser for the detection model.

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.

Detection of fickle trolls in large-scale online social networks

Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users' data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is O ( n 3 ) which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.

Repressive Moralism: World Making and Petty Fascism in Transgender Politics

The past decade has seen a shift in the way that minorities exert their influence in society. Where in previous decades the emphasis was on winning the hearts and minds of the population at large, a recent strategy has been to ignore general public discourse and only to target specific influential bodies. In this paper we use the example of transgender issues to analyse the socio-psychological dimensions of this approach. We show how some groups promoting these issues eschew a wider social discourse and debate in the mass media, and how their strategy rests on a self-construction as victims of the hetero-normative society, with a concomitant appeal to moral rather than factual argumentation. This is combined with a programme of aggressive challenge to opponents through social media, and sometimes direct action, which effectively closes discussion on the topic. We conclude that these methods have much in common with the oppressive politics of fascist rule.

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.