Cyberbullying Detection on Twitter using Multiple Textual Features

Zhang, Jianwei; Otomo, Taiga; Li, Lin; Nakajima, Shinsuke

doi:10.1109/icawst.2019.8923186

Cited by 19 publications

(8 citation statements)

References 8 publications

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“…Other common features usually considered for the detection of cyberbullying are profanity [ 15 , 16 , 17 , 18 ] or sentiment analysis [ 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

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The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeawareprecision (TaP) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.

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“…Other common features usually considered for the detection of cyberbullying are profanity [ 15 , 16 , 17 , 18 ] or sentiment analysis [ 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

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“…They discovered that indirect speech acts, usually manifesting as one's adoption of the interrogative mood, were more common in Eastern settings than direct speech acts. Zhang et al (2019) found that bullying words were useful for classifying cyberbullying in Japan, with informal language and emerging words in tweets affecting the results of sentiment analysis. Research from Pakistan showed that cyberbullies attacked the victim's appearance through comparisons and certain discourse markers (e.g., capitalization, punctuation, and mathematical symbols; Rafi, 2019).…”

Section: Linguistic Features Of Cyberbullyingmentioning

confidence: 99%

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

et al. 2022

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As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern—especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens.

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“…Both methods are used to learn the usage context of a word. Global vector space [20], [25], [26], [27], [28], [29], [30] Distance Measures Edit-Distance [20] Word Embedding Word2Vec [31], [32], [28], [33], [34], [35], [30], [36], [37], [38], [39] Skip-gram [25] CBoW [25], [32] BoW [40], [31], [33], [34] TF-IDF [26], [27] FastText [25], [36] GLoVe [41], [42], [37] LSHWE [37] Vulgarity/Hate Features [43], [25], [32], [33], [44] Sentiment Sentiment Analysis [27], [45], [32], [33], [41], [46], [30], [39] User Profile [27],…”

Section: Word Embedding Techniquesmentioning

confidence: 99%

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Arif

2021

JISCR

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Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

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Cyberbullying Detection on Twitter using Multiple Textual Features

Cited by 19 publications

References 8 publications

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

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