Technology Threats:Impacts of Cyberbullying to Today's Generation

Keitemoge, Pearl

doi:10.1109/icsssm.2018.8464953

Cited by 2 publications

(2 citation statements)

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“…Word and characters were taken as token for TF-IDF feature extraction [21]. Features were extracted using TF-IDF along with sentiment analysis to design the cyberbullying detection model designed by Hani et al [1].Analysis of tweets to identify bully and non-bully tweets was performed using TF-IDF vectorization by Keitemoge et al [58]. TF-IDF is considered to be a simple and proven method in text classification [5].…”

Section: Tf-idf(term Frequency-inverse Frequency) Is a Commonly Used ...mentioning

confidence: 99%

Cyberbullying Detection using Ensemble Method

Puthenveedu¹

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Section: Tf-idf(term Frequency-inverse Frequency) Is a Commonly Used ...mentioning

confidence: 99%

Cyberbullying Detection using Ensemble Method

Puthenveedu¹

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“…Semantic analysis of OSN published content is a current hot research area that allows to detect and prevent undesirable uses of the OSN. For instance, the semantic analysis at word level has been reported to allow to detect cyberbullying [ 30 ], helps detecting drunken tweets [ 24 ], and the age of users [ 56 ]. Also, social media posts content analysis allows to predict depression levels [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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Technology Threats:Impacts of Cyberbullying to Today's Generation

Cited by 2 publications

References 10 publications

Cyberbullying Detection using Ensemble Method

Cyberbullying Detection using Ensemble Method

Neuro-semantic prediction of user decisions to contribute content to online social networks

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