Identifying Abusive Comments in Hebrew Facebook

Liebeskind, Chaya; Liebeskind, Shmuel

doi:10.1109/icsee.2018.8646190

Cited by 12 publications

(2 citation statements)

References 25 publications

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“…The web-based disdain discourse is additionally expanding our online media issues. The intention is to carry out a framework that can identify and report hate to the consistent power utilizing advance AI with regular language handling Guntur Budi Herwanto ,AnnisaMaulidaNingtyas , Kurniawan EkaNugrahaz [8],If persistent sack of words (CBOW) And skip gram in a constant pack of words or (CBOW) foresee the objective word from the setting some like this and skip gram we attempt to anticipate the challenge word from the objective word, you might inquire as to for what reason are we attempting to anticipate word when we want vectors for draw word. We as a whole need a more modest model since English language has around 13 million word in the word reference this is very immense for a model.…”

Section: Literature Surveymentioning

confidence: 99%

A Review on Online Public Shaming on Social Media: Detection, Analysis and Mitigation

Mane,

Khule,

Nanaware

et al. 2023

Int. J. Res. Publ. Rev.

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Lately, public shaming on internet based informal organizations and related web-based public gatherings, for example, Twitter has expanded. These events are known to appallingly affect the casualty's social, political, and monetary prosperity. Notwithstanding the undeniable unfortunate results, little has been finished to address this in well known web-based virtual entertainment, with the support that the enormous volume and variety of such comments requires an infeasible number of human arbitrators to finish the work. We mechanize the assignment of identifying public shaming by means of Twitter according to the point of view of casualties in this examination, zeroing in on two perspectives: occasions and shamers. Oppressive, correlation, condemning, strict/ethnic, mockery/joke, and whataboutery are the six kinds of disgraceful tweets, and each tweet is arranged into one of these classifications or as non-shaming. It has been shown that most of clients who post remarks in a shaming occasion are probably going to embarrass the person in question. Shockingly, shamers' Twitter adherent counts develop speedier than those of non-shamers. At last, utilizing AI methods, for example, Backing Vector Machine and Irregular Backwoods, a web application called block Disgrace was fabricated and carried out in light of the order and characterization ofshaming tweets for on-the-fly quieting/obstructing of shamers manhandling a casualty on Twitter.

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Section: Literature Surveymentioning

confidence: 99%

A Review on Online Public Shaming on Social Media: Detection, Analysis and Mitigation

Mane,

Khule,

Nanaware

et al. 2023

Int. J. Res. Publ. Rev.

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“…It is documented that Cao et al ( 2009) and Arun et al ( 2010) metrics tend to overfit the data (Hou-Liu 2018, Gerlach et al 2018). Marginal likelihood (Griffiths & Steyvers 2004) has been widely used as a measure to specify k on large-scale social media datasets across different languages and health topics, where the topic candidate with the highest likelihood value is considered the best fit (Paul & Dretze 2012, Ma et al 2016, Zhao 2018, Liebeskind & Liebeskind 2018, Rissola et al 2019. Perplexity is often used alongside marginal likelihood as a method of crossvalidating k selection, where lower perplexity is considered better fit (Hoang 2015).…”

Section: Figure 3 Computed Metrics and Estimated K Number Of Topics Using The 'Ldatuning' R Packagementioning

confidence: 99%

Network Life of Non-biomedical Knowledge

Nguyen

2021

jdsr

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Traditional medicine is hugely popular throughout Southeast Asia and other parts of the world. The development of the internet and online social networks in these contexts has enabled a significant proliferation of non-biomedical knowledge and practices via platforms such as Facebook. People use Facebook to advocate for non- biomedical alternatives to unaffordable biomedicine, share family medical recipes, discuss medicinal properties of indigenous plants, buy and sell these plants, and even crowdsource disease diagnoses. This paper examines the network characteristics of, and discourses present within, three popular Vietnamese non-biomedical knowledge Facebook sites over a period of five years. These large-scale datasets are studied using social network analysis and generative statistical models for topic analysis (Latent Dirichlet allocation). Forty-nine unique topics were quantitatively identified and qualitatively interpreted. Among these topics, themes of religion and philanthropy, critical discussions of traditional medicine, and negotiations involving overseas Vietnamese were particularly notable. Although non-biomedical networks on Facebook are growing both in terms of scale and popularity, sub-network comment activities within these networks exhibit ‘small world’ characteristics. This suggests that social media seem to be replicating existing social dynamics that historically enable the maintenance of traditional forms of medical knowledge, rather than transforming them here.

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