An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments

Dessı̀, Danilo; Recupero, Diego Reforgiato; Sack, Harald

doi:10.3390/electronics10070779

Cited by 21 publications

(40 citation statements)

References 49 publications

(75 reference statements)

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“…SA models, methods, and techniques have been successfully applied in the context of text analytics; in applications such as the analysis of reviews on products and services [128,144]; in the analysis of social media posts in Twitter [145,146], Facebook [147], Instagram [148], etc. ; in the detection of social spam to prevent normal users from being unfairly overwhelmed with unwanted or fake content via social media [149]; in the detection or irony, sarcasm, and satire in formal and informal text [150,151]; in the detection of sexism, racism, bullying, harassment, and hate speech [152][153][154][155][156]; in influence and reputation analysis [157,158]; in political [159,160], social [161,162], and economic analysis [163,164]; in security monitoring [165]; and in health and well-being analyses [166,167].…”

Section: Sentiment Analysis As a Base Component For Text Analyticsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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Section: Sentiment Analysis As a Base Component For Text Analyticsmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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“…In this respect, the word vectors that share some regularities are located nearly in the vector space. According to Dessì et al [54], "the idea behind this algorithm is to model the context of words by exploiting ML and statistics to represent and come up with a vector representation for each word within the corpus. The resulting word vector representations allow the recognition of relatedness between words".…”

Section: Word2vecmentioning

confidence: 99%

Figurative Language in Atypical Contexts: Searching for Creativity in Narco Language

Reyes

Arreola

2022

Applied Sciences

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Literal language is commonly defined in terms of direct meaning, i.e., any literal utterance must convey a unique meaning. Such meaning has to be the one conventionally accepted to guarantee a successful communication. Figurative language, on the other hand, could be regarded as the opposite of literal language. Thus, whereas the latter is assumed to communicate a direct and explicit meaning, figurative language is related to the communication of veiled or implicit meanings. For instance, the word pozolero (stewmaker), which literally refers to a person who cooks a traditional Mexican food, when it is used in a figurative utterance, it can refer to different concepts, which are hardly related to food. Therefore, it can work instead of hitman, murderer, drug dealer, and others, in such a way its literal meaning is intentionally deviated in favor of secondary interpretations. In this regard, we are focused on analyzing the use of figurative language in an atypical context: drug trafficking. To this end, a corpus about narco language in Spanish was built. This corpus was used to train a word embedding model to identify creative ways to name narco-related concepts. The results show that various concepts are commonly expressed through figurative devices, such as metaphor, metonymy, or mental imagery. This fact corroborates that figurative language is quite recurrent in our daily communication, regardless of the context. In addition, we show how this creativity can be recognized by applying Natural Language Processing (NLP) techniques.

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“…Deep Learning (DL) technique has been used by the authors of [1] and [5]. The main goal of the papers is to ease online communication on textual platforms without being hurt by insults, harassment, and fake news.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection

Yazdizadeh¹,

Shi²

2022

Data Science and Machine Learning

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Communication using modern internet technologies has revolutionized the ways humans exchange information.. Despite the numerous advantages offered by such technology, its applicability is still limited due to problems stemming from personal attacks and pseudoattacks. On social media platforms, these toxic contents may take the form of texts (e.g., online chats, emails), speech, and even images and movie clips. Because the cyberbullying of an individual via the use of such toxic digital content may have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect, using machine learning approaches, cyberbullying on social media. It is important to use word embedding techniques to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words. The extracted embeddings are used to decide if a digital input contains cyberbullying contents. Supplying strong word representations to classification methods is a key facet of such detection approaches. In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, using three basic machine learning models and four deep learning models. The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.

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An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments

Cited by 21 publications

References 49 publications

Sentiment Analysis for Fake News Detection

Sentiment Analysis for Fake News Detection

Figurative Language in Atypical Contexts: Searching for Creativity in Narco Language

Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection

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