"Brute-Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection"

Ptaszyński, Michał; Lempa, Paweł; Masui, Fumito; Kimura, Yumi; Rzepka, Rafał; Araki, Kenji; Wroczyński, Michał; Leliwa, Gniewosz

doi:10.17705/1jais.00562

Cited by 14 publications

(12 citation statements)

References 3 publications

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“…A known way to avoid over-consolidation is to use the lempos format (lemma with POS tag, e.g., “set NOUN”), as implemented by Akhtar, Sahoo, and Kumar (2017) and Ptaszynski et al . (2019). Moreover, some words require context to determine its true base form or lemma.…”

Section: Commonly Used Text Preprocessing Practicesmentioning

confidence: 99%

“…Both methods suffer from over-consolidation of words-assigning two word forms with clearly different meanings to the same token, such as mapping both "computer" and "computation" to the token "comput" (Jivani 2011). A known way to avoid over-consolidation is to use the lempos format (lemma with POS tag, e.g., "set_NOUN"), as implemented by Akhtar, Sahoo, and Kumar (2017) and Ptaszynski et al (2019). Moreover, some words require context to determine its true base form or lemma.…”

Section: Known Issuesmentioning

confidence: 99%

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Comparison of text preprocessing methods

Chai

2022

Nat. Lang. Eng.

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Text preprocessing is not only an essential step to prepare the corpus for modeling but also a key area that directly affects the natural language processing (NLP) application results. For instance, precise tokenization increases the accuracy of part-of-speech (POS) tagging, and retaining multiword expressions improves reasoning and machine translation. The text corpus needs to be appropriately preprocessed before it is ready to serve as the input to computer models. The preprocessing requirements depend on both the nature of the corpus and the NLP application itself, that is, what researchers would like to achieve from analyzing the data. Conventional text preprocessing practices generally suffice, but there exist situations where the text preprocessing needs to be customized for better analysis results. Hence, we discuss the pros and cons of several common text preprocessing methods: removing formatting, tokenization, text normalization, handling punctuation, removing stopwords, stemming and lemmatization, n-gramming, and identifying multiword expressions. Then, we provide examples of text datasets which require special preprocessing and how previous researchers handled the challenge. We expect this article to be a starting guideline on how to select and fine-tune text preprocessing methods.

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Section: Commonly Used Text Preprocessing Practicesmentioning

confidence: 99%

Section: Known Issuesmentioning

confidence: 99%

Comparison of text preprocessing methods

Chai

2022

Nat. Lang. Eng.

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“…Another one was collected from Wykop 2 , which is a Polish social networking service. As feature selection and feature engineering have been proven to be integral parts of cyberbullying detection [35,59], the entries are provided as such, without additional preprocessing to allow researchers using the datasets apply their own preprocessing methods. The only preprocessing applied to the dataset was done only to mask private information, such as personal information of individuals (usernames, etc.…”

Section: Polish Datasetmentioning

confidence: 99%

Transfer language selection for zero-shot cross-lingual abusive language detection

Eronen

Ptaszyński

Masui

et al. 2022

Information Processing & Management

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“…Machine learning is one popular methodology for studying user‐generated content (Larsen et al ., 2019; Lukyanenko et al ., 2017; Ptaszynski et al ., 2019). Research shows that this method can support companies to recognize applicable content generated by social media (Vermeer et al ., 2019).…”

Section: Data Analysis: Detecting Ai‐enabled Social Bots On Twittermentioning

confidence: 99%

Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence

Hajli

Saeed

Tajvidi

et al. 2021

British J of Management

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Artificial intelligence (AI) is creating a revolution in business and society at large, as well as challenges for organizations. AI‐powered social bots can sense, think and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, such as providing wrong information to people, escalating arguments, perpetrating scams and exploiting the stock market. As such, an understanding of different kinds of social bots and their authors’ intentions is vital from the management perspective. Drawing from the actor‐network theory (ANT), this study investigates human and non‐human actors’ roles in social media, particularly Twitter. We use text mining and machine learning techniques, and after applying different pre‐processing techniques, we applied the bag of words model to a dataset of 30,000 English‐language tweets. The present research is among the few studies to use a theory‐based focus to look, through experimental research, at the role of social bots and the spread of disinformation in social media. Firms can use our tool for the early detection of harmful social bots before they can spread misinformation on social media about their organizations.

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"Brute-Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection"

Cited by 14 publications

References 3 publications

Comparison of text preprocessing methods

Comparison of text preprocessing methods

Transfer language selection for zero-shot cross-lingual abusive language detection

Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence

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