Extremism: a philosophical analysis,

Hart, Gavin

doi:10.1080/17539153.2022.2137081

Cited by 2 publications

(2 citation statements)

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“…A critical component of ML success in detecting extremist content lies in feature extraction, where raw data is transformed into a format that these algorithms can utilize. Techniques such as Term Frequency-Inverse Document Frequency (tf-idf) [22], Bag of Words (BoW) [23], and Word2Vec have been commonly employed for this purpose [24]. Tf-idf emphasizes the importance of words in a document, BoW assesses the frequency of words independent of the order or grammar, and Word2Vec encapsulates semantic relationships between words, by mapping them as vectors in a multidimensional space.…”

Section: B Machine Learning Methods For Online Extremism Detectionmentioning

confidence: 99%

Social Media Mining to Detect Online Violent Extremism using Machine Learning Techniques

Mussiraliyeva¹,

Bagitova²,

Sultan³

2023

IJACSA

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In this paper, we explore the challenging domain of detecting online extremism in user-generated content on social media platforms, leveraging the power of Machine Learning (ML). We employ six distinct ML and present a comparative analysis of their performance. Recognizing the diverse and complex nature of social media content, we probe how ML can discern extremist sentiments hidden in the vast sea of digital communication. Our study is unique, situated at the intersection of linguistics, computer science, and sociology, shedding light on how coded language and intricate networks of online communication contribute to the propagation of extremist ideologies. The goal is twofold: not only to perfect detection strategies, but also to increase our understanding of how extremism proliferates in digital spaces. We argue that equipping machine learning algorithms with the ability to analyze online content with high accuracy is crucial in the ongoing fight against digital extremism. In conclusion, our findings offer a new perspective on online extremism detection and contribute to the broader discourse on the responsible use of ML in society.

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Section: B Machine Learning Methods For Online Extremism Detectionmentioning

confidence: 99%

Social Media Mining to Detect Online Violent Extremism using Machine Learning Techniques

Mussiraliyeva¹,

Bagitova²,

Sultan³

2023

IJACSA

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“…Initially Reddit hosted incel user-generated dedicated forums or ‘subreddits’, r/incel and r/braincels, prior to removal for breach of usage policy in 2017 and 2019 respectively (O’Donnell and Shor 2022). These fora provided ample textual data for numerous thematic content analyses, seemingly the most frequent methodology utilized in the investigation of incels (Hart and Huber 2023). Investigations using this type of data also applied the hegemonic masculinity construct leading to further instances of slippage.…”

Section: Conceptual Slippage In Incel Researchmentioning

confidence: 99%

Slippages in the Application of Hegemonic Masculinity: A Case Study of Incels

Lucy

2024

Men and Masculinities

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Although an important theoretical tool within the field of critical study of men and masculinities, mishandling of Connell’s theory of multiple masculinities and subsequent developments frequently overlooks the relational and legitimizing components central to the hegemonic masculine construct, producing conceptual ‘slippage’. This case study demonstrates such misapplication, examining four investigations within the emerging field of research concerning the antifeminist masculine performances and ideological constructs associated with involuntary celibates (incels). Each study lacks acknowledgement and demonstration of the political mechanics of relational legitimacy that define hegemonic masculinity, producing, instead, trait analyses of non-hegemonic dominant or dominating masculine behaviour resting on fixed, often toxic character types. As novel and emergent communities of men receive scholarly attention, it is important researchers heed repeated attempts to guide correct use of theory by engaging with the history and evolution of employed concepts.

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Extremism: a philosophical analysis,

Cited by 2 publications

References 0 publications

Social Media Mining to Detect Online Violent Extremism using Machine Learning Techniques

Social Media Mining to Detect Online Violent Extremism using Machine Learning Techniques

Slippages in the Application of Hegemonic Masculinity: A Case Study of Incels

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