The Logos of the Blogosphere: Flooding the Zone, Invention, and Attention in the Lott Imbroglio

Pfister, Damien Smith

doi:10.1080/00028533.2011.11821743

Cited by 11 publications

(2 citation statements)

References 40 publications

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“…As Collin Brooke ( 2009) notes, new media texts foreground both "a more social model of invention" and "a model that is concerned more with practice than product" (82). For example, in a case of blogging by citizen-journalists, Damien Pfister (2011) argues that a "fundamental contribution that bloggers make to public deliberation" is "the invention of novel arguments. It is not just that bloggers simply pay attention to certain issues, thus directing the focus of the press; it is their ability to (occasionally) invent arguments worth taking up in broader spheres of public engagement" (152).…”

Section: Inventionmentioning

confidence: 99%

Digital rhetoric: theory, method, practice

2015

Choice Reviews Online

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Section: Inventionmentioning

confidence: 99%

Digital rhetoric: theory, method, practice

2015

Choice Reviews Online

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“…Computational propaganda, which uses technical means to disseminate information and create propaganda, has made it easier for individuals and groups to spread propaganda on a larger scale [1]. The proliferation of computational propaganda in social media platforms has raised significant concerns about the manipulation of public discourse and the potential influence on political, social, and cultural narratives [2]. Over 81 different nations have been manipulated over social media.…”

Section: Introductionmentioning

confidence: 99%

Munir: Weakly Supervised Transformer for Arabic Computational Propaganda Detection on Social Media

Bodor Moheel Almotairy

2024

jes

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The intentional manipulation of public opinion has become prevalent in the realm of computational propaganda. The swift spread of misinformation through social networking sites poses significant challenges for governments and society, impacting various aspects of human life. Arab countries are considered among the most affected countries. Accurately identifying and countering computational propaganda is crucial, especially given the impracticality of manually annotating large volumes of social media-generated data. Moreover, the constant propagandists' evolving tactics pose a challenge to accounting models, making the immediate preparation of responsive training data difficult. To address this issue, this research proposes a novel weakly supervised learning approach, leveraging programmatic labeling to label training data in a systematic and timely manner. New labeling functions (LFs) are introduced, where experts' heuristics, knowledge, new proposed lexicons, different fine-tuned pre-trained models are turned into rules to label the data. Leveraging these LFs, we fine-tune a deep learning model for computational propaganda detection. The proposed model achieves a remarkable 94% accuracy and 86% precision in the minority class, outperforming a fine-tuned, fully supervised deep learning model. This research contributes a substantial dataset, a robust weakly supervised model, and lexicons, offering valuable tools for combating computational propaganda on Arabic social media. The code and the dataset are publicly available at https://github.com/Bmalmotairy/Arabic-Propaganda-Detection.

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