Automating Protest Event Analysis by Mamaev Bogdan (20 results)

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Condition: new. Hardcover. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA). Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Condition: New. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics.…

Condition: New.

Hardback. Condition: New. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics.…

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Condition: New.

Condition: New. 2026. hardcover. . . . . .

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Condition: New. 2026. hardcover. . . . . . Books ship from the US and Ireland.

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Hardcover. Condition: Brand New. 152 pages. 5.98x2.00x9.02 inches. In Stock.

Condition: New.

Hardback. Condition: New. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics.…

Buch. Condition: Neu. Neuware - Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA). Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom. A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics.…

Hardback. Condition: New. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics.…

Buch. Condition: Neu. Automating Protest Event Analysis Using Digital Media in Contentious Politics | Bogdan Mamaev | Buch | Emerald Points | Englisch | 2026 | Emerald Publishing Limited | EAN 9781805926566 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Hardcover. Condition: Brand New. 152 pages. 5.98x2.00x9.02 inches. In Stock. This item is printed on demand.

Hardcover. Condition: new. Hardcover. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA). This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Automating Protest Event Analysis Using Digital Media in Contentious Politics addresses one of the most critical challenges in the field: the need for more efficient, reliable, and scalable data collection. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA).Investigating the transformative impact of Generative AI on data access and efficiency, Mamaev argues that state-of-the-art proprietary and open models address the cost and resource constraints of traditional manual and semi-automated approaches to PEA, enabling the creation of high-quality datasets. By employing techniques such as zero-shot classification, Named Entity Recognition (NER), and semantic deduplication, researchers can extract rigorous quantitative and qualitative data from various sources, including news archives and social media platforms. Focusing on Russia, this work explores the complexities of building the Russian Contentious Events Dataset for News and Social Media (RCED-NSM) within an authoritarian regime characterised by heavy censorship. The book also examines the limitations and biases of algorithmic tools, testing their generalisability through comparative applications in China and the United Kingdom.A pathbreaking contribution, this timely advancement in computational social science bridges interdisciplinary knowledge, offering researchers a reproducible framework to navigate the biases of digital media and utilise cutting-edge computational methods in the study of contentious politics. Using Russia as a primary case study, author Bogdan Mamaev explores how Large Language Models (LLMs) and digital media are reshaping the methodology of protest event analysis (PEA). This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…