Donetsk don’t tell – ‘hybrid war’ in Ukraine and the limits of social media influence operations

Maschmeyer, Lennart; Abrahams, Alexei; Pomerantsev, Peter; Yermolenko, Volodymyr

doi:10.1080/19331681.2023.2211969

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…Therefore, in this case, the mass media is closely related among people's social life. Narratives built by the media can build public opinion (Maschmeyer et al, 2023).…”

Section: Mass Media and Narrativementioning

confidence: 99%

Narrative policy of bureaucratic reform in Indonesia: Rules of narrative in mass media

Haryono,

Nugroho,

Putera

et al. 2023

J. Infras. Policy. Dev.

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This paper aims to analyze the narratives that have emerged in the process of bureaucratic reform in Indonesia. The analysis is conducted using the Narrative Policy Framework at the mesa level. Using data from articles published in 6 credible national media about “bureaucratic reform” from 2010 to 2023. The collected data was classified according to the Narrative Policy Framework (NPF) elements in the article: Issue setting, the cause of the issue, plot, character (villain, victim, hero), and recommendations for solutions offered. There were 31 articles analyzed. The result showed that the main plot in the process of bureaucratic reform in Indonesia is based on the corrupt bureaucracy and the slow public service provided. The victims in the plot are the people who will access the services. The villains of the narrative are civil servants who do not improve the required competencies. The heroes of the narrative are several government institutions (Ministry of State Apparatus Utilization and Bureaucratic Reform, Commission of Corruption Eradication, and The Audit Board of The Republic of Indonesia) that are considered to expose the problem.

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“…Therefore, in this case, the mass media is closely related among people's social life. Narratives built by the media can build public opinion (Maschmeyer et al, 2023).…”

Section: Mass Media and Narrativementioning

confidence: 99%

Narrative policy of bureaucratic reform in Indonesia: Rules of narrative in mass media

Haryono,

Nugroho,

Putera

et al. 2023

J. Infras. Policy. Dev.

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“…This can lead to a distorted understanding of the conflict, hinder diplomatic efforts, and exacerbate tensions. Furthermore, spreading fake news in conflict zones can have dire humanitarian consequences, as it can incite violence, cause panic, and impede aid delivery (Maschmeyer et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

A novel approach to fake news classification using LSTM-based deep learning models

Padalko,

Chomko,

Chumachenko

2024

Front. Big Data

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The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.

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“…Larson et al (2009) argue that these operations influence a target audience, whether an individual leader, members of a decision-making group, military organizations and personnel, specific population subgroups, or mass publics (p. 2). In general, however, influence operations refer to intelligence operations that interfere in the affairs of another actor (Callanan, 2009;Maschmeyer et al, 2023). Many researchers argue that what is important is that the decision-making capabilities are interfered with through influence operations and information warfare.…”

Section: Introductionmentioning

confidence: 99%

Twitter Propaganda Operations: Analyzing Sociopolitical Issues in Saudi Arabia

Albert,

Aleroud,

Yang

et al. 2023

Social Media + Society

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The purpose of this article is to explore Arabic-language Tweets based out of Saudi Arabia to investigate the social media landscape. Specifically, this article seeks to address the question, “What thematic issues concerning the U.S. socio-political landscape are present in Arabic-language Twitter postings?” And, “To what extent can these issues be described as propagandic in nature?” To do so, we propose a machine-learning and artificial intelligence span detection approach to identify propaganda Tweets in Middle Eastern Countries, with a focus on Saudi Arabia. As opposed to previous work, this article maps and investigates different propaganda categories using the BEND Social Cyber Security framework. This article then proceeds to a case study analysis of state-sponsored targeted propaganda from Saudi Arabia and briefly describes the categories of propaganda uncovered. We then relate those categories to the BEND Framework and conclude with policy recommendations and discussion.

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Donetsk don’t tell – ‘hybrid war’ in Ukraine and the limits of social media influence operations

Cited by 7 publications

References 27 publications

Narrative policy of bureaucratic reform in Indonesia: Rules of narrative in mass media

Narrative policy of bureaucratic reform in Indonesia: Rules of narrative in mass media

A novel approach to fake news classification using LSTM-based deep learning models

Twitter Propaganda Operations: Analyzing Sociopolitical Issues in Saudi Arabia

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