Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges
Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/14/9/485 |
| _version_ | 1797579614858510336 |
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| author | Fahim Sufi |
| author_facet | Fahim Sufi |
| author_sort | Fahim Sufi |
| collection | DOAJ |
| description | Utilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated content on these platforms, ranging from eyewitness accounts to multimedia evidence, serves as invaluable resources for corroborating and contextualizing cyber attacks, facilitating the attribution of malicious actors. Furthermore, social media data afford unique access to public sentiment, the propagation of propaganda, and emerging narratives, offering profound insights into the effectiveness of information operations and shaping counter-messaging strategies. However, there have been hardly any studies reported on the Russia–Ukraine cyber war harnessing social media analytics. This paper presents a comprehensive analysis of the crucial role of social-media-based cyber intelligence in understanding Russia’s cyber threats during the ongoing Russo–Ukrainian conflict. This paper introduces an innovative multidimensional cyber intelligence framework and utilizes Twitter data to generate cyber intelligence reports. By leveraging advanced monitoring tools and NLP algorithms, like language detection, translation, sentiment analysis, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), Porter stemming, n-grams, and others, this study automatically generated cyber intelligence for Russia and Ukraine. Using 37,386 tweets originating from 30,706 users in 54 languages from 13 October 2022 to 6 April 2023, this paper reported the first detailed multilingual analysis on the Russia–Ukraine cyber crisis in four cyber dimensions (geopolitical and socioeconomic; targeted victim; psychological and societal; and national priority and concerns). It also highlights challenges faced in harnessing reliable social-media-based cyber intelligence. |
| first_indexed | 2024-03-10T22:38:41Z |
| format | Article |
| id | doaj.art-ec4c93df46c1402480796f0458987884 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T22:38:41Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ec4c93df46c1402480796f04589878842023-11-19T11:13:55ZengMDPI AGInformation2078-24892023-08-0114948510.3390/info14090485Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and ChallengesFahim Sufi0School of Public Health and Preventive Medicine, Monash University, 553 St. Kilda Rd., Melbourne, VIC 3004, AustraliaUtilizing social media data is imperative in comprehending critical insights on the Russia–Ukraine cyber conflict due to their unparalleled capacity to provide real-time information dissemination, thereby enabling the timely tracking and analysis of cyber incidents. The vast array of user-generated content on these platforms, ranging from eyewitness accounts to multimedia evidence, serves as invaluable resources for corroborating and contextualizing cyber attacks, facilitating the attribution of malicious actors. Furthermore, social media data afford unique access to public sentiment, the propagation of propaganda, and emerging narratives, offering profound insights into the effectiveness of information operations and shaping counter-messaging strategies. However, there have been hardly any studies reported on the Russia–Ukraine cyber war harnessing social media analytics. This paper presents a comprehensive analysis of the crucial role of social-media-based cyber intelligence in understanding Russia’s cyber threats during the ongoing Russo–Ukrainian conflict. This paper introduces an innovative multidimensional cyber intelligence framework and utilizes Twitter data to generate cyber intelligence reports. By leveraging advanced monitoring tools and NLP algorithms, like language detection, translation, sentiment analysis, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), Porter stemming, n-grams, and others, this study automatically generated cyber intelligence for Russia and Ukraine. Using 37,386 tweets originating from 30,706 users in 54 languages from 13 October 2022 to 6 April 2023, this paper reported the first detailed multilingual analysis on the Russia–Ukraine cyber crisis in four cyber dimensions (geopolitical and socioeconomic; targeted victim; psychological and societal; and national priority and concerns). It also highlights challenges faced in harnessing reliable social-media-based cyber intelligence.https://www.mdpi.com/2078-2489/14/9/485cyber analyticsanalyzing cyber threatcyber warsocial media analyticsRussian cyber incidentUkrainian cyber incident |
| spellingShingle | Fahim Sufi Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges Information cyber analytics analyzing cyber threat cyber war social media analytics Russian cyber incident Ukrainian cyber incident |
| title | Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges |
| title_full | Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges |
| title_fullStr | Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges |
| title_full_unstemmed | Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges |
| title_short | Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges |
| title_sort | social media analytics on russia ukraine cyber war with natural language processing perspectives and challenges |
| topic | cyber analytics analyzing cyber threat cyber war social media analytics Russian cyber incident Ukrainian cyber incident |
| url | https://www.mdpi.com/2078-2489/14/9/485 |
| work_keys_str_mv | AT fahimsufi socialmediaanalyticsonrussiaukrainecyberwarwithnaturallanguageprocessingperspectivesandchallenges |