Supplementary Materials: Prigozhin's Propaganda Team: The St Petersburg Internet Research Agency (2013–2021)

Supplementary Materials For the Research Project Prigozhin’s Propaganda Team: The St Petersburg Internet Research Agency (2013–2021) Serge Poliakoff ab *, Toepfl, F. b a Department of Media Studies, University of Amsterdam, Amsterdam, the Netherlands b Chair of Political Communication, University of Passau, Passau, Germany *Corresponding author: Postbus 94550, 1090 GN Amsterdam. Email: [email protected] Serge Poliakoff ( https://orcid.org/0000-0002-8875-9008 ) is a Postdoctoral Researcher at the Department of Media Studies of the University of Amsterdam and a former Researcher at the European Research Council (ERC) Consolidator Project on “The Consequences of the Internet for Russia’s Informational Influence Abroad” (RUSINFORM, 2019-2025) at the University of Passau. His research interests are in OSINT methods, Russia's informational influence, disinformation and propaganda. Florian Toepfl ( https://orcid.org/0000-0001-5773-779X ) is a Professor at the University of Passau, Germany, where he holds the Chair of Political Communication with a Focus on Eastern Europe and the Post-Soviet Region. He is Principal Investigator of an European Research Council (ERC) Consolidator Project on “The Consequences of the Internet for Russia’s Informational Influence Abroad” (RUSINFORM, 2019-2025). His research is grounded in qualitative, quantitative, and computational methods of social science. Funding This article has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 819025). It is part of the ERC consolidator project on ‘The Consequences of the Internet for Russia’s Informational Influence Abroad’ ( www.rusinform.uni-passau.de/en ) 2 Contents I. Additional Analyses 3 Tables and Figures 3 IRA-affiliated Groups and Channels on Social Networking Sites Mentioned in the CVs 16 II. Description of the Procedures of Data Collection/Analysis and the Data Set 19 Part 1. Data Collection 19 Part 2. Coding Procedure 22 Units of data collection 22 Data scraping procedure 22 Manual coding 22 Part 3. Description of the JSON Data Set Variables 23 Part 4. Instructions for Coders on How to Manually Group Variables 31 IRA Job Type 31 Job Type (non-IRA) 32 Part 5. Instructions for Coders on Data Cleaning 35 Level of Education 35 International experience 36 City of the University 36 University Geographical Distribution 39 Field of Study 39 Part 6. Instructions for Coders on How to Manually Code Variables 42 Organisation Type 42 Part 7. Overview of all Variables in the Data Set 44 3 Supplementary Materials For the Research Project Prigozhin’s Propaganda Team: The St Petersburg Internet Research Agency (2013–2021) I. Additional Analyses Tables and Figures Table 1. Number of Work Experiences Mentioned in the CV Data Set by Legal Entity Comprising the IRA Name of the legal entity % n LLC Internet Research 21.05% 84 LLC MixInfo 20.55% 82 LLC GlavSet' 15.54% 62 LLC MediaSintez 5.01% 20 LLC Internet Research Agency 3.01% 12 LLC Azimut 1.50% 6 LLC NovInfo 18.80% 75 Inforeactor 4.26% 17 PolitExpert 4.01% 16 PolitRossiya 2.76% 11 Slovo i Delo 2.01% 8 NewInform 1.50% 6 Grand Total 100% 399 Note. N of total work experience at all IRA entities = 399. In our dataset we have 350 CVs, of which 305 have one IRA work experience entry, 37 have 2 IRA entries, 7 have 3 entries and 1 has 4 entries 4 Figure 1. The proportion of CVs Last Updated in Any Month by Month (N=350) 5 Table 2. Self-declared Language Proficiency Note. N = 350 CVs. 6 Figure 2. Headcount of Legal Entities Over Time 7 8 9 Note. Dates of legal registration and legal termination of these organisations in Russia’s Unified State Register of Legal Entities are highlighted in the figures. Novinfo LLC and its registered media are still active at the time of data collection (March 2022), so only the legal registration date is shown. Please note that we stopped reporting figures in March 2021, one year before the month in which we downloaded the CV, as data for the last year is skewed by the fact that many individuals report having left the IRA shortly 16 IRA-affiliated Groups and Channels on Social Networking Sites Mentioned in the CVs To illustrate the reach of groups and channels, data on followers/subscribers were collected as of 17 February 2023. AKULA (2 mentions) ● Vkontakte: https://vk.com/akula.media (721,464 followers) ● Telegram: https://t.me/akula_vide (51,498 subscribers) ● Instagram https://www.instagram.com/akula_video_news/ (11,200 followers) ● Youtube https://www.youtube.com/channel/UCZ1Ma83Js52mBTt8hA8PrYQ (deplatformed) ● OK https://ok.ru/akulakhish 10,598 followers DERZKIY KVADRAT (1 mention) ● Vkontakte: https://vk.com/derzkvadrat (744733 followers) ● Vkontakte: https://vk.com/derzkikvadrat – Saint-Petersburg branch (94584 followers) ● Telegram: https://t.me/+sZN7OVGwm7cxZDcy (52260 subscribers) ● Instagram https://www.instagram.com/kvadratderzky/ (82.3K followers) ● OK https://ok.ru/derzkykvad 7039 followers ● Youtube https://www.youtube.com/channel/UCVB6wHCwXBWecAVmN7j4uWA (deplatformed) Znakom’tes’, Bob (4 mentions) 17 ● Youtube: https://www.youtube.com/channel/UCFA23i6dYWRd9hYnaRW7RqQ (2.02M subscribers) ● Yandex.Zen https://dzen.ru/id/60bf783a8e7e993b17f88b42 (3.1K subscribers) ● Vkontakte: https://vk.com/thisisbob_official (136171 followers) ● TikTok: https://www.tiktok.com/@bobvtiktok?_d=dcc4bmbe9fdbg5&language=ru &sec_uid=MS4wLjABAAAA2qIhha19189Fz_QKjz1NMjFxJgcqvsmTW ObDZroKp2phgMOOn2piNvisb-8BS_oJ&u_code=dcejb34km8c21b&utm _campaign=client_share&app=musically&utm_medium=ios&user_id=682 9594703018411014&tt_from=copy&utm_source=copy&source=h5_m (1.5M likes) ● Instagram: https://www.instagram.com/official_this_is_bob/ (51.8K followers) ● Coub: https://coub.com/hellobob (438496 views) ● Rutube: https://rutube.ru/channel/24777186/ (42 followers) Chto, esli (3 mentions) ● Youtube: https://www.youtube.com/channel/UCaH6_l7kegoxm84cLsJjziw (493K subscribers) ● Yandex.Zen: https://dzen.ru/id/60ba3a9a52b6a776639ad9b0 (1.1K followers) ● Vkontake: https://vk.com/whatif_official (49381 follower) ● TikTok: https://www.tiktok.com/@chtoesli.official?_d=dcc4bmbe9fdbg5&languag 18 e=ru&sec_uid=MS4wLjABAAAAGPv3p0FwjjpyW5NIEryavDDN4vFB8 Lu1WftAn_lxfzN85IHp1P37y8-jxjdjGalJ&share_author_id=68307803247 64812294&u_code=dcg2292mdb4mhj&utm_campaign=client_share&app =musically&utm_medium=ios&user_id=6830780324764812294&tt_from =copy&utm_source=copy&source=h5_m (3.4M Likes) ● Rutube: https://rutube.ru/channel/24776566/ (217 followers) Kratkaya istoriya (3 mentions) ● Youtube: https://www.youtube.com/@Kratkaya-Istoria (750K subscribers) ● Yanex.Zen https://dzen.ru/id/5ec64f3ebd57897717b49271 (19.5K subscribers) ● Vkontakte: https://vk.com/kratkaya_istoria (258 followers) ● Rutube: https://rutube.ru/channel/24774171/ (1458 followers) Igry razuma (1 mention) ● Vkontakte: https://vk.com/gamesofmind_official (16497 followers) ● Youtube: link leads to Znakomtes‘, Bob CheBe (1 mention) ● Youtube: https://www.youtube.com/@user-wg6ni7zx3f/featured (93.4K subscribers) ● Vkontakte: https://vk.com/chebeanimation (19K followers) ● Rutube: https://rutube.ru/channel/24773900/ (44 followers) 19 II. Description of the Procedures of Data Collection/Analysis and the Data Set Part 1. Data Collection For data collection, we created accounts on the two most popular Russian job search portals (HeadHunter and SuperJob) in the name of one of the researchers working on this project. Where we needed to register as an organisation (HeadHunter), we registered as [blinded for peer review] at the University of [blinded for peer review]. Both platforms allow users registered as employers to search their CV database using multiple search criteria. We chose to search for previous work experience with any of the organisations named in the US indictment and the media outlets registered under their name. During an exploratory phase, we noticed that some CVs used misspelt names of one or more organisations. Consequently, we included the most frequent misspellings in our initial search list. Our final search list contained 48 search terms (see Table 5). If the name of the organisation was too common (e.g. "Azimut"), we limited the search results to Saint-Petersburg and Leningrad region as the geographical location of the organisation. This initial collection of CVs was done manually. We saved each CV as an HTML file. The CVs from Superjob were collected between November 2021 and March 2022. In total, we collected 394 CVs. After manually removing or merging the information of 43 duplicate profiles, we obtained a JSON data set of 350 unique CVs. Table 5. The List of Deployed Search Terms Number of Entity Legal Name of Entity Search Terms Deployed 1 GlavSet LLC ООО "ГлавСеть" 20 ГлавСеть Глав Сеть 2 Internet Research LLC Интернет-исследования Интернет исследования Интернет иследования Маркетинговое агенство интернет-исследований ИА "Интернет-исследования" Федеральное агентство новостей «Интернет Исследования» ОАО "Интернет-Исследования" 3 Teka LLC Тека 4 Azimut LLC Азимут 5 MixInfo LLC МиксИнфо Микс Инфо 6 MediaSintez LLC МедиаСинтез Медиа Синтез 7 Internet Research Agency LLC ООО "Агентство интернет иссследований" Агенство интернет исследований Агенство интернет расследований Агенство интернет иследований Агентство интернет иследований Путинские тролли 8 NovInfo LLC НовИнфо Нов Инфо 9 NewInform Ньюинформ Нью Информ НьюИнфо 21 newinform new inform 10 Inforeaktor Инфореактор Инфо реактор inforeactor ireactor 11 Politekspert Политическая экспертиза Политэксперт Полит эксперт politexpert politexpert.net 12 Slovo i delo Слово и дело slovodel 13 PolitRossiya ПолитРоссия Полит Россия politros Note. Entities 9-13 are media outlets registered by LLC NovInfo 22 Part 2. Coding Procedure Units of data collection The unit of data collection is the unique HTML file containing a unique CV. Data scraping procedure We used JavaScript code (available on request) to automatically extract relevant information from each data collection unit. We then unified this information from all the collected units in JavaScript Object Notation (JSON) format. In line with our ethics protocol, we refrained from extracting key personal information such as name and contact details at this early stage of data processing. After scaping, we removed 43 duplicate profiles. Manual coding In the subsequent step, additional key-value pairs were created based on manual coding. These new variables contained, for instance, additional information on how specific “work experiences” and “education experiences” related to the IRA. For example for “work experience|, we differentiate between (1) work experience at IRA, (2) first work experience immediately after leaving the IRA, and (3) first work experience immediately before joining the IRA). For an overview of all variables, see Part 5. 23 Part 3. Description of the JSON Data Set Variables Please note that, as described in Part 1, “Work experience” and “Education experience”, are arrays of objects including multiple key-value pairs. These key-value pairs we intended in the descriptions of the arrays of objects below for convenience. Manually coded variables are marked as [manually coded]. ID ( id ) Format: universally unique identifier (UUID) Description: CV ID generated for analysis Last update date ( lastUpdateDate ) Format: YYYY-MM-DDTHH:mm:ssZ (ISO8601) Description: Date of the last CV update according to the job-seeking portal Birthday ( birthday ) Format: YYYY-MM-DDTHH:mm:ssZ (ISO8601) Description: Date of birth of CV owner Citizenship ( citizenship ) Format: array of strings (individual could have multiple citizenships) Description: Citizenship(s) of CV owner at the time of last CV update Work Permit ( workPermit ) Format: array of strings (individual could have multiple work permits) Description: CV owner's work permit(s) at the time of the last CV update 24 Gender ( gender ) Format: string Description: Gender of CV owner City ( city ) Format: string Description: Self-declared city of CV owner at the time of last CV update Self Description ( selfDescription ) Format: string Description: Self-description of CV owner. Usually short bio and a list of skills relevant for the desired job. Education Level ( educationLevel ) Format: string Description: Educational level of the CV holder when last updated Languages ( languages ) Format: Object with the language as the key name and the level of proficiency as the entry (e.g. "English" : "Intermediate"). An individual may know several languages Description: Language knowledge Multiple Entities ( multipleEntities ) [manually coded] Format: „chain“ or „separated“ or „same“ 25 Description: "Chain" for a chain of multiple experiences in the IRA legal entities, "separate" for separate experiences in the multiple relevant legal entities (with breaks for different jobs), "same" for different positions in the same legal entity IRA Count ( iraCount ) Format: number Description: Number of IRA work experiences recorded on the individual's CV IRA StartDate ( iraStartDate ) Format: YYYY-MM Description: Absolute start date at the IRA IRA EndDate ( iraEndDate ) Format: YYYY-MM Description: Absolute end date at the IRA IRA StartAge ( iraStartAge ) Format: integer Description: Calculated age at the start of the first entry at the IRA IRA EndAge ( iraEndAge ) Format: integer Description: Calculated age at the end at the last entry at the IRA IRA TotalDays ( iraTotalTime ) Format: integer Description: Number of days of the total working time at the IRA Pre IRA Experience ( preIraExpereince ) Format: integer 32 ● Marketing: A position responsible for promoting and selling a company's products or services, often through advertising, market research, and customer outreach. ● Office Manager: A position responsible for the day-to-day operations of an office or department, such as managing staff, coordinating meetings, and maintaining supplies and equipment. ● Security Service: A position related to maintaining the security and safety of a company or organisation, such as through monitoring and surveillance, or the implementation of security procedures and protocols. ● SEO (Search Engine Optimization): A position responsible for optimising the visibility and ranking of a company's website or online content in search engine results. ● SMM (Social Media Marketing): A position responsible for promoting and engaging with customers through social media platforms. ● Other: A broad category that could include various job titles not listed above. Job Type (non-IRA) In this field, we have grouped the "Company Position" field according to the following criteria: ● Analyst/Auditor/Quality Control/Accountant/Lawyer: Individuals working in fields such as data analysis, auditing, quality control, accounting, and law. ● Army: Individuals working in the armed forces. ● Assistance/Supply/Control/Logistics: Individuals working in positions related to assistance, supply, control, and logistics. 33 ● Barkeeper/Waiter/Salesman/Administrator: Individuals working in positions such as barkeeper, waiter, salesman, or an administrator. ● Manual Labourer: Individuals working in manual labour positions. ● Creative (Other): Individuals working in creative fields not specified elsewhere in the list. ● Design/Photo/Video : Individuals working in fields such as design, photography, and videography. ● Engineer: Individuals working in engineering fields. ● Freelance: Individuals working in freelance or self-employment positions. ● Government: Individuals working in government positions. ● Higher Ranks (Executive/Managerial Positions): Individuals in executive or managerial positions. ● HR (Human Resources): Individuals working in human resources positions. ● IT/Tech: Individuals working in information technology or technical positions. ● Journalism: Individuals working in journalism positions. ● Medicine: Individuals working in medical fields. ● PR/Events: Individuals working in public relations or event planning positions. ● Sales and Marketing: Individuals working in sales and marketing positions. ● SMM/Content/Copywriting: Individuals working in social media marketing, content creation, or copywriting positions. 34 ● Support/Consulting: Individuals working in support or consulting positions. ● Teaching/Organisation of Teaching Process/Researchers: Individuals working in teaching, education organisation, or research positions. ● Trainees/Lab Assistants: Individuals working as trainees or lab assistants. ● Translation: Individuals working in translation positions. ● Other (unspecified job type): Individuals whose job type is not specified in the dataset. 35 Part 5. Instructions for Coders on Data Cleaning The following variables have been created and then cleaned according to the instructions below. Level of Education Our coding of this variable is based on the entries provided by the individuals to the fields 'Faculty' and 'Specialisation', independently of whether they completed relevant education before or after the IRA. We distinguish the following levels: ● Bachelor's degree: The individual has completed a bachelor's degree program. ● Higher education: The individual has completed some form of higher education, but no specific degree level is mentioned. ● Higher education (Candidate of Sciences) : The individual has completed a Candidate of Sciences degree, which is a postgraduate degree in some countries. ● Master's degree: The individual has completed a master's degree program. ● Middle professional education: The individual has completed middle professional education, which may include vocational education or an associate degree. ● Military education: The individual has completed military education. ● School only: The individual has not completed any higher education and has only completed school. ● Unfinished higher education: The individual has completed some college or university education, but has not earned a degree. ● Other: The individual's education level is not specified in the data set. 36 International experience These fields contain the names of the foreign countries found in the 'Experience' objects, except for South Ossetia, which is a territory of Georgia occupied by Russia. ● Belarus ● China ● Czech Republic ● Estonia ● India ● Kazakhstan ● Lithuania ● Libya ● South Ossetia (Territory of Georgia occupied by Russia) ● Syria ● Ukraine ● United Arab Emirates ● United States of America ● Uzbekistan ● No abroad experience City of the University The city of the institution listed in the education item is grouped in this field. ● Abroad (not a city in Russia) ● Arkhangelsk ● Astrakhan ● Bratsk 37 ● Bryansk ● Buzuluk ● Cheboksary ● Chelyabinsk ● Cherepovets ● Dzerzhinsk ● Ekaterinburg ● Gatchina ● Irkutsk ● Ivanovo ● Izhevsk ● Kazan ● Kirov ● Kirsanov ● Komsomolsk-on-Amur ● Kostroma ● Krasnodar ● Krasnoyarsk ● Kursk ● Lyubertsy ● Moscow ● Murmansk ● Nalchik ● Nizhny Novgorod ● Nizhny Tagil 38 ● Norilsk ● Novokuznetsk ● Novosibirsk ● Omsk ● Orenburg ● Other (no specific city mentioned) ● Ozersk ● Petrozavodsk ● Pskov ● Rostov-on-Don ● Ryazan ● Saint Petersburg ● Samara ● Saratov ● Sarov ● Smolensk ● Sochi ● Syktyvkar ● Tambov ● Tolyatti ● Tomsk ● Tuapse ● Tver ● Tyumen ● Ukhta 39 ● Ufa ● Ulyanovsk ● Veliky Novgorod ● Vladivostok ● Volgograd ● Vologda ● Yoshkar-Ola ● Not stated = Null University Geographical Distribution In this field, we have re-grouped the field "City of University" according to the following criteria: ● Saint Petersburg: The university is in Saint Petersburg or Gatchina (Leningrad region). ● Moscow: The university is in Moscow or Liubertsy (Moscow region). ● Abroad: The university is located outside of Russia. ● Other Russian City: The university is located in a city in Russia that is not Moscow or Saint Petersburg. ● Not stated: The university location is not specified in the dataset. Field of Study In this field, we grouped the fields of „Faculty“ and „Specialization“ according to the following criteria: ● Architecture: The individual studied architecture. ● Arts: The individual studied art. 40 ● Eastern Studies: The individual studied subjects related to Eastern cultures and societies. ● Economics/Management: The individual studied economics or management. ● Publishing: The individual studied publishing or editing. ● Engineering/Construction: The individual studied engineering or construction. ● History: The individual studied history. ● Information Technology (IT): The individual studied information technology or computer science. ● Journalism/Public Relations/Advertising: The individual studied journalism, public relations, or advertising. ● Law: The individual studied law. ● Medicine: The individual studied medicine. ● Natural Science: The individual studied natural science such as physics, chemistry, or biology. ● Philology and Languages (Foreign): The individual studied a foreign language or philology. ● Philology and Languages (Russian): The individual studied the Russian language or philology. ● Political Science/International Relations: The individual studied political science or international relations. ● Psychology: The individual studied psychology. ● Sociology/Conflict Resolution : The individual studied sociology or conflict resolution. 41 ● Teaching: The individual studied teaching or education. ● Tourism: The individual studied tourism. Other: The individual's field of study is not specified in the dataset.