Государство, гражданское общество истабильность 27
Научная статья / Research Article
УДК 004.89
DOI: 10.14258/SSI(2024)1-02.
О применимости тематического моделирования для
определения акторов социально-политической мобилизации
сиспользованием low-code аналитических платформ
Иван Юрьевич Степанов
Кемеровский государственный университет, Кемерово, Россия,
zextel1995@gmail.com, https://orcid.org/0000-0002-7938-8049
Елена Анатольевна Кранзеева
Кемеровский государственный университет, Кемерово, Россия,
elkranzeeva@mail.ru, https://orcid.org/0000-0003-2577-9017
Евгений Васильевич Головацкий
Кемеровский государственный университет, Кемерово, Россия,
xomaik@rambler.ru, https://orcid.org/0000-0002-8485-5852
Инна Вениаминовна Донова
Кемеровский государственный университет, Кемерово, Россия,
idonova@gmail.com, https://orcid.org/0000-0001-9370-5402
Анна Леонидовна Бурмакина
Кемеровский государственный университет, Кемерово, Россия,
anna-sidjakina@rambler.ru, https://orcid.org/0000-0003-3087-4393
Аннотация. --
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low-code ,
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,
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-
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,
.
Ключевые слова: , , --
, , ,
Society andSecurity Insights № 1 2024 28
Финансирование: -
( FZSR-2023-0006 « --
:
data-mining»).
Для цитирования: Степанов И.Ю., Кранзеева Е.А., Головацкий Е.В., Донова И.В., Бурма-
кина А.Л. Оприменимости тематического моделирования для определения акторов социаль-
но-политической мобилизации сиспользованием low-code аналитических платформ // Society
andSecurity Insights. 2024. Т.7, №1. С.27–39. doi: 10.14258/ssi(2024)1-02.
e Applicability ofTopic Modeling to Identify Actors ofSocio-
Political Mobilization using Low-Code Analytical Platforms
Ivan Yu. Stepanov
Kemerovo State University, Kemerovo, Russia,
zextel1995@gmail.com, https://orcid.org/0000-0002-7938-8049
Elena A. Kranzeeva
Kemerovo State University, Kemerovo, Russia,
elkranzeeva@mail.ru, https://orcid.org/0000-0003-2577-9017
Evgenyi V. Golovatskyi
Kemerovo State University, Kemerovo, Russia,
xomaik@rambler.ru, https://orcid.org/0000-0002-8485-5852
Inna V. Donova
Kemerovo State University, Kemerovo, Russia,
idonova@gmail.com, https://orcid.org/0000-0001-9370-5402
Anna L. Burmakina
Kemerovo State University, Kemerovo, Russia,
anna-sidjakina@rambler.ru, https://orcid.org/0000-0003-3087-4393
Abstract. earticle presents acontemporary perspective on theanalysis ofsocio-political
processes, grounded inthe premise that theapplication oftopic modeling through low-code plat-
forms can substantially enhance thequality ofresearch performed by analysts. is enhance-
ment is particularly signi cant inidentifying thepivotal actors andtheevolving dynamics with-
in socio-political processes. eauthors argue that topic modeling, arelatively novel approach
compared to traditional methods, is capable ofuncovering relationships andtrends that might
otherwise remain obscured. In advocating for this approach, thepaper proposes an integrated
methodology. is methodology is designed to empower researchers inthe social sciences, en-
Государство, гражданское общество истабильность 29
abling them to e ectively utilize these innovative tools. eobjective is to deepen their compre-
hension ofthe underlying mechanisms that drive socio-political mobilization. To substantiate
their argument, theauthors present various case studies. ese case studies demonstrate theef-
fectiveness oftopic modeling inrevealing otherwise hidden connections among various actors.
Additionally, they illustrate how topic modeling sheds light on thecontributions ofthese actors
to thedynamics ofmobilization. is approach represents asigni cant advancement inthe eld,
o ering new insights andamore nuanced understanding ofcomplex socio-political landscapes.
Keywords: topic modeling, clustering, socio-political interaction, mobilization, analytical
platforms, computer sociology
Financing: ework was carried out with the nancial support ofthe Ministry ofScience
andHigher Education ofthe Russian Federation (project FZSR-2023-0006 «Network socio-politi-
cal mobilization inthe regions ofthe resource type ofSiberia: research capabilities ofdata-mining
tools»).
For citation: Stepanov, I.Yu., Kranzeeva, E.A., Golovatsky, E.V., Donova, I.V., Burmakina, A.L.
(2024). The Applicability of Topic Modeling to Identify Actors of Socio-Political Mobilization using
Low-Code Analytical Platforms. Society andSecurity Insights, 7(1), 27–39. (In Russ.). doi: 10.14258/
ssi(2024)1-02.
Введение
—
-
(Apishev, Vorontsov, 22).
, «
» « ».
-
/ .
, Word2Vec
FastText. ,
, RNN, GRU LSTM,
(Gre et
al., 217).
LSA— Latent Semantic Analysis. LSA 1988 . -
. (Deerwester et al, 1988). 1999 .
(PLSA) (Hofmann, 2).
-
().
.
(Latent Dirichlet Allocation, LDA)—
(, 22).
— -
(Additive Regularization for Topic Modeling, ARTM)
PLSA . -
Society andSecurity Insights № 1 2024 30
ARTM .
LDA ARTM , ,
(, , 219).
- , -
: --
, , —
, «»
, .
VK («-
»),
, .
state-of-the-art
, — -
,
. -
, « » ,
low-code .
, -
, «», , -
.
.
( ) -
, Orange Data Mining, KNIME
Analytical Platform PolyAnalyst. -
, . ,
Orange Data Mining -
(, 222),
(,
, 22), -
(, 222). , -
( )
(, 222). PolyAnalyst
— , -
, -
(, , , 221). -
( , RSS-) (-
., 221), (, 222),
- (, 222).
-
-
, -
Государство, гражданское общество истабильность 31
(, 216). -
,
( ., 218).
- , -
,
,
. , (-
, )
,
,
.
«
», —
(, 218) .
n- , -
.
big data
(, , ..).
-
(bias): (selection bias),
; -
(description bias), / -
; (research bias),
( ) (Hutter, Porta, 214).
()
, - -
. -, «-
» ( ), -
, , -
- . -,
( -
) (, , 216;
, 219).
Методы исследования
- low-code -
. ,
Society andSecurity Insights № 1 2024 32
, , -
- ,
. , -
PolyAnalyst, -
, ,
VK, -
. -
-
-
. -
. , -
.
low-
code - .
Результаты исследования
, -
,
PolyAnalyst
— , , -
«» , -
, , ,
, . -
, , -
/ ,
.
API VK -, -
, ( -
, id .). -
LDA gensim
-
, (.1).
«»,
, ,
pipeline .
PolyAnalyst (.2).
PolyAnalyst -
, . ,
« » -
- . ,
«» -
/ (.3).
Государство, гражданское общество истабильность 33
1— LDA (,
, 222 ., ~2 , ).
Figure 1— Results ofthematic modeling using theLADA model (Kuzbass, Tyumen region, December
222, initial array of~2 million lines, manual selection ofthematic groups).
Society andSecurity Insights № 1 2024 34
2— PolyAnalyst.
Figure 2— estructure ofthe research assembly stages inthe PolyAnalyst platform.
Государство, гражданское общество истабильность 35
3—
222 . (, , ).
Figure 3— Enlarged thematic grouping ofthe agenda ofnetwork communities inthe period from
January to December 222 (Kuzbass, Tyumen region, manual sorting ofthematic groups).
Обсуждение изаключение
-
-
. -
« »,
.
-
- .
, low-code -
,
. -
- -
, .
-
low-code
. -
- ,
.
Society andSecurity Insights № 1 2024 36
СПИСОК ИСТОЧНИКОВ
. . // -
. 219. 49 (287). . 539–542.
. ., . . datamining -
( ) //
. 22. 3 (68). C. 16–26.
. ., . .
: // PolitBook. 216. 3.
. .
ORANGE ( ) //
( ) -
(, 1–3 222 .). , 222. C. 185–187.
. ., . ., . ., . .
- -
«» // :
. 221. 3. . 1-17. DOI: 1.7256/2454-749.221.3.35234
. . -
: -
// . -
. 222. 14 (2). https://doi.org/1.1772/273-6681-222-2-18-26
. .
// . 222. 3. . 493–5.
. . - -
( ) // -
. 222. . 5: . 5. . 16–28.
. ., . ., . . -
: -
// -
. 221. 464. . 81–9. DOI: 1.17223/15617793/464/1
. ., . ., . .
// -
: . 221. 2 (41). . 57–72. doi:1.18799/26584956/221/3
(42)/189
. . . .: -
-, 216. 296..
. . Data mining --
: «» //
. . . . 222. 6. . 126–
136. doi:1.21686/2413-2829-222-6-126-136
. -
// . 218. .17, 2. . 317–341. doi:
1.17323/1728-192X-218-2-317-341
Государство, гражданское общество истабильность 37
. ., . . -
// . . 219.
.23? 4. . 7–23.
. . -
«» //
. 22. 6–2 (75). . 45–49.
Apishev M., Vorontsov K. Learning topic models with arbitrary loss // Proceedings
ofthe XXth Conference ofOpen Innovations Association FRUCT. 22. Vol. 26. P.1–8.
DOI: 1.23919/FRUCT4888.22.987559
Deerwester S., et al, Improving Information Retrieval with Latent Semantic Indexing,
Proceedings ofthe 51st Annual Meeting ofthe American Society for Information Science
25, 1988. Pp. 36–4.
Golovatsky E., Kranzeeva E., Orlova A., Burmakina A. Social Practices ofMobilizing
Population Initiatives: Prospects for Hybrid Methodology // International Conference
on Communicative Strategies ofInformation Society (CSIS 218). Advances inSocial
Science, Education andHumanities Research, 218. Vol. 289. Pp. 8–13. doi: 1.2991/csis-
18.219.2
Gre K., Srivastava R. K., Koutník J., Steunebrink B. R., andJ.Schmidhuber, «LSTM:
A Search Space Odyssey,» in IEEE Transactions on Neural Networks and Learning
Systems, Vol. 28, No 1. Pp. 2222–2232, Oct. 217, doi: 1.119/TNNLS.216.2582924.
Hofmann . Learning the Similarity of Documents: an information-geometric
approach to document retrieval andcategorization, Advances inNeural Information
Processing Systems 12. MIT Press, 2. Pp. 914–92.
Hutter S. 214. Protest Event Analysis and Its O spring // della Porta D. (ed.).
Methodological Practices in Social Movement Research. Oxford: Oxford University
Press. Pp. 335–367.
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СВЕДЕНИЯ ОБ АВТОРАХ / INFORMATION ABOUT THE AUTHORS
—
, , . , .
Ivan Yu. Stepanov— Assistant Professor, Department ofDigital Technologies, Institute
ofDigitalization, Kemerovo State University, Kemerovo, Russia.
— - . , , . -
, , . , .
Elena A. Kranzeeva — Dr. Sci. (Sociology), Head of the Department of Sociological
Sciences, Kemerovo State University, Kemerovo, Russia.
— - . , ,
, , . ,
.
Evgeny V. Golovatsky— Dr. Sci. (Sociology), Professor ofthe Department ofSociologi-
cal Sciences, Kemerovo State University, Kemerovo, Russia.
— . . ,
.. , -
, . , .
Inna V. Donova— Cand. Sci. (Economics), Associate Professor, I.P. Povarich Depart-
ment ofManagement, Institute ofEconomics andManagement, Kemerovo State Uni-
versity, Kemerovo, Russia.
— -
, , . , .
Anna L. Burmakina— Senior Lecturer, Department ofSociological Sciences, Kemerovo
State University, Kemerovo, Russia.
Статья поступила вредакцию 12.01.2024;
одобрена после рецензирования 15.02.2024;
принята кпубликации 20.02.2024.
The article was submitted 12.01.2024;
approved after reviewing 15.02.2024;
accepted for publication 20.02.2024.
