=Paper=
{{Paper
|id=Vol-3688/paper1
|storemode=property
|title=Topic Modelling of Ukraine War-Related News Using Latent Dirichlet Allocation with Collapsed Gibbs Sampling
|pdfUrl=https://ceur-ws.org/Vol-3688/paper1.pdf
|volume=Vol-3688
|authors=Nina Khairova,Yehor Holyk,Dmytro Sytnikov,Yurii Mishcheriakov,Nadiia Shanidze
|dblpUrl=https://dblp.org/rec/conf/colins/KhairovaHSMS24
}}
==Topic Modelling of Ukraine War-Related News Using Latent Dirichlet Allocation with Collapsed Gibbs Sampling==
https://ceur-ws.org/Vol-3688/paper1.pdf
Topic Modelling of Ukraine War-Related News Using
Latent Dirichlet Allocation with Collapsed Gibbs Sampling
Nina Khairova1,2, Yehor Holyk3, Dmytro Sytnikov3, Yurii Mishcheriakov3 and Nadiia
Shanidze1
1 National Technical University “Kharkiv Polytechnic Institute”, Kyrpychova str. 2, Kharkiv, 61002, Ukraine
2 Umeå University, UNIVERSITETSTORGET 4, Umeå, 901 87, Sweden
3 Kharkiv National University of Radio Electronics, Nauky Ave. 14, Kharkiv, 61166, Ukraine
Abstract
The context of this research is the application of topic modeling to war-related news in the context of
the Ukraine war. The objective of the research is to use Latent Dirichlet Allocation (LDA) with Collapsed
Gibbs sampling to identify distinct content groups in war-related news. The method used in the research
involves data scraping from a Ukrainian news website, data preprocessing, and applying the LDA with
Collapsed Gibbs algorithm to infer the latent topics within the corpus. The results of the research include
the identification of twelve distinct topics and the corresponding keywords that characterize each topic.
The analysis of the results provides insights into the context of each topic, such as discussions on safety
measures during wartime, consequences of military actions, and reports on military casualties. The
research concludes that the application of LDA with Collapsed Gibbs is a valuable tool for identifying
and understanding the context of war-related news. However, there may be discrepancies between the
results of the model and human interpretation, which may be due to limitations in the results, model
parameters, and the presence of noise data. Future research should focus on optimizing model
parameters, filtering noise data, and improving the analysis of topic context to enhance the reliability
and interpretability of the results.
Keywords
Topic modeling, Ukraine war, Latent Dirichlet Allocation 1
1. Introduction
With the progress of information technologies, concepts such as "information warfare",
"information hygiene", and "hybrid warfare" have emerged in modern warfare. These terms have
appeared not without reason, as wars and conflicts now take place not only on the battlefield but
also in cyberspace and the information environment. Information warfare is used to manipulate
public opinion, influence political processes, and destabilize countries or regions. This can include
spreading disinformation, fake news, cyberattacks on critical infrastructure, and so on. Hybrid
warfare combines military actions with unofficial, unconventional methods of warfare, such as
subversive activities, psychological warfare, economic pressure, and more [1–2]. This can involve
destabilizing countries through supporting internal conflicts, hybrid military operations,
cyberattacks, and other methods of influence.
To counter information attacks, it is important to be able to classify types of information by
their content. The use of machine learning algorithms has a wide range of applications, including
natural language processing (NLP). They are widely used for tasks involving large volumes of
data, which is advantageous when dealing with abundant information.
The research topic is the application of topic modeling to war-related news to identify distinct
content groups. Topic modeling of news will allow for the separation of information by content
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024,
Lviv, Ukraine
khairova.nina@gmail.com (N. Khairova); yehor.holyk@nure.ua (Y. Holyk); dmytro.sytnikov@nure.ua
(D. Sytnikov), iurii.mishcheriakov@nure.ua (Y. Mishcheriakov); nashanidze@ukr.net (N. Shanidze)
0000-0002-9826-0286 (N. Khairova); 0009-0007-6325-1666 (Y. Holyk); 0000-0003-1240-7900 (D. Sytnikov);
0000-0002-5334-1808 (Y. Mishcheriakov), 0000-0002-9613-186X (N. Shanidze)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
and observation of the context behind the keywords of these separate groups. In the future, the
results could be valuable for comparing news from different news websites for the presence of
disinformation or manipulation.
The objective of the research is to apply topic modeling to war-related news to identify distinct
content groups. The research object is a news website. The subject of the research is Latent
Dirichlet Allocation (LDA) with collapsed Gibbs sampling, belonging to the field of machine
learning. LDA is a generative probabilistic model used for topic modeling in natural language
processing (NLP). It is a technique used to discover hidden topics in a collection of documents by
modeling how words are generated from these topics and how documents are generated from a
mixture of topics.
2. Related works
2.1. Topic modelling usage in news
Topic modeling of news is a relevant topic for contemporary research. In the work [3], the
possibility of applying the Latent Dirichlet Allocation (LDA) method to Indonesian news in the
context of infrastructure development in the country is considered. The specificity of applying
this method to news lies in finding the optimal number of topics among all news, as an excessive
number of topics can lead to confusion and incomplete results. To find the optimal number of
topics, it was proposed to determine the number of topics within a certain range and evaluate the
coherence value for each number of topics. The highest value can be used for further analysis of
topics. The conventional Latent Dirichlet Allocation (LDA) method was used in the work to
conduct topic modeling and identify topics related to infrastructure development. In addition to
the application of the method, the authors used data visualization models to display the obtained
results.
In [4], two commonly used topic modeling methods are Latent Dirichlet Allocation (LDA) and
BERTopic. They are employed to analyze the change in topics in Swedish newspaper articles
about COVID-19. This allowed for obtaining more information about the main topics and topic
changes in a large volume of data. The study processed 6515 articles, applying methods and
tracking topic change statistics over approximately 1 year and 2 months from January 17, 2020,
to March 13, 2021.
The article [5] describes the methodology used in a study to analyze the portrayal of urology
in the media. The researchers collected data from news articles using a search term and extracted
relevant information using Python's 'beautiful soup' library. They then preprocessed the data by
segmenting the text and removing unnecessary words. The data was analyzed using Latent
Dirichlet Allocation (LDA) topic modeling to identify key topics and associated words. The results
showed that topics such as research and developments in new technologies, urinary conditions,
health insurance coverage, and robotic surgery were frequently discussed in urology-related
news.
2.2. Topic modelling usage in social media
The utilization of topic modeling in social media analysis has become increasingly prominent
in contemporary research, offering valuable insights into the dynamic landscape of online
discourse. [6] focuses on utilizing Twitter data to enhance disaster response and management
efforts. The methods employed include natural language processing (NLP), particularly a
supervised approach for classifying tweets into different categories to extract situational
awareness (SA) information. However, it highlights the limitations of high-performing supervised
models due to their reliance on domain knowledge and costly labeling tasks. To address these
limitations, the research proposes a guided latent Dirichlet allocation (LDA) workflow to identify
temporal latent topics from tweets during the 2020 Hurricane Laura disaster event. By
integrating prior knowledge, coherence modeling, LDA topic visualization, and validation from
official reports, the guided approach reveals that tweets during Hurricane Laura contain multiple
latent topics. This finding suggests that existing supervised models may not fully exploit tweet
information, as they assign each tweet a single label. In contrast, the proposed model not only
identifies emerging topics during different disaster events but also provides multilabel references
to enhance classification accuracy. Additionally, the results can aid in quickly extracting SA
information for responders, stakeholders, and the general public to facilitate timely response
strategies and resource allocation during hurricane events.
Another example of using LDA in social networks is described in the [7]. This study focuses on
analyzing Instagram data related to the Healthy Living Community Movement (GERMAS) in
Indonesia. The country is currently facing a double burden of disease, with a shift in disease
patterns due to changes in people's lifestyles. The researchers used Data Mining techniques,
specifically Latent Dirichlet Allocation (LDA), to model topics from the data captions on
Instagram. They collected 80,745 data captions with the “#germas” keyword and performed
preprocessing and feature extraction before applying LDA. The evaluation of the number of topics
was done using topic coherence, and the results showed that eight topic segments were most
appropriate. The content analysis revealed that the most dominant topic related to GERMAS was
a healthy lifestyle diet. This study highlights the importance of Instagram data in providing new
media information for the community and the health department, and it can help promote a
healthy lifestyle among the population.
3. Methods
To address the task of topic modeling news related to the theme of war, the following algorithm
of actions is proposed, depicted in the form of a flowchart in the Figure 1:
Figure 1: Overview of the proposed workflow of Guided LDA with Collapsed Gibbs model.
3.1. Data scrapping
The process of data collection and extraction is unique and requires an individualized
approach for each data source. The easiest way to obtain data from news sources is by extracting
data from news websites, as they are stored in a structured format. The complexity lies in the
need to familiarize oneself with the structure of the HTML code of the news website, and locate
the tags and classes corresponding to the news content. Additionally, one must also consider the
structure of requests to the news website, especially if the news is stored across multiple pages,
requiring separate requests for each page. The LxmlSoup library in the Python programming
language is a convenient tool for extracting data from HTML documents. Its versatility and robust
functionality make it an indispensable asset for web scraping and data extraction tasks. With
LxmlSoup, it can be easy to navigate through HTML documents, swiftly locating specific elements
based on class names, identifiers, or other attributes.
For the study, the Ukrainian news website 'Ukrainska Pravda' [8] was selected. This news
website covers events related to various domains, including war, military actions, and shelling,
as well as political, social, and economic developments within Ukraine and internationally.
This website stores news on pages by specific date, namely day, month, and year. The structure
of queries to such a news website is very simple, and by using the Python programming language,
it will be possible to extract the necessary data.
3.2. Data preprocessing
To analyze the news, it is necessary to first perform a preprocessing step. This step is designed
according to the specificities of applying the Latent Dirichlet Allocation method with Collapsed
Gibbs.
Text preprocessing is a crucial step in natural language processing (NLP) that involves
transforming raw text data into a format that can be easily understood and analyzed by machine
learning algorithms. It involves several stages of data cleaning and transformation to prepare the
text for feature extraction and analysis.
When using Latent Dirichlet Allocation, it is important to customize preprocessing steps to
match the needs of the algorithm. One example of this is preprocessing text to maintain the
structure of phrases or multi-word combinations, as this can provide useful information on topic
consistency and thematic connections.
The main goals of text preprocessing are to simplify the text, reduce noise and variability, and
extract meaningful features. This is important because machine learning algorithms typically
require numerical inputs and unstructured text data needs to be converted into a structured
format.
The preprocessing steps typically involved in text preprocessing include:
1. Stop word gathering. Stop words are commonly used words in a language that do not
carry much meaning and are often removed during text preprocessing in natural language
processing (NLP). These words, such as "the," "is," "and," etc., occur frequently in text but do
not provide much information for analysis. By removing stop words, we can reduce the size of
the dictionary of unique words, which can improve the efficiency and performance of natural
language processing (NLP) algorithms. Gathering a list of stop words helps in identifying and
removing these common words from the text data before further analysis and feature
extraction
2. Tokenization. This involves breaking the text into individual words or tokens. It is the first
step in feature extraction and involves splitting the text at the word level
3. Token cleansing. Stop words are common words that do not carry much meaning. These
words are often removed from the text as they can add noise and do not contribute much to
the overall meaning of the text
4. Lemmatization. This step involves reducing words to their base or root form. This helps
in reducing the dimensionality of the data and ensures that variations of the same word are
treated as the same entity. This can be done through stemming or lemmatization techniques
5. Vocabulary creation. A master dictionary is a collection of all the unique words in the
corpus. It helps in creating a standardized representation of the text data and serves as a
reference for feature extraction
Once the text has been preprocessed, various techniques can be used to represent the text as
numerical features. One popular technique is the bag-of-words (BOW) representation, where
each word in the text is treated as a separate feature [9]. The values of these features can be
modified based on techniques such as term count, term frequency (TF), and term frequency-
inverse document frequency (IDF) [9]. These techniques assign different weights to words based
on their frequency and importance in the corpus.
Overall, text preprocessing is a critical step in NLP as it helps in transforming unstructured
text data into a structured format that can be easily analyzed using machine learning algorithms.
It involves several stages of data cleaning and transformation to extract meaningful features and
simplify the text.
3.3. Guided LDA with Collapsed Gibbs
Selecting the optimal number of topics for a given corpus is a critical step in topic modeling.
The coherence model, particularly when integrated with guided Latent Dirichlet Allocation (LDA)
using Collapsed Gibbs sampling, serves as a robust method for this purpose.
Topic modeling aims to uncover the latent thematic structure within a corpus of text.
Determining the appropriate number of topics is pivotal for obtaining meaningful insights. The
coherence model, coupled with guided LDA employing Collapsed Gibbs sampling, facilitates this
task effectively. This process includes:
1. Topic Candidates. This step begins by generating a range of potential topic numbers, often
referred to as topic candidates. These candidates represent the spectrum of possible thematic
structures that the corpus might encapsulate. The range is typically predefined based on
domain expertise or through iterative exploration
2. Applying LDA. This involves utilizing guided LDA with Collapsed Gibbs sampling to model
each candidate's number of topics on the corpus. The process entails iteratively inferring the
topic-word distributions and document-topic assignments, thereby uncovering the underlying
thematic structure of the text
3. Coherence Model. After applying LDA to each candidate topic number, the coherence of
the resulting topics using a coherence model is calculated. The coherence model assesses the
semantic coherence and interpretability of the topics by measuring the relatedness of the top
words within each topic
4. Optimal Number of Topics Selection. This step involves selecting the optimal number of
topics based on coherence scores. Higher coherence scores indicate more coherent and
interpretable topics. Thus, the number of topics corresponding to the peak coherence score is
considered optimal for representing the thematic structure of the corpus
5. Refinement and Validation. This involves refining the selected number of topics if
necessary, considering contextual relevance and domain-specific requirements. It is essential
to validate the chosen number of topics through qualitative analysis and expert judgment to
ensure alignment with the corpus's underlying themes
3.3.1. Latent Dirichlet Allocation with Collapsed Gibbs
Latent Dirichlet Allocation (LDA) is a generative probabilistic model commonly used for topic
modeling, which aims to discover the latent topics present in a collection of documents and the
distribution of words within each topic. Collapsed Gibbs sampling is a method used to estimate
the posterior distribution of latent variables in a Bayesian model, such as LDA. The procedure for
LDA with Collapsed Gibbs unfolds as follows:
1. Initialization. The process commences with initializing model parameters, encompassing
topic distributions for each document, word distributions for each topic, and hyperparameters
such as the number of topics and Dirichlet priors for the distributions [10]
2. Gibbs Sampling. At the core of the LDA algorithm lies a Gibbs sampling iteration that
continually updates topic assignments for every word in the corpus. During each iteration, the
algorithm samples a new topic assignment for a randomly selected word, influenced by
existing topic assignments of all other words in the document and the prevailing topic
distributions [10]
3. Convergence. The Gibbs sampling process persists until reaching convergence, typically
identified through various convergence criteria like a predetermined number of iterations,
minimal alterations in the model's log-likelihood, or slight adjustments in topic assignments
[10]
4. Estimation of Parameters. Upon achieving convergence, the model's parameters can be
estimated, encompassing topic distributions for each document, word distributions for each
topic, and hyperparameters [10]
5. Inference. Utilizing the estimated parameters facilitates inference tasks, such as
determining the most probable topics for a new document, estimating a document's likelihood
given the model, or identifying the most likely words associated with a given topic [10]
4. Experiment
The experiment on topic modeling of war-related news was conducted within the Jupyter
Notebook environment with the capability of utilizing GPU. The implementation of the program
code was done using the Python programming language and the following packages: LxmlSoup,
requests, datetime, json, numpy, spacy, nltk, and random.
Initially, an algorithm was developed to generate a set of dates within a specified range, and
then, using HTTP requests, the HTML code of the news page from the “Ukrainska Pravda” [8]
website was obtained. From each news page, its title and article text were extracted. To obtain
news related to the theme of war, each article was checked for the presence of the following
phrases, words, or abbreviations: “attack on”, "explosions rock","attack in", "war", "agression",
"millitary", "support", "ukraine", "europ"," "united", "states". These words were manually
selected by analyzing which phrases or combinations of words were most frequently
encountered in articles related to the theme of war. The retrieved data was saved in a file named
“news.json”. This dataset will undergo the preprocessing process. Thus, within the specified date
range between 02/17/2023 and 02/17/2024, a total of 2364 news articles were obtained,
containing a total of 423,251 words, and 13,414 unique words. Fragment of the extracted data is
shown in Figure 2.
Figure 2: Sample of News Articles from “news.json” file
Next, during the data preprocessing stage, the "news.json" file obtained in the previous step
was read. For each article, the title and text of the article were combined and placed into a
document container. In order to improve text analysis, a carefully selected list of stop words was
created. This list was compiled using an additional file called "stopwords-en.json" and utilizing
the features of the English language text processing tool "en_core_web_sm" from the spacy
package. By utilizing these resources, a thorough stop word list was developed to eliminate
redundant and unhelpful terms that could mask important patterns in the text data. After filling
the document container, we carefully cleaned the data in each document to improve its quality
and relevance. This involved a series of important operations designed to enhance the text
corpus, such as:
• Tokenization. This step involves breaking the text into smaller units, like words or
phrases, to make it easier to study and work with. It is an essential first step in preparing the
text for more in-depth analysis and handling. This initial phase lays the groundwork for other
tasks involved in processing the text
• Removal of tokens belonging to the compiled list of stop words. Stop words were removed
from the text to reduce noise and focus on important content for analysis
• Removal of tokens belonging to punctuation marks. Extraneous punctuation marks were
stripped from the text to ensure consistency and readability, mitigating any potential
interference with subsequent processing steps
• Removal of tokens consisting of numbers. Tokens that only contained numbers were
removed from the text to keep the attention on language-based content, avoiding the inclusion
of numeric information that could potentially impact the analysis
• Removal of tokens representing email addresses. Tokens representing email addresses
were filtered out to maintain data privacy and integrity, ensuring that personal information
did not influence the analysis
• Lemmatization of the text to bring words with the same root to a unified form. The
process of lemmatization was applied to the text data, standardizing words to their base or
root form. This step aimed to reduce lexical variation and enhance the coherence of the text
corpus, thereby facilitating more accurate analysis and interpretation
Through careful execution of these preprocessing steps, the text underwent a thorough
refinement process to ensure it was suitable for analysis and modeling. This laid the foundation
for extracting valuable insights and patterns from the text, ultimately promoting a deeper
understanding and exploration of the data.
Next, a structure was created with a key-value pair where the key is the word and the value is
the total count of the word appearing in the documents. For each token in each document in the
container, the token was added to the structure containing the word count, and the value was
incremented by one. This way, a dictionary of all possible words encountered in the documents
was obtained. This dictionary was then filtered so that only those words whose count exceeds 5
remained in the dictionary. From this set, a vocabulary was formed where each word contains its
identifier, and a mirror vocabulary was created where each identifier contains the word. The
mirror vocabulary will be necessary at the end of the experiment for the reverse conversion of
identifiers to words, as part of the decoding process. Once the dictionaries were established, the
corpus was preprocessed to convert each word token into its corresponding identifier using the
dictionary of word-to-identifier mappings. This transformation facilitated the subsequent
modeling steps by representing the corpus in a numerical format suitable for analysis.
Additionally, the mirror dictionary ensured that the original words could be reconstructed from
their identifiers when interpreting the model's output or evaluating its performance. This
bidirectional mapping between words and identifiers formed a crucial component of the data
preparation phase, enabling seamless integration of the corpus into the modeling pipeline.
Next, a corpus was created, which is a collection of documents to be analyzed. For each token
in each document in the container, word-to-identifier mapping was applied, as Latent Dirichlet
Allocation with Collapsed Gibbs sampling typically requires numerical representations of the
words instead of their textual forms for efficient processing. This mapping converts each word
token into a unique identifier, allowing the algorithm to operate on numerical data. With this
preprocessing step completed, the LDA with Collapsed Gibbs sampling algorithm can proceed to
infer the latent topics within the corpus and the associated word distributions, facilitating a
deeper understanding of the underlying thematic structure. In Figure 3, an example of a
document from the corpus before mapping is shown, and in Figure 4, an example of a document
from the corpus after mapping is shown.
Figure 3: Corpus before mapping
Figure 4: Corpus after mapping
According to [11], the implementation of the software code executing the Latent Dirichlet
Allocation with Collapsed Gibbs algorithm was performed, which takes the following parameters:
• CORPUS – the corpus being analyzed
• NUM_ITER – the number of iterations
• ALPHA – Dirichlet prior parameter for the distribution of topics in documents
• BETA – Dirichlet prior parameter for the distribution of words in topics
• NUM_TOPICS – the number of topics
Implemented function initializes topic assignments for each word in each document randomly.
Then, it estimates document-topic counts (NDK), topic-word counts (NKW), and topic counts
(NK) based on the initial assignments. Next, it iterates through the specified number of iterations,
updating topic assignments for each word in each document using the collapsed Gibbs sampling
algorithm. Finally the function returns the final topic assignments (Z), document-topic counts
(NDK), topic-word counts (NKW), and topic counts (NK).
In addition to the LDA with Collapsed Gibbs algorithm, using [12], an auxiliary function has
been developed to determine the coherence score for the model depending on the number of
topics. This function will be used to analyze the coherence score of models with different numbers
of topics to select their optimal quantity. The coherence score calculation function takes the
following parameters:
• NKW – topic-word count matrix obtained from LDA Gibbs sampling
• TEXTS_FOR_LDA – textual representation of documents
• CORPUS_FOR_COHERENCE – corpus in the required format for coherence calculation
• DCT – dictionary mapping words to their integer indices
• NUM_OF_TOPICS – number of topics inferred from the corpus
This function calculates coherence scores for the inferred topics, computes the topic
coherence by considering the top 20 words per topic, and constructs a coherence model using the
specified coherence measure. In this case, 'c_v' coherence was chosen because this coherence
measure is a widely used metric for evaluating topic coherence in Latent Dirichlet Allocation
(LDA) models [12]. By selecting 'c_v' coherence, the function aims to provide a coherence score
that reflects the interpretability and semantic coherence of the topics, making it easier for
researchers or practitioners to assess the quality of the topics generated by the LDA model and
compare different models or parameter settings effectively [13].
With the ALPHA = 0.1, BETA = 0.1, NUM_ITER = 200 and the NUM_TOPICS ranging between 7
and 12, an LDA with Collapsed Gibbs algorithm was executed to evaluate the coherence score of
the processed models. Table 1 depicts the coherence scores of the models depending on the
number of topics. Figure 5 illustrates the coherence score graph of the processed models.
Table 1
Coherence score of the processed models
Number of topics Coherence score
7 0.4486
8 0.3656
9 0.3965
10 0.317
11 0.3893
12 0.477
Figure 5: Coherence score graph of the processed models
Based on Table 1, it can be seen that the most appropriate number of topic segments is twelve
topic segments. Therefore, to analyze the results of the Latent Dirichlet Allocation with Collapsed
Gibbs, a model with 7 topics will be used.
5. Results
Based on the results of Latent Dirichlet Allocation with Collapsed Gibbs on war-related Ukrainian
news, a comprehensive analysis was conducted to generate Table 2, which illustrates the topic
number alongside 20 keywords that distinctly characterize each of the identified topics.
After obtaining the results of the LDA with Collapsed Gibbs method, namely twelve identified
topics and twenty keywords for each topic, a thorough analysis of the keywords was conducted
to understand the context of news related to the topic of war. As a result of the analysis, Table 3
was formed, which illustrates the approximate context for each topic.
Table 2
Topic Modeling Results of War-related Ukrainian News Using LDA with Collapsed Gibbs
Topic number Top 20 words
Topic 1 [agency, spirne, protective, safe, education, threat, metro, simultaneously,
senate, house, representatives, direct, shutdown, negative, impact, engage,
internal, regulatory, underground, exhaust]
Topic 2 [forces, armed, destroy, kill, loss, agency, russian, february, form, late, tank,
contact, russia, total, represent, invader, russians, troop, continue, artillery]
Topic 3 [armed, forces, kill, russian, destroy, agency, loss, december, parenthesis,
represent, armoured, tank, reproduction, contact, confirm, distribution,
continue, invader, figure, combat]
Topic 4 [chicherina, senate, protective, safe, education, threat, metro,
simultaneously, house, underground, representatives, direct, shutdown,
negative, impact, engage, regulatory, exhaust, chamber, pivnichne]
Topic 5 [armed, forces, loss, russian, destroy, kill, agency, support, represent, late,
parenthesis, liberation, carrier, combat, armoured, artillery, russians, form,
total, continue]
Topic 6 [forces, armed, agency, loss, kill, destroy, russian, liberation, carrier, russians,
tank, support, war, total, confirm, figure, combat, contact, source, form]
Topic 7 [forces, armed, loss, destroy, russian, agency, kill, carrier, russia, liberation,
figure, february, invader, total, reproduction, support, continue, parenthesis,
confirm, armoured]
Topic 8 [troop, reproduction, armed, senate, safe, education, threat, metro,
simultaneously, house, regulatory, representatives, direct, shutdown,
negative, impact, engage, internal, protective, exhaust]
Topic 9 [armed, forces, russian, agency, loss, destroy, kill, continue, source, figure,
liberation, distribution, february, combat, war, confirm, represent, form,
artillery, russia]
Topic 10 [forces, armed, kill, destroy, loss, agency, russian, late, contact, combat,
source, invader, armoured, reproduction, war, total, support, distribution,
figure, artillery]
Topic 11 [forces, armed, russian, kill, agency, destroy, loss, december, troop, source,
distribution, february, russia, parenthesis, artillery, war, tank, figure, confirm,
armoured]
Topic 12 [forces, armed, kill, agency, destroy, loss, russian, war, russians, february,
carrier, troop, invader, armoured, artillery, continue, reproduction, russia,
combat, confirm]
Table 3
Topic context analysis
Topic number Topic context analysis
Topic 1 Discussion of safety measures during wartime. Utilization of
shelters, underground infrastructure as shelters.
Topic 2 Consequences of military actions. Report on enemy military
operations in February.
Topic 3 Consequences of military actions. Report on enemy military
operations in December.
Topic 4 Discussion of the consequences of shelling on the civilian
population
Topic 5 Report on military casualties due to defensive or offensive
actions.
Topic 6 Report on military casualties due to defensive or offensive
actions.
Topic 7 Report on military losses resulting from defensive or offensive
actions. Discussion on support for Ukraine in conducting military
operations.
Topic 8 Discussion of threats resulting from military actions.
Topic 9 Consequences of military actions. Report on enemy military
operations in February.
Topic 10 Report on military casualties due to defensive or offensive
actions.
Topic 11 Report on military casualties due to defensive or offensive
actions.
Topic 12 Consequences of military actions. Report on enemy military
operations in February.
Table 3 shows the results of the analysis of the approximate context of the topics based on
keywords.
For topic 1, the most characteristic keywords are "safe", "education", "threat", "metro",
"shutdown", "negative", "impact". Based on these keywords, the context for topic 1 is likely to be
Discussion of safety measures during wartime and the usage of shelters or underground
infrastructure as shelters.
Topic 4 has very similar keywords to topic 1, although some of them, such as "threat", "direct",
"shutdown", "negative", "impact", most likely indicate consequences of shelling on the civilian
population.
Topic 8, based on the keywords, is similar to topics 1 and 4, but also includes the keywords
"troop" and "protective", which most likely indicate reports in the news about future threats to
the civilian population due to military actions.
Topics 2, 3, 5, 6, 7, 9, 10, 11, 12 were the most difficult to analyze in terms of context. In these
topics, about half of the keywords overlap, such as "forces", "armed", "destroy", "kill", "loss",
"russian", "invader", "artillery". All these words are directly related to the war, as they reflect
events taking place on the territory of Ukraine. However, it is possible to distinguish separate
groups of topics, such as:
• Topics 5, 6, 10, 11, which reflect reports on military losses due to offensive or defensive
actions. Characteristic keywords: "liberation", "combat", "invade"
• Topics 2, 3, 9, 12, which reflect the consequences of military actions. Characteristic
keywords: "total", "loss", "represent", "destroy"
• Topic 7, which likely highlights discussion on support for Ukraine in conducting military
operations. Characteristic keywords: "support", "continue", "armoured"
Thus, based on the conducted analysis of the context, it can be said that among the 12 topics
identified by the Latent Dirichlet Allocation with Collapsed Gibbs method, there are topics that
are semantically similar to each other based on keywords, and in reality, only 6 distinct topic
groups are likely to be distinguished.
For visual interpretation of the results, a distance map of the obtained outcomes was
constructed. Pairwise distances between topics were calculated based on their word
distributions. Common distance metrics were computed using cosine similarity. Subsequently,
Multidimensional Scaling (MDS) was applied to project the topics into a two-dimensional space
while maintaining the pairwise distances between them. MDS transforms the high-dimensional
topic space into a lower-dimensional space, facilitating visualization. After the initial iteration of
data visualization, it was decided to display topics 1, 3, 5, 6, 7, 8, 9, 10, and 12 in a separate plot.
The results of data visualization in the form of a Distance Map are provided on Figure 6 and Figure
7, respectively. To emphasize the lexical patterns within war-related news discourse, a word
cloud was constructed from the set of terms extracted from the news data. As shown in Figure 8,
this graphical tool visually highlights the most common terms to offer a snapshot of the thematic
focus of the field.
Figure 6: Distance Map of All Topic Segments
Figure 6 and Figure 7 depict the Distance Map of the created topic segments. Based on Figure
6, it can be concluded that the most distant topics in terms of semantic content are topics 2, 4, and
11. The rest of the topic segments are closest in semantic content and form a cluster on the graph.
Therefore, topic segments numbered 1, 3, 5, 6, 7, 8, 9, 10, 12 were separated and displayed on a
separate Distance Map. Based on Figure 7, it can be concluded that although the topic segments
are closest in semantic content, they do not intersect on the graph and have their own significance
among other topics.
Figure 7: Distance Map of Topic Segments 1, 3, 5, 6, 7, 8, 9, 10, 12
Figure 8: Word cloud
6. Discussions
Comparing the results of the context analysis of topics based on keywords and the results of
displaying topic segments on the Distance Map graph, it can be said that there are discrepancies
between the conclusions made by humans and those made by the LDA with Collapsed Gibbs
method. This could be due to several factors:
1. Limitation of results. Since only 20 keywords were obtained for each topic, this may lead
to loss of context and insufficient completeness in representing each topic segment. Some
keywords may be common across multiple topics, complicating their interpretation.
Additionally, excluding parts of the vocabulary that are not key to any topic may lead to loss
of contextual information important for a complete understanding of the topic. Thus, analyzing
the results of LDA with Collapsed Gibbs requires careful consideration and interpretation
taking into account these limitations
2. Model parameters. Discrepancies in results may stem from the model being either overfit
or underfit, as the number of iterations of the LDA with Collapsed Gibbs method and the
number of topics were incorrectly set. Insufficient iterations may result in instability in the
results of topic modeling, while too many iterations may lead to overfitting the model and
formation of overly complex or insufficiently generalized topics. Furthermore, improper
selection of the ALPHA and BETA parameters of the LDA with Collapsed Gibbs method may
also distort the results. ALPHA controls the distribution of topics in documents, while BETA
influences the distribution of words in topics. Improper tuning of these parameters may lead
to underestimation or overestimation of the importance of topics or words in the model,
affecting its accuracy and interpretability [14]. Thus, it is important to consider these
parameters when analyzing the results of topic modeling
3. Noise data. Discrepancies in results may also arise from the processed data containing
words that do not carry meaningful content but were not included in the list of stop words.
This is because depending on the context, some words may be important for understanding
the text, while others may not convey significant information. Despite the model being
carefully checked multiple times for the presence of noise words, the results were difficult to
interpret as the obtained keywords for topics were often semantically distant from each other
The question for future research is how these factors affect the reliability and interpretability
of the results of topic modeling using the LDA with Collapsed Gibbs method. Limitation of results,
model parameters, and the presence of noise data may affect the accuracy and completeness of
topic and keyword detection, as well as their interpretability. For future research, it will be
important to explore optimal strategies for model parameter selection, develop more effective
methods for filtering noise data, and devise new approaches to analyzing the context of topic
models to improve the quality and reliability of the results.
7. Conclusions
We can conclude that the application of topic modeling to war-related news using Latent Dirichlet
Allocation (LDA) with Collapsed Gibbs sampling is a valuable tool for identifying distinct content
groups and understanding the context behind key words in these groups. The research conducted
in this study demonstrated the effectiveness of this approach in analyzing war-related news from
a Ukrainian news website.
The analysis of the results showed that the LDA with Collapsed Gibbs method was able to
identify distinct topics related to safety measures during wartime, consequences of military
actions, discussions on threats resulting from military actions, and reports on military casualties.
However, there were discrepancies between the results of the model and the human
interpretation of the topics, which may be attributed to limitations in the results, model
parameters, and the presence of noise data.
To improve the reliability and interpretability of the results, future research should focus on
optimizing model parameters, developing more effective methods for filtering noise data, and
exploring new approaches to analyzing the context of topic models. Additionally, further
investigation is needed to understand how these factors affect the accuracy and completeness of
topic and keyword detection in order to enhance the quality of topic modeling in the field of war-
related news analysis.
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