=Paper=
{{Paper
|id=Vol-3740/paper-228
|storemode=property
|title=Oppositional Thinking Analysis: Conspiracy Theories vs Critical Thinking Narratives
|pdfUrl=https://ceur-ws.org/Vol-3740/paper-228.pdf
|volume=Vol-3740
|authors=Prabavathy Balasundaram,Karthikeyan Swaminathan,Oviasree Sampath,Pradeep Km
|dblpUrl=https://dblp.org/rec/conf/clef/BalasundaramSSK24c
}}
==Oppositional Thinking Analysis: Conspiracy Theories vs Critical Thinking Narratives==
https://ceur-ws.org/Vol-3740/paper-228.pdf
Oppositional Thinking Analysis: Conspiracy Theories vs
Critical Thinking Narratives
Notebook for PAN at CLEF 2024
Prabavathy Balasundaram1,† , Karthikeyan Swaminathan1,*,† , Oviasree Sampath1,† and
Pradeep Km1,†
1
Department of CSE, SSN College of Engineering, Rajiv Gandhi Salai, Chennai, Tamil Nadu, India
Abstract
Conspiracy theories [1] are complex narratives that attempt to explain the ultimate causes of significant events
as cover plots orchestrated by secret, powerful, and malicious groups, whereas critical thinking on the other
hand is the process of objectively analyzing and evaluating information to form a reasoned judgment and putting
them forth for the public view. Identifying conspiracy theories using Natural Language Processing (NLP) models
is challenging because it is hard to tell them apart from critical thinking. Mislabeling critical messages as
conspiratorial can push curious individuals towards conspiracy communities, and hence it is highly important to
be accurate in such classifications. The task involves distinguishing between two types of oppositional narratives:
(1) conspiracy narratives, which suggest secret plots by powerful, malicious groups, and (2) critical thinking
narratives, which question major decisions without implying a conspiracy. To achieve subtask 1, a pre-trained
BERT classifier is employed to differentiate between the two classes using a sigmoid activation function. The
model for subtask 2 is a pretrained BERT-based sequence classifier fine-tuned for multi-label classification, which
enables span-level classification of oppositional narratives. This working note paper presents the results of the
Kaprov team at the Oppositional thinking analysis: Conspiracy theories vs critical thinking narratives [2] of PAN
at CLEF 2024 [3],which includes two subtasks.
Keywords
BERT, Multi-label classification, Conspiracy Theories (CTs), Tokenizer
1. Introduction
In the realm of Natural Language Processing, the computational detection and analysis of conspiracy
theories (CTs) within textual data has gained significant momentum [4]. CTs are elaborate narratives
attributing significant events to covert actions by powerful clandestine groups, contrasting with critical
thinking, which challenges mainstream beliefs without endorsing conspiracies. Differentiating between
these is crucial, as mislabeling opposing views as conspiratorial may sway individuals towards extreme
viewpoints. Current research predominantly focuses on binary classification tasks aimed at accurately
distinguishing between conspiratorial and critical texts. Existing methodologies for distinguishing be-
tween conspiratorial and critical texts typically involve leveraging advanced natural language processing
techniques and machine learning models. Some common approaches include:
• Feature-based Classification: Using algorithms like SVMs (Support Vector Machines) [5] or logistic
regression, which analyze word frequencies, n-grams, and syntax to classify texts.
• Graph-based Methods: Representing texts as graphs, where nodes represent entities (e.g., words
or phrases) and edges represent relationships (e.g., co-occurrence). Graph-based methods can
capture structural patterns and semantic relationships indicative of conspiratorial or critical
narratives.
CLEF 2024: Conference and Labs of the Evaluation Forum, September 09–12, 2024, Grenoble, France
*
Corresponding author.
†
These authors contributed equally.
$ prabavathyb@ssn.edu.in (P. Balasundaram); karthikeyan2210394@ssn.edu.in (K. Swaminathan);
oviasree2210386@ssn.edu.in (O. Sampath); pradeep2210432@ssn.edu.in (P. Km)
© 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
• Sentiment Analysis: Analyzing the sentiment expressed in texts can provide insights into whether
the text is promoting conspiratorial beliefs (e.g., distrust, fear) or engaging in critical discourse
(e.g., skepticism, questioning).
Subtask 2 focuses on token-level classification within oppositional narratives, distinguishing between
conspiracy theories and critical thinking. It aims to identify specific text segments—goals, effects, agents,
facilitators, objectives, and negative effects—using advanced NLP techniques. This approach enhances
understanding of nuanced narrative elements for effective content moderation and societal discourse
analysis. The approach includes:
• Topic Modeling: Techniques like Latent Dirichlet Allocation (LDA) [6] or Non-Negative Matrix
Factorization (NMF) [7] can uncover latent topics within texts, revealing prevalent themes or
ideologies associated with conspiratorial or critical narratives. By identifying dominant topics,
these methods contribute to understanding the discourse’s thematic focus and distinguishing
between different narrative types.
• Contextual Embeddings: Utilizing pre-trained contextual embeddings like BERT ( Bidirectional
Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformer) can
capture nuanced contextual information within texts, enabling models to discern subtle linguistic
cues indicative of conspiratorial versus critical narratives. These embeddings enhance model
performance by integrating rich contextual understanding into classification tasks.
The two tasks discussed in this paper and their successful implementation collectively advance the
field by enabling automated detection and analysis of conspiratorial narratives, facilitating nuanced
understanding and effective management of such discourse in various domains, and thereby create
stable and peaceful platforms for discussion on public health issues.
2. Task and Dataset Description
There are two tasks that have been worked upon, the first involves distinguishing between critical and
conspiracy texts, while the second focuses on detecting elements within oppositional narratives. The
dataset contains Telegram messages in English and Spanish. Both the tasks are performed exclusively
in English.
Subtask 1 involves classifying texts into two categories: (1) messages that critically question public
health decisions without promoting conspiracy theories, and (2) messages that attribute pandemic
or health decisions to secret, influential conspiracies. Each text in the dataset is labeled as either
CONSPIRACY or CRITICAL. Evaluation of model performance is done based on Matthews Correlation
Coefficient (MCC) [8], with a baseline established by a BERT classifier [9].
Subtask 2 involves a token-level classification challenge where the goal is to identify specific text
segments that represent essential elements in oppositional narratives. Each text data in the input dataset
contains span texts along with their starting and ending positions and the type of oppositional narrative
the span text belongs to out of: AGENT, FACILITATOR, VICTIM, CAMPAIGNER, OBJECTIVE, and
NEGATIVE EFFECT. The performance of models is evaluated using the macro-averaged span-F1 score,
which assesses overall accuracy across all span categories.
3. Data Pre-Processing
This section outlines the process of preparing data for the two tasks.
3.1. Subtask 1 : Distinguishing between critical and conspiracy texts
In the data pre-processing stage for subtask 1, the dataset is initially split into two subsets: one for critical
messages and another for conspiracy messages . Each subset is filtered based on the “category” column
values. Exploratory Data Analysis (EDA) [10] begins with a count plot to visualize the distribution
of categories (“CRITICAL” and “CONSPIRACY”) [Fig. 1]. This provides an initial understanding of
the dataset’s class distribution. Following EDA, data cleansing involves checking for missing values.
Addressing any missing data ensures the dataset is ready for subsequent steps such as tokenization,
feature extraction, and model training for binary classification.
Figure 1: Visualization of category distribution in the dataset.
3.2. Subtask 2 : Detecting elements of the oppositional narratives
Subtask 2 pre-processing starts with extracting annotations from each JSON (JavaScript Object Notation)
entry, gathering crucial details about the relevant text spans and their corresponding categories. This
step prepared the dataset for subsequent pre-processing, ensuring its alignment with the machine
learning pipeline. Post annotation extraction, the Hugging Face AutoTokenizer [11] tailored for BERT
models was employed to tokenize the dataset. Tokenization converted raw text sequences into numerical
token IDs suitable for ingestion by the BERT-based model. To meet BERT’s input specifications, a
truncation strategy was applied to handle sequences exceeding the model’s maximum input length.
This approach maintained consistency in sequence lengths across the dataset, optimizing computational
efficiency during training and evaluation phases.
4. Methodologies Used
4.1. Tiny BERT Text Classifier
The Tiny BERT Text Classifier model [12] is a variant of BERT optimized for English text classification
tasks, specifically focusing on the SST (Stanford Sentiment Treebank)-2 dataset [13] for sentiment
analysis. Built on transformer architecture, this model enables bidirectional understanding of language
nuances, enhancing accuracy in classifying sentences as either critical or conspiracy in nature. This
capability is crucial for distinguishing between texts that question public health decisions (critical) and
those that attribute them to malevolent conspiracies (conspiracy). By leveraging bidirectional context,
these models can capture subtle linguistic cues that differentiate between these two types of narratives
effectively.
4.2. Enhanced Multi-label BERT Classifier
Methodologies of subtask 2 typically involve initial dataset preparation by sourcing annotated text
spans and categorizing them for training, validation, and test sets to ensure unbiased model evaluation.
Utilizing tools like AutoTokenizer from Hugging Face’s Transformers library [11], raw text sequences
are tokenized into numerical token IDs, with strategies like truncation and padding managing sequence
lengths. Model selection focuses on transformer-based architectures pretrained on extensive text
corpora, fine-tuned for span-level classification using transfer learning techniques. Training optimizes
model parameters with Adam optimizer and Binary Cross-Entropy loss [14], while evaluation metrics
such as span-level F1-score, precision, recall, and micro-averaged F1-score assess model performance.
5. Implementation
To implement subtask 1, the dataset is structured into a format where each text sample is categorized
either as “CRITICAL” or “CONSPIRACY”. The BertClassifier model from keras-nlp.models is then
employed with specific configurations for binary classification. Pre-trained weights are loaded, and a
sigmoid activation function is utilized to facilitate binary output. The model is trained on the training
data to distinguish between critical viewpoints and conspiracy theories regarding public health decisions.
Evaluation is performed on the test set to assess the model’s capability in accurately classifying these
texts. This approach leverages the capabilities of BERT for semantic understanding, thereby supporting
the task’s objective of discerning between critical analyses and conspiratorial narratives in the domain
of public health.
The BERT-Based Multi-Label Text Classifier was implemented in Python using the bert-base-uncased
model architecture from Hugging Face’s Transformers library. The dataset, sourced from JSON files,
contained annotated text spans (span text) categorized into specific classes (category). After partitioning
the dataset into training (70%), validation (10%), and test (20%) sets, annotations were extracted to
prepare the data for tokenization. The Hugging Face AutoTokenizer [11] was employed to tokenize the
text sequences into numerical token IDs, with a truncation strategy applied to handle sequences longer
than BERT’s maximum input length. The model was fine-tuned for multi-label classification, optimizing
with the Adam optimizer and Binary Cross-Entropy loss function [14] over multiple epochs. Evaluation
on the validation set involved monitoring metrics such as accuracy, precision, recall, and F1-score
to ensure model performance. Finally, the trained model and tokenizer were saved for deployment,
emphasizing reproducibility and scalability in future applications. The BERT-Based Multi-Label Text
Classifier was implemented in Python using the bert-base-uncased model architecture from Hugging
Face’s Transformers library. The dataset, sourced from JSON files, contained annotated text spans
(span text) categorized into specific classes (category). After partitioning the dataset into training (70%),
validation (10%), and test (20%) sets, annotations were extracted to prepare the data for tokenization.
The Hugging Face AutoTokenizer was employed to tokenize the text sequences into numerical token
IDs, with a truncation strategy applied to handle sequences longer than BERT’s maximum input length.
The model was fine-tuned for multi-label classification, optimizing with the Adam optimizer and Binary
Cross-Entropy loss function over multiple epochs. Evaluation on the validation set involved monitoring
metrics such as accuracy, precision, recall, and F1-score to ensure model performance. Finally, the
trained model and tokenizer were saved for deployment, emphasizing reproducibility and scalability in
future applications.
6. Results and Analysis
Based on the provided results for subtask 1 and subask 2 in English, the performance of the BERT-
Based Multi-Label Text Classifier was evaluated. For subtask 1 [Table 1], focusing on conspiracy
and critical categorization, the model achieved an F1-macro score of 0.3700 and 0.8255, respectively,
indicating moderate performance in identifying critical texts compared to conspiracy-related ones . In
subtask 2 [Table 2], which evaluated span-level F1-score and micro-averaged F1, the model attained
scores of 0.0150 and 0.0600, respectively, suggesting challenges in precise span-level predictions. The
implementation utilized Python with the bert-base-uncased model from Hugging Face’s Transformers
library, leveraging AutoTokenizer for tokenization and fine-tuning with Adam optimizer and Binary
Cross-Entropy loss. The results underscore the model’s effectiveness in critical text classification but
highlight areas for improvement in span-level prediction accuracy.
Table 1
Subtask 1 : Distinguishing between critical and conspiracy texts.
Metric Value
MCC 0.3700
F1-MACRO 0.6240
F1-CONSPIRACY 0.4224
F1-CRITICAL 0.8255
Table 2
SUubtask 2 : Detecting elements of the oppositional narratives.
Metric Value
span-F1 0.0150
span-P 0.0261
span-R 0.0165
micro-span-F1 0.0600
7. Conclusion
The task Oppositional Thinking Analysis: Conspiracy vs Critical tackles the challenge of distinguishing
conspiratorial from critical narratives in oppositional texts, especially regarding COVID-19. Conspiracy
theories, often depicting events as manipulated by secretive, powerful groups, are complex and hard to
separate from genuine critical thinking. The competition aims to enhance understanding and automatic
detection of these narratives, crucial for content moderation on social media. Differentiating conspira-
torial messages from critical ones is essential, as mislabeling can push individuals toward conspiracy
communities. This task involved developing sophisticated NLP models to discern these nuances for
accurate classification and better content management. The approach included preprocessing steps like
text cleaning and feature extraction using TF-IDF (Term Frequency-Inverse Document Frequency) [15]
and word embeddings. Both traditional machine learning algorithms, such as logistic regression and
support vector machines, and advanced deep learning models, like LSTM (Long Short-Term Memory)
and BERT, were used. Evaluations with metrics such as accuracy, precision, recall, and F1-score showed
deep learning models, especially BERT, outperformed traditional ones. Cross-validation ensured robust-
ness and mitigated overfitting. The methodologies from this competition promise to improve automatic
detection of conspiratorial versus critical narratives, aiding effective content moderation on digital
platforms.
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