Dynamic domain analysis for predicting concept drift in engineering AI-enabled software

Social media platforms, such as Twitter, provide a rich source of evolving discussions, which can be leveraged for concept drift detection. Shahzad and Alhoori (2022) demonstrated how Twitter can reveal shifts in public perception towards scientific research, reinforcing the potential of social media as an indicator of evolving concepts in AI-driven systems. Lifna and Vijayalakshmi (2015) collected 30,000 tweets for over a month using Twitter’s API and made topics on tweets by grouping them into four classes: Politics, Entertainment, Foreign Affairs, and General. They assigned a weight-age parameter for each class and used varying window sizes of 10, 20, 30, and 100 minutes for the tweets and built the models.

Bechini et al. (2021) performed an experiment with tweets on vaccination topics in Italy using common methods, such as static and retrain learning. In static learning, the model is trained once new tweets are classified and added to the dataset. This approach minimizes the cost but does not detect any change. In the retrain learning approach, the model is retrained with the incoming data and is better at detecting concept drift. As the models are frequently retrained, the computation cost is extensive. The authors proposed a semantic learning technique containing a set of labeled tweets called a train pool. Each new tweet is evaluated for its similarity with the train pool to be given a weight. The model is retrained based on the weighted samples. The authors specified that semantic learning performed better in detecting concept drift.

Costa et al. (2014) predicted the hashtag of each tweet based on its text by using techniques such as the time window approach (select tweets based on time window), incremental approach (learning and retraining), ensemble approach (combining the different predictions) trained on a set of time windows. Li et al. (2016) built ML models that classify reshare and popularity scores based on concept drift. These models predict the popular (continuous/periodically resharing) content on Twitter and Plurk. They found that the two important features of models were information diffusion and explicit multimedia meta-information. Deshpande and Narasingarao (2019) built an ML model to detect the concept drift on about 40,000 tweets after the Indian government announced three new policies. They used weight-based features (documents are given weight as per the words in the document, which is known as term frequency) and n-gram methodology in feature extraction of the data.

Nishida and Yamauchi (2007) proposed a drift detection method called the “Statistical Test of Equal Proportions” (STEPD). This methodology is based on comparing two accuracies: recent accuracy (for some recent X samples) and overall accuracy (accuracy from the beginning of learning). A significant decrease in these accuracies indicates that there has been a drift.

Drift Detection Method (DDM) uses the error rate in detecting the concept drift, while Early Drift Detection Method (EDDM) (Baena-Garcıa et al., 2006) is based on the comparison of the distribution of distances between the classification errors. EDDM is better able to detect drift than DDM on datasets that have gradual(slow) drift. Cabral and Barros (2018) used three variations of Fisher’s test to detect concept drift. The evaluation results showed that all three variations of the Fisher’s test detected the drift with higher accuracy.

Harel et al. (2014) proposed a method for concept drift detection using the prediction loss distribution and reusing the data multiple times through resampling. In comparison with DDM, EDDM, and STEPD methods, this method achieves higher recall and precision values and is shown to be more robust to noisy data. To detect gradual concept drift, Dries and Ru¨ckert (2009) proposed statistical tests, adapting to data and updating faster when the detection window moves. Linear Four Rates (LFR) (Wang & Abraham, 2015) is another technique used to detect concept drift and identify data points that belong to the new concept. The LFR framework keeps track of the changes in True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) rates given by the classifiers.

Hinder et al. (2024) provided a comprehensive survey on unsupervised drift detection methods with a particular focus on applications in security and infrastructure monitoring. They offer a detailed taxonomy of detection techniques, such as two-sample tests and block-based methods, highlighting the importance of identifying concept drift without relying on labeled data. Their work emphasized the growing need for systems capable of monitoring evolving environments while maintaining operational accuracy.

In the context of deep learning frameworks, Xiang et al. (2023) categorized concept drift adaptation methods into four primary categories: discriminative learning (e.g., multilayer perceptrons, convolutional neural networks), generative learning (e.g., generative adversarial networks, autoencoders), hybrid learning, and other approaches like deep reinforcement learning and deep transfer learning. Their literature review demonstrates that different types of learning models offer varying levels of adaptability to drift, but challenges remain, particularly in real-time drift adaptation for deep learning models in high-dimensional data environments.

Xu et al. (2023) addressed concept drift in IoT systems by introducing the ADTCD framework, which combines a teacher-student model with one-class SVM for anomaly detection and drift filtering. The framework’s dynamic parameter adjustment mechanism allows real-time adaptability, making it robust against changes in data distribution, a key requirement in IoT environments where data streams are volatile and subject to frequent drifts.

In process mining, Adams et al. (2023) proposed a framework to detect and explain concept drift through the construction of a time series of process metrics. Their framework identifies drift points and correlates them with potential root causes using both linear and non-linear relationships, especially in object-centric event data. The explainability feature of their framework allows businesses to not only detect when and where drifts occur but also uncover the reasons behind these changes, which is crucial for decision-making in dynamic business processes.

Halstead et al. (2023) introduced the FiCSUM framework, which leverages a diverse range of meta-features to detect recurring concept drift in data streams. By integrating both supervised and unsupervised meta-features, FiCSUM constructs a comprehensive concept fingerprint that improves the detection and re-identification of recurring drift. Their approach, which dynamically selects the most relevant meta-features, has shown significant accuracy improvements and can be generalized across various datasets, offering a robust solution for recurring drifts in real-time data streams.

Collectively, these studies highlight the diverse approaches to handling concept drift across multiple domains, from deep learning and IoT to process mining. While significant progress has been made in detecting and adapting to concept drift, challenges remain in ensuring real-time adaptability, explainability, and efficiency, particularly in high-dimensional and evolving data environments. The discussed detection techniques are only based on a proportion of the occurrences of the terms in a system, and therefore, only their partial changes are assessed. As such, the main question of “the drift relevancy to the concept” is left unanswered because the drift needs to be evaluated once it is relevant to the domain concepts. Instead, our proposed framework initially highlights the semantically relevant topics to the concept to exhibit the drift.

Deep Learning models assume constant input and output data distribution post-training. However, in real-world applications like image recognition, this assumption is often violated due to dynamic concept semantics, leading to changes in data distribution. Concept drift, where the relationship between input data and class labels changes over time, poses a significant challenge to supervised learning in non-stationary environments. This requires regular model updates to reflect domain changes (Tsymbal, 2004).

Concept drift refers to changes in the conditional distribution of the output given an input, even if the input distribution remains unchanged (Gama et al., 2014). It includes qualitative and quantitative measures such as types of drift (e.g., class drift, covariant drift) and properties like magnitude, duration, path length, and drift rate (Webb et al., 2016).

Current techniques for handling concept drift include: 1) instance selection, which uses a sliding window to select the latest data and build a model; 2) instance weighting, in which data instances are weighted based on their age and relation to the current concept; and 3) ensemble approaches, which uses DL ensemble models to learn and detect concept drifts. The problem with these methods is that they detect drift only after it occurs. Our approach, built on semantics, can anticipate concept drift early based on semantic changes, allowing us to address the drift before AIS performance is compromised.

To clarify the problem of concept drift, we formalize it as follows. Let x ∈ R^D be a data sample in a D-dimensional space, where D is the number of features describing x(f₁, …, f_D). An optimal classifier assigns x(f₁, …, f_D) → C_i, where C₁, C₂, …, C_m are the existing class labels. Probabilistic classifiers select a label with the highest probability by predicting a distribution for P(X|C_i) over all classes i = 1, …, m. For example, the naive Bayes classifiers use Bayes’ theorem to compute probabilities as: 1P(Ci∣X)=P(X∣Ci)P(Ci)P(X)

Assume t_j and t_j+1 are two points in time. In this context, we define concept drift as: 2∃X∈RD∣P(X,Ci)tj≠P(X,Ci)tj+1,i=1,…,m where P(X, C_i) represents the joint probability distribution for X and C_i. Thus, the concept drift may happen due to changes in P(C_i), P(C_i|X), P(X|C_i), or P(X). These changes are inter-related. For example, P(C_i) cannot change without either P(C_i|X) or P(X) changing. It is also worth noting that a change over time in any of the aforementioned aspects of distribution requires a change in P(X, C_i).

While some studies suggest that, in the context of classification, a change in P(C_i|X) (i.e., class drift) is the primary reason for concept drift, others argue that changes in P(X) (i.e., covariate drift) are as important in drifting a concept (Kelly et al., 1999; Tsymbal, 2004). Here, we summarize both of these.

3.1.2

Covariant drift (Changes in P(X))

In contrast to class drift, covariant drift considers changes only in P(X): 4∃X∈RD∣P(X)tj≠P(X)tj+1

For example, assume that a school uses socio-economic factors to accept students. Over time, the demographics of the student base may change, leading to a change in the probability of each demographic factor. Figure 1 shows the classifications under class drift, covariate drift, and our proposed approach. The triangles and circles represent instances of two different classes. Figure 1(a) shows the original model with no drift, correctly classifying instances. Figure 1(b) shows misclassification due to class drift, where class distributions change over time. Figure 1(c) shows misclassification caused by covariate drift, where input feature distributions change. Figure 1(d) shows our approach, which anticipates drift and maintains accurate classification.

Figure 1.

Class and co-variant drifts and how our approach tends to eventually address the domain concept drifts in AIS.

Problem restatement

In our approach, C_i refers to the concept pedestrian C_P, and X represents instances of pedestrians with various appearances. A reliable autonomous vehicle object detector must classify diverse-looking instances of Xs as C_P accurately over time. As pedestrian appearances X vary, P(X) changes, leading to changes in P(C_i|X). The model’s unawareness of these changes results in less accurate detection. For example, pedestrian recognition in autonomous vehicles becomes more challenging during Halloween due to the wide range of costumes worn by pedestrians.

Solution restatement

Figure 2 demonstrates the proposed approach. Initially, we anticipate X (concept-related features) whose drift signals a concept drift. We calculate the current state and significant changes of X within a social context as a probability density function, P(X), and monitor it for anomalies. This approach aims to maintain P(C_P|X)(∀x ∈ X) stability over time by adjusting the DL model as needed. We use social sources like news feeds and social media to predict changes in X, enabling us to anticipate covariate drift before it leads to class drift. This proactive approach allows us to adapt the DL model to preserve detection accuracy before significant drift interferes with AIS visual perception tasks.

Figure 2.

High-level overview of our iterative process.

As illustrated in Figure 3, this study primarily focuses on predicting concept drifts in domainspecific concepts using an integrated approach that combines natural language processing (NLP) and image analysis. By identifying and quantifying the probability shifts in relevant domain features over time, we anticipate potential virtual concept drift before it impacts AI-driven decision-making.

Figure 3.

Current research focuses on the prediction of concept drift, while future work aims at addressing the drift.

Our future work will extend this research to mitigate concept drift through proactive interventions. Specifically, we aim to adapt the training dataset dynamically by employing strategies such as training set selection, training set manipulation, and feature set modification (Barzamini et al., 2022; Perez & Wang, 2017; Zhang et al., 2021). These adjustments will allow for targeted retraining of deep learning models to maintain robustness against evolving domain shifts. This two-step framework, anticipation followed by adaptation, ensures that AI systems can respond to changing real-world conditions with minimal manual intervention.

We selected March 18, 2018, as a potential concept drift date due to a fatal incident involving a self-driving Uber vehicle in Arizona (New York Times, 2018). The goal is to verify the hypothesis that specific events cause significant changes in the probability distribution of relevant terms over their semantic and lexical features. Term frequency was selected as the primary lexical feature for this analysis.

We used a topic modeling algorithm, Latent Dirichlet Allocation (LDA) (Blei et al., 2003), on Twitter content from one month before to one month after the accident. LDA discovers latent semantic structures in text by grouping semantically similar terms under abstract topics. The following sections describe our systematic process for dynamically identifying concept drift.

We selected the top 10 clusters (Kanungo et al., 2002; Wagstaff et al., 2001) with the highest cosine similarity (Rahutomo et al., 2012) to the search query “pedestrian safety.” By visualizing the term frequencies within each cluster, we observed a sudden spike in term frequency on the date of the selected event. Figure 4 illustrates this finding.

Figure 4.

Sudden drift in P(X): The frequency of pedestrian safety-related topics, on March 18, 2018, the day a self-driving Uber ran over a pedestrian in Arizona.

This approach is not aimed at predicting sudden, unpredictable events like car accidents. Instead, it focuses on events for which we can gather social information beforehand. The car accident example was chosen to highlight how such incidents can shift social discussions towards pedestrian safety, demonstrating the concept drift phenomenon.

In Figure 2a, to demonstrate our research idea, we selected C_P to represent the concept “pedestrian” from the automotive domain. C_P is essential for vision-based perception in autonomous vehicles. The challenge is in the limited human ability to intuitively define concepts like “pedestrian” for computers. A typical solution is data-driven training. However, the model’s quality depends on the diversity of the training set representing various pedestrians the AI might encounter.

We implemented two automated processes to iteratively retrieve linguistic and visual information from available sources. Linguistic data were processed using natural language processing (NLP) techniques to identify terms important to humans for describing concept variants. Simultaneously, image processing techniques were applied to a large set of images and videos to extract visually significant features of these concept variants. Although this paper uses the automotive domain and pedestrians as examples, the framework employs generic and domain-agnostic processes.

4.1.2

Visually-important features

While the retrieved features are semantically important to the meaning of “pedestrian,” only those that affect AIS visual perception are relevant. Changes in features that are not visually focused on by the model do not necessarily impact AIS vision and are, therefore, not of interest. Thus, we identify and monitor linguistically important features that also visually impact AIS perception for significant drifts.

We utilized visual sources to identify concept-relevant features recognizable by AIS. Specifically, we selected the Wikipedia-based Image Text Dataset (Srinivasan et al., 2021), which contains 37.6 million image-text samples. For our experiment, we used 5,411,978 entries with English captions. We computed the similarity score of each image caption with the concept of pedestrian. We then selected approximately 25% of the images based on caption similarity, resulting in 1,355,838 textimage entities.

To identify features that are both semantically and visually significant, textual and visual data must be in a consistent format. Therefore, AIS visual perception of images was initially converted into natural language words and phrases.

Despite the success of Region-Based Convolutional Neural Networks (R-CNN) in object detection, they are typically computationally expensive (Ren et al., 2016). Faster R-CNN addresses this by replacing the selective search algorithm with a region proposal network, reducing the number of proposed regions while ensuring precise detection. We adopted the Faster R-CNN model (Ren et al., 2016) as our object detector, equipped with a ResNeXt-101-FPN backbone (Barzamini et al., 2022; Lin et al., 2017; Xie et al., 2017), with a batch size of 8, and an initial learning rate of 8×10³. Faster R-CNN is originally trained on the COCO dataset for object detection.

To generate image captions, we adopted the multimodal model “One for All” (Wang et al., 2022). This model integrates various cross-modal and unimodal tasks (e.g., image generation, visual grounding, image captioning) using 20 million publicly available image-text pairs for pretraining. We fine-tuned the model on a subset of pedestrian images from the Wikipedia-based Image Text dataset. Figure 5 shows examples of Faster R-CNN object detection and “One for All” caption generation tasks on two pedestrian images. The outcome of this step was 3,671 image captions. Each caption contains multiple terms(words). These terms were used to calculate similarity scores with the concept of “pedestrian.” The most significant terms related to “pedestrian” were then selected.

Figure 5.

Model-generated captions and detected objects.

Figure 2b We used Twitter (rebranded to X) to extract recent contextual information relevant to the concept of “pedestrian” as it reflects common daily conversation topics. Twitter was chosen over other social media platforms like Facebook and Instagram because it is primarily used for sharing ideas and thoughts. Additionally, recent account suspensions on Twitter indicate stricter policy controls compared to other platforms.

We selected autonomous driving failures as a potential trigger for concept drift in pedestrian detection. These unpredictable events cannot be prevented, but they provide a relevant scenario for safety analysis. By anticipating sudden changes in pedestrian appearance before they occur, we can prepare the AIS in advance to prevent failures.

Figure 2c To study concept drift, we tracked the changes of important terms within social conversation topics. The following process was implemented to consistently measure the relevancy of common topics to the pedestrian concept.

4.3.2

Identifying significant topic drifts

Figure 6 illustrates the pattern of autonomous Tesla and Uber accidents in 2018 and 2019. Terms defining the pedestrian concept were more prevalent in Twitter conversations during these accidents, but their significance diminished post-event. Interestingly, the Tesla accident on July 29, 2016, was overshadowed on Twitter by discussions about the Tesla and SolarCity merger on July 31, 2016. The unique trend observed during the 2016 Tesla accident was due to a major concurrent event that dominated the discussion topics. This issue can be addressed by filtering topics for relevance. Despite this, the identified trends validated our approach of using social discussion topics to predict sudden concept drifts, which could adversely affect the perception of AI-based software.

Figure 6.

The change in mean similarity scores for “pedestrian” in social topics before, during, and after car accidents.

4.4

Event-based drift specification (X)

Figure 2d: Finally, to identify ∀x ∈ X where P(x, C_P)_t≠P(x, C_P)_t’, we calculated P(x ∈ X)_t for each term in pedestrian-related social topics. This probability indicates whether the occurrence of x significantly changed within the top social conversation topics during each timeframe and was computed as follows: 6P(x∈X)t=∑i=1TtOcc(x)itW where Occ(x)_t is the frequency of x occurring in each topic, formed in each time frame t, and W is the number of total terms in social topics, formed in time frame t. For example, the probability of a term like “crash” might increase in the event of a car accident.

To understand how the significance of each x changes over time, we computed the probabilities of each x. These probabilities were calculated at three different points: before, during, and after the targeted time frame. We then calculated the probability changes from before to during, during to after, and before to after the incident by finding the differences in probabilities obtained from Equation 6.

Considering the time window of an event’s occurrence as t₁, then t₀ represents the time window before the event. For simplicity of representation, we refer to the absolute value of P(X_t1) – P(X_t0) as P_Xt, referring to the probability variance of the terms in X before and during the event.

Assuming P(X_t0) and P(X_t1) for all terms follow a normal distribution, their probability density function resembles the curve in Figure 7. The probability of values within each interval corresponds to the interval’s area under the curve. For example, in a normal distribution, approximately 95.4% of the data typically appear within the μ ± 2σ range. This range represents normal data points. To identify X, we selected terms whose probability changes deviated significantly from most data points. We calculated the mean and standard deviation of these probability differences. The most significant terms, with notable appearance drift, were those whose probability changes fell outside the 95.4% range of the data. The range μ ± 2σ measures these threshold values, as illustrated in lighter blue in Figure 7.

Figure 7.

The Gaussian probability density function shows the interval probabilities as areas under the curve.

To detect significant shifts that are likely to indicate potential sudden concept drifts, we focused on data points that behaved differently from the majority of instances. The terms whose probability variance falls within the same interval as the majority of terms are not of interest. Instead, we are interested in the terms that exhibit higher appearance variance from before to during the timeframes of the incident, as these are relevant to the problem of concept drift.

In a normal distribution, the left and right probability intervals are calculated as: 7PXt≤μ−2σ and PXt≥μ+2σ

These terms showed a drastic change in probability during the event. Figure 8 illustrates these terms, with their probabilities before and during the incident shown in blue and orange bars, respectively. During the Tesla accident in 2016, terms such as {acquired, deal, merger, minibus, porsche, shop, solarcity} saw an increase in probability, mainly due to the Tesla and SolarCity merger. In the Uber accident in 2018, terms like {cars, crosswalk, driving, police} increased in probability, directly relating to the accident and pedestrian context. For the Tesla accident in 2019, terms such as {crash, musk, rebuilder, safer, street} showed an increase in probability, also relevant to the pedestrian concept. It is notable that the terms related to the Tesla 2019 and Uber 2018 accidents are closely tied to the concept of pedestrians, whereas the Tesla 2016 terms were more about the business merger than the accident itself.

Figure 8.

The set of terms and their probability shifts in the car accident data.

The remaining terms, with P(X_t0) or P(X_t1) within μ ± 2σ, are still significant and widely discussed on social platforms. However, their probability changes followed the same behavior as the majority (about 95.4%) and thus were not related to the concept changes discussed on social media.

In summary, although car accidents are unpredictable, this example shows the feasibility of identifying concept drift. We specified the concept semantically and visually using common terminology and continuously monitored concept-related features in contexts like news or social media. Sudden shifts in the probability of these features can indicate drift early before it impacts AIS performance.

We collected data from three weeks around Halloween for the years 2017 to 2019, resulting in 40,860, 29,525, and 43,897 tweets, respectively. After preprocessing, we classified data into topics for each period: before, during, and after Halloween.

Figure 9 shows the mean similarity scores between pedestrian-relevant topics and daily social topics before, during, and after Halloween in 2017, 2018, and 2019. The x-axis represents the time windows, and the y-axis represents the mean similarity score of pedestrian-related features to these topics. Notable drifts in similarity to the pedestrian concept were evident during Halloween in 2018 and 2019, indicating an increased discussion of the concept during the event. These drifts highlight the need for human analysts to investigate concept changes and determine whether they require addressing within the AI model. Significant visual drifts may necessitate incorporating pedestrian costumes into the training dataset and fine-tuning the AIS.

Figure 9.

The change in mean similarity scores for “pedestrian” in social topics before, during, and after Halloween.

The 2018 and 2019 Halloween trends show increased relevance of pedestrian-related terms during the event, which subsequently decreases afterward. In contrast, the 2017 Halloween data is dominated by attack-related tweets due to a terrorist attack in New York City, overshadowing pedestrian-related discussions. Despite this, the extracted data still provide insights into the dominant incident and suggest potential feature removal to retrieve pedestrian-related information.

In Figure 10, during Halloween 2017, 2018, and 2019, terms like {nyc, shouted, truck}, {halloween, kids, safe, streets}, and {detection, dusk, safe, trooper} are identified due to their sudden increase in social topics. These changes draw attention to concept drift early, allowing potential issues to be addressed before AIS failure occurs.

Figure 10.

The set of terms and their probability shifts in the Halloween data.

We selected three major airplane crash events: the California crash (2020), the Washington crash (2022), and another Washington crash (2025), as shown in Table 4. As in the original study, we analyzed the before, during, and after periods of each event, spanning one week each. GDELT provided a vast collection of news articles, with millions of entries per crash time duration, from which we identified articles containing the term “airplane.” The dataset for each event was structured similarly, allowing for a comparative analysis of term frequency variations over time.

To ensure consistency, we applied the same NLP and computer vision (CV) processing techniques as in our original study. The NLP component involved preprocessing the news articles by removing digits, stopwords, and duplicate entries, followed by tokenization and lemmatization to normalize extracted terms. This helped us retrieve 197 meaningful terms related to airplane crashes from sources such as Google Books, ConceptNet, and the Wikipedia-based Image Text (WIT) dataset.

For the CV component, we used Faster R-CNN with a ResNeXt-101-FPN backbone for object detection and applied the “One for All” multimodal model to generate textual descriptions from images. These image captions were then processed similarly to the textual data, resulting in 585 visual terms linked to airplane-related events. We then computed SBERT-based similarity scores between NLP and CV terms, selecting the top quartile of the most relevant term pairs for analysis.

The results show a clear increase in the probability of terms associated with airplane crashes during the event period across all three incidents. As illustrated in Figure 11, terms such as “crash” and “helicopter” emerged prominently in the 2020 California crash, while the 2022 Washington crash saw an increase in words like “pilot,” “police,” and “Patterson”, the last referring to an individual involved in a widely reported airplane-related threat. In the 2025 Washington crash, the recurrence of “crash” and “helicopter” corresponds to an in-air collision between a plane and a helicopter, highlighting how the framework successfully captures event-driven linguistic shifts.

Figure 11.

Probability shifts of terms in the Airplane Crashes dataset.

Further validating this trend, Figure 12 shows the change in mean similarity scores for the term “airplane” across the three events. The similarity scores consistently peak during the crash period, reflecting a heightened contextual association with airplane-related discussions. In some cases, such as the Washington 2022 crash, the similarity score declines post-event, suggesting that media coverage quickly shifted focus. Conversely, in the Washington 2025 crash, the similarity score continues to rise even after the event, indicating sustained media attention and prolonged discussion.

Figure 12.

The change in mean similarity scores for the term “airplane” in social topics before, during, and after the Plane Crash.

These observations reinforce the framework’s capability to detect evolving discourse patterns related to real-world incidents. The consistent patterns across different events and media sources suggest that this approach is adaptable beyond a single domain, making it effective in monitoring concept drift in various contexts.

These findings reinforce the generalizability of our framework by demonstrating its effectiveness across different domains and data sources. The ability to detect concept drift in both pedestrian and airplane-related events confirms that our approach is not domain-specific but adaptable to various real-world contexts. This suggests potential applications in monitoring concept evolution in industries such as transportation, healthcare, and finance. Moreover, by transitioning from social media (Twitter) to news-based data (GDELT), we validate that our framework is not restricted to a particular type of information source, making it robust for analyzing evolving concepts across diverse domains.