Prior work shows contradictory evidence about the usefulness of sentiment signals. Jamil et al. demonstrate correlations between hostile online discourse and hacking incidents, suggesting predictive value. Conversely, Xu et al. and Zimmer et al. highlight that sentiment data are often confounded by bots, coordinated campaigns, or exogenous events, making simple correlations unreliable.

Most machine-learning approaches (SVM, LSTM) achieve higher classification accuracy than lexicon-only methods but still rely on English-only or binary-labeled datasets, which cannot capture nuanced emotions like sarcasm, contempt, or mixed sentiment common in activist communities. Few studies compare models on defacement-specific data, and even fewer addresses how sentiment interacts with technical indicators such as vulnerability disclosures.

According to [13] cyber defacement refers to the unauthorized alteration of a website’s content, typically done with the intent of sending a political or social message, or simply for the purpose of vandalism. In these attacks, the legitimate content of the website is replaced or altered, often with offensive or inflammatory messages, damaging the reputation and trust of the organization being targeted [9] . According to [14] , the motivations behind cyber defacement are diverse and can range from political activism to personal vendettas. Additionally, hacktivism according to [15] , who promote political or social causes, are a common driver for cyber defacement, as perpetrators may alter a website to express dissent against a government, corporation, or ideological group

As stated by Surya et al. [16] , the impact of cyber defacement on an organization can be far-reaching. Beyond the immediate disruption caused by the defaced website, these attacks can lead to significant long-term consequences, including loss of public trust, damage to brand reputation, financial losses, and erosion of customer loyalty. Furthermore, a study by [17] indicated that cyber defacement can serve as a distraction for more severe cyberattacks, such as data breaches or denial-of-service attacks, which can be hidden under the veil of the defacement. This multiplicity of consequences makes the monitoring and prediction of cyber defacement a critical area of focus in cybersecurity.

Defacement attacks are commonly executed by cybercriminals, hacktivists, and other malicious actors who either aim to send a message or demonstrate their capability and control over high-profile targets [13] . According to [18] , cybercriminals may engage in defacement as a diversionary tactic, while hacktivists are more likely to target institutions they oppose, aiming to highlight political issues. The increase in defacement activities observed over the past decade signals a need for improved detection methods and preventive measures.

Sentiment analysis, a subset of natural language processing, has found increasing application in cybersecurity [19] . It involves analyzing the emotional tone of text data, allowing researchers to classify the sentiment of content as positive, neutral, or negative [20] . This method according to Jouini et al. [21] , has been used to gauge public opinion on emerging threats or cybersecurity incidents. Researchers have found that the emotional tone of online content such as discussions, posts, and news articles can offer valuable predictive indicators of impending cyber threats.

Sentiment analysis in cybersecurity primarily focuses on monitoring social media platforms and online forums where discussions about vulnerabilities, exploits, or attacks often emerge first [22] . For instance, the emotional tone expressed in discussions about a specific cyber threat can indicate the level of concern or urgency associated with that threat [23] . Negative sentiment, such as expressions of anger, frustration, or fear, can sometimes precede more aggressive online behaviors, including cyberattacks like defacement [9] .

While sentiment analysis in the context of cybersecurity is still in its early stages, its potential to provide early-warning systems is recognized by many experts [22] . For example, according to [23] , an analysis of online discussions about security vulnerabilities has been used to predict when a newly discovered exploit might be actively targeted by cybercriminals [24] . Additionally, sentiment analysis has also been applied to track public sentiment during ongoing incidents, such as data breaches, where rising anger and dissatisfaction often precede further malicious activities.

In the context of cyber defacement, sentiment analysis could identify emerging negative public sentiment towards specific institutions or governments, signaling a higher risk of defacement attacks [10] . As stated by Davanzo [25] , if aggressive discourse or calls for action gain traction, it could potentially serve as an early indicator that a defacement attack is imminent. However, integrating sentiment analysis as a predictive tool for defacement specifically is still an area that has not been extensively explored in the literature.

Social media platforms like X have become critical sources of real-time intelligence about emerging threats in cybersecurity [26] . These platforms are rich with discussions, debates, and news about ongoing political, social, and cybersecurity-related events [27] . Researchers like Zimmer et al. [28] , have long recognized that online discourse often provides a reflection of public sentiment and can signal shifts in societal attitudes or concerns that may precede cyberattacks. For example, Jamil [27] demonstrated that social media platforms could provide real-time indicators of public concern over specific security incidents, such as data breaches or attacks on critical infrastructure.

The real-time nature of platforms like X makes them particularly useful for tracking discussions related to cybersecurity threats [29] . Keywords and hashtags related to cyberattacks, vulnerabilities, or defacement incidents often gain traction on these platforms, providing an opportunity to monitor shifts in sentiment that may precede actual attacks. Jamil [27] found that sentiment expressed in online discussions about hacking incidents frequently correlates with the subsequent appearance of attacks. However, the connection between sentiment in these online discussions and cyber defacement specifically remains under-researched [13] .

While there is literature on the use of social media as a source of cyber threat intelligence, the relationship between specific sentiment trends in online discourse and the likelihood of cyber defacement has not been thoroughly examined [30] . Although studies have identified sentiment as a predictor of general cyberattacks, the unique nature of cyber defacement, where the public-facing website is altered as a message of protest or discontent, suggests that sentiment in these cases could have a direct influence on the probability of such incidents occurring [31] .

Although sentiment analysis has been applied in various domains within cybersecurity, including general threat detection and attack prediction, there is a notable gap in the literature concerning its specific application to cyber defacement domain [32] . Most research like [33] and [34] has focused on broader trends in cyberattack prediction and indicators such as network anomalies or system vulnerabilities, leaving a gap in the understanding of how sentiment, in online discourse, correlates with the occurrence of defacement attacks.

According to Shu et al. [35] the absence of focused research on sentiment as a predictor for cyber defacement is significant because defacement attacks are often driven by socio-political ideological motivations, which are often mirrored in online sentiment. This gap presents an opportunity for further investigation into how online discourse, particularly hostile or agitated sentiment, may act as an early warning signal for cyber defacement.

Furthermore, there is limited research on the integration of sentiment analysis with technical cybersecurity measures, such as intrusion detection systems and threat intelligence platforms, to create more holistic early-warning systems for cyber defacement [25] . While sentiment analysis has shown usefulness in predicting broader cyber threats, the expressed emotional context of defacement incidents, where attackers often want to send a message or protest, requires a more tailored approach [36] . As such, this study aims to fill the gap by exploring how sentiment in online discourse, platforms like X, may act as a potential predictor for cyber defacement attacks.

At its core, the research draws upon social signal detection theory, which posits that collective emotional expressions in public discourse particularly anger, frustration, or outrage can function as early signals of impending collective action, including adversarial behavior such as hacktivism [37] . In the context of cybersecurity, this theory suggests that online platforms like X may act as digital public squares where grievances are articulated, mobilized, and sometimes operationalized into cyberattacks. The hypothesis that spikes in negative sentiment precede defacement incidents is thus rooted in the assumption that ideologically driven attackers often telegraph intent through affect-laden discourse before executing attacks. The expression for the theory is expressed as in Equation (1) below.

$\delta \left(S_t\right)=\{\begin{array}{c}1\text{if}{S}_t\ge \theta \\ 0\text{if}{S}_t<\theta \end{array}$ (1)

where $\delta \left(S_t\right)$ represents a detected socio-linguistic threat signal.

Complementing this, socio-technical systems theory provides the conceptual scaffolding for treating cybersecurity not merely as a technical domain but as a dynamic interplay between human behavior and technological infrastructure. Cyber defacement unlike financially motivated cybercrime is frequently performative and communicative, designed to convey political or social messages [38] . This performative nature implies that attackers and their audiences co-constitute meaning through shared socio-political contexts, making public sentiment a potential proxy for threat actor motivation and target selection. The study therefore treats sentiment not as noise, but as a structured socio-technical artifact embedded within the threat landscape. Socio-technical systems theory expression in as shown in Equation (2) below.

$R_t=f\left(\delta \left(S_t\right),H,T,C\right)$(2)

where $R_t$ is the dynamic risk score of cyber defacement at time t, and f(.) is a system function integrating social, technical, and contextual interactions.

From a methodological standpoint, the research operationalizes these theoretical premises through computational sentiment analysis, leveraging both lexicon-based models (e.g., VADER) and machine learning classifiers such as Naïve Bayes, LSTM. VADER’s design for short, informal social media text aligns with the linguistic ecology of X, while deep learning models capture contextual and sequential dependencies that may encode implicit threats or collective intent [37] . This hybrid approach reflects a pragmatic epistemology, wherein multiple analytical lenses are combined to mitigate the limitations of any single method particularly important given the challenges of sarcasm, bot amplification, and low signal-to-noise ratios in open-source data.

The theoretical model visualized in Figure 1 conceptualizes a directional, albeit not necessarily causal, relationship between aggregate negative sentiment and defacement incidents, mediated by external catalysts such as geopolitical events, vulnerability disclosures, or institutional controversies. Critically, the framework distinguishes between ambient negativity (broad, domain-level sentiment) and targeted hostility (entity-specific anger or threat language), positing that only the latter possesses predictive utility. This distinction aligns with recent advances in context-aware threat intelligence, which emphasize actor- and asset-centric indicators over generic emotional valence.

In this study, we use Design Science Research (DSR) approach to systematically develop and evaluate a model for sentiment-based cyber-defacement risk detection. DSR is particularly appropriate for problems at the nexus of technology and practice because it focuses on the building of an artefact in this case a hybrid sentiment analysis framework, and its incremental improvement using cycles of design, implementation and evaluation. Within each of the cycles, we collected new data, pre-processed it, re-calibrated the algorithms for classifying sentiment, and re-tested the procedures for correlation to ensure internal validity and external applicability.

The study used data gathered on X because it is commonly used by cybersecurity professionals, hacktivists, and common citizens to share and discuss real-time information about the occurrence of cyber incidents [39] . We harvested 780,000 raw posts from X (Aug 2024-Mar 2025) [40] using cybersecurity-related keywords and hashtags like #defacement, “website hacked”, and “CVE”. After removing duplicates, non-English posts, and irrelevant content, 503,456 posts remained. This large, diverse corpus increases the generalizability of findings across actors and events [41] .

Two complementary techniques were employed: (i) Lexicon-based VADER to handle short, informal text typical of social media as cited by Arief and Samsudin [36] ; and (ii) Machine-learning classifiers (Naïve Bayes, SVM, LSTM) trained on a labeled subset for greater context sensitivity [37] . Sentiment analysis procedure entailed a number of steps through Natural Language Processing (NLP). The raw text was initially pre-processed by deleting stop words, the URLs and the special characters and proceeded by the tokenization procedure which has served to break up the posts in words.

To offer ground-truth of cyber defacement incidents, additional information was taken in the form of publicly reported cyber incident databases, such as the Zone-H defacement archive and other cybersecurity resources. These are the typical sources that we have used in previous research to monitor web defacement and other subsequent cyberattacks [42] .

The correlation between the changes in sentiment over X and actual cyber empirical defacement events was then tested using statistical measures of correlation as well as time-series interpretation. In particular, Pearson correlation coefficient was used to quantify the linear relationship in sentiment trends with attack frequencies, and time-series analysis was also used to identify temporal patterns and time-lagged relationships. The approaches are extensively used in social media-driven cybersecurity studies in order to assess predictive relationships [43] .

A 10% stratified subsample was manually annotated by two independent coders, achieving Cohen’s κ = 0.82 (substantial agreement). Minority sentiment classes were balanced using SMOTE to reduce bias. Model performance was evaluated with macro-F1 and confusion matrices.

All posts were publicly available from dataset [40] ; personally identifying information was removed at preprocessing. The protocol was reviewed by the Co-operative University of Kenya ethics committee and research supervisors, and complied with the Kenyan Data Protection Act of 2019.

We combined descriptive statistics (sentiment distributions, attack frequencies), time-series analysis (daily/weekly trends), and cross-correlation to test predictive lags. Regression and subgroup comparisons such as individuals vs. organizations) are proposed for future iterations to deepen insight.

After filtering and cleaning, 503,456 posts related to cybersecurity remained from an initial 780,000. Missing data were confined to optional metadata fields such as mentions and URLs, which did not affect sentiment scoring. This large, balanced dataset underpins the robustness of the analyses. The analysis of X sentiment data provided some interesting findings related to the data composition, quality of preprocessing and thematic integrity.

The sentiment analysis revealed a tri-modal distribution, with neutral posts (256,039) constituting the majority, followed by positive (203,914) and negative (43,503) sentiments as shown in Table 1 and a bar-graph in Figure 2 below.

The distribution is consistent with prevailing trends in the cybersecurity rhetoric where neutral posts tend to prevail simply because they are information-based. Messages like, “New patch available at CVE-2024-1234” were often assigned the neutral sentiment, whereas messages like, “Another ransomware attack this is getting out of hand” were labeled as negative. The negative classifications were dominated with class and intelligence. The dominance of neutral emotion implied that the data was chiefly non-emotional fact reporting beyond opinion.

The dataset contained 12 distinct attack types, each with approximately 16,000–17,000 instances, demonstrating exceptional balance. The most frequent attack types included Credential Stuffing (16,999 posts), Supply Chain (16,957), and Ransomware (16,924) as shown in Table 2 below.

This balance was especially beneficial to multi-class classification models, since this reduced the likelihood of any single model of attack being favored. As an example, posts related to “phishing attacks against healthcare”, and “DDoS attacks against financial organizations” were comparably represented, allowing the training of the models to have a sufficient level of parity across threat domains.

User engagement metrics indicated moderate interaction levels, with posts receiving an average of 3 reposts (SD = 1.73) and 8 likes (SD = 2.83), this is summarized in Table 3 below.

Distribution of engagement was long-tail; on a log-scale KDE plot in Figure 3 below where most posts received little engagement with only a few achieving greater virality.

The shape of distribution of engagement was long-tailed as shown in the log-scale KDE plot, so that many posts have very little engagement, and only a few posts a lot of engagement. Structurally, both the number of replies and the number of quotes aligned with repost patterns, indicating that the way people interacted with these messages was restricted to the amplification of the message but not to in-depth discussion.

The sentiment distribution of the dataset was also balanced and representative of realistic public conversation of realistic cybersecurity domain. Neutral posts made the highest frequency in the corpus, as users have the propensity to post information based on alerts, technical advisories and news reports. This neutral preponderance of sentiment forms a consistently sure basis of fact-analysis and fact-extraction.

Sentiments that were measured were positive and negative, which albeit less, provided an emotional context to the data. Positive sentiments were frequently mentioned as part of discussions on effective mitigation of threats or support of the community whereas negative sentiment identified some public concern, irritation or criticism in the event of a cyber incident. This mixture of the type of sentiments adds value to the dataset, and it makes a good fit to perform a nuanced analysis in terms of threat perception studies and the way to react to it.

Equally important was the fact that the data were uniformly distributed as it concerned the attacks of different nature, ransomware, phishing, credential stuffing, and SQL injection. This even distribution counteracts a common shortcoming of cybersecurity sample data imbalance that often tends to negatively impact the generalizability and fairness of any predictive model. With the balance of attack-type representation, the machine learning models trained on such a dataset may learn all of the discriminative patterns without synthetic data production and/or weighting mechanisms.

Although this feature contributes to improving the dependability of model evaluation, it also allows the training of multi-label classifiers that are able to recognize simultaneous attack themes in real-time threat feeds. Speaking in the user engagement terms, the dataset has relatively low engagement rates being a typical characteristic of the cyber-security discussions on social media. As opposed to general viral material, cyber-security posts of-ten have the character of notice, technical information, or professional commentary which, quite appositely, obtain few likes, reposts, or comments.

Such a pattern of engagement can be explained by the professional and informational character of the field and confirms the idea that this kind of data reflects the real behavioral conventions in the field of cybersecurity. Consequently, the data would allow constructing a realistic picture of how cybersecurity incidences are notified and perceived in online communities, thus presenting an excellent resource of modeling how users respond to cyber threat ecosystems.

A temporal analysis of post volume as shown in Figure 4 , revealed that neutral sentiment dominated consistently, with positive sentiment showing a slight upward trend over time.

The negative sentiment distribution was fairly flat and did not take a large number of comments therefore indicating that no major cybersecurity incidents could have a significant change in overall sentiment distribution. In one of such examples of the collaboration between posts and spikes on neutral posts, a significant spike in neutral posts appeared when an announcement was made concerning software patches like the example of CVE-2024-1234 resolved.

The proportions of sentiments in a daily basis were recalculated using the balanced model. Indeed, with a number of large hashtag accumulations (DataBreach, CVE, Phishing) the proportion of negative posts now increased more gradually than during the imbalanced period indicating that the previously represented plateau was an artefact of class bias rather than occurrences of a detached status. In Figure 5 below the dashed lines illustrate the DataBreach, CVE and Phishing incidents.

Anecdotal increases in negative sentiment (red line) coincide well with these events, suggesting the balanced model more closely identifies event-related emotional change than its unbalanced counterpart. The new temporal curves follow security event days much more closely, and indicate temporary bursts of negativity during breach disclosures and vulnerability announcements.

A thorough methodology was utilized to develop sentiment analysis and defacement attack prediction models, which included: the feature engineering, temporal analysis, mitigation of biases, and evaluation of models. The outcomes offered meaningful information on effectiveness of various methods and the main bottlenecks of cybersecurity-related text classification. The sentiment analysis was performed to determine the emotive content of posts. The VADER (Valence Aware Dictionary and sentiment Reasoner) tool assigned a certain degree of sentiment in each post either negative, positive or neutral.

To predict defacement using sentiment patterns, a naive Bayes, the probabilistic classifier was trained using TF-IDF features. A LSTM neural network was used to model the sequential text dependencies which, compared to other neural networks, can learn long-term patterns. Such techniques gave a multidimensional look into sentiment changes that point to defacement.

Using VADER as summarized in Table 4 below, the X cybersecurity dataset gave significant information on the sentiment’s dynamics in terms of defacement attack prediction. Analysis revealed differences in the expression of awareness, concern and approval by users to the cybersecurity incidents.

The compound score distribution exhibited a bimodal pattern, with a notable concentration at the neutral point. Approximately 25% of posts clustered sharply at a compound score of 0, reflecting neutral sentiment. These typically consisted of factual updates such as “Patch released for CVE-2025-1234”, indicating information sharing without emotional expression.

The plot of distribution showed that there was strong spike on 0 (neutral) and the negative scores followed but were relatively short. The descriptive statistics in Table 5 above reinforced the sense of overall slight positive tendency (mean = c. 0.15) and wide dispersion (std = c. 0.39). It was a right-skewed distribution with positive tail, with a mean value of compound score 0.15 and maximum 0.97 (see Figure 6 ). The Posts within this range tended to be commendation or reassurance, like “Excellent work mitigating DDoS attack!”. On the other hand, lesser negative tail was observed and the lowest score was registered as −0.95. These were typically emergency notices and included things like, “critical vulnerability-exploits already in the wild!”, and documented extreme risks and threats.

The analysis of the sentiment of the VADER analysis produced some important insights toward predicting defacement attacks. Negative sentiment tail, consisting of around 5 percent of posts, held evident high-risk indicators that could act as early-warning signs. As an example, posts having overt defacement cognates such as the phrase “Website defaced by XX hacker group” (VADER score: −0.82) had a very low positive score. Coupled with other lexical features like hashtags, e.g., #defacement, these hints may prompt immediate notifications to security teams, whom can use this knowledge to preemptively act to minimize the impact of the compromised account.

Nonetheless, the analysis also indicated false negative occurrence in the neutral expression range (scores 2 0). Posts that refer to temptingly innocuous wording, like Domain X is down, were quite often followed by subsequent defacement incidents. This re-affirmed the shortcomings of lexicon-based processes and the imperativeness of a contextual analysis to record implicit threats. Semantically rich models such as BERT or LSTM model could solve this discrepancy.

A positive sentiment paradox was interestingly realized where positive-scoring posts (e.g., New CVE disclosed great find!) led to attacks. This association implied that commercial adversaries are on the defensive side, and attack on the latest security announcement is an offensive score against the security announcements. This makes sentiment-based prediction difficult, because in such instances, it will be needed to distinguish between goodwill (genuine positive sentiment) and ill will (malicious intent).

This can be circumvented in future models by designing in temporal features, e.g., by including time delay between CVE advertisements and incidents or user reputation scores to warn of high-risk positive posts. All of these results support the importance of a multi-layered defacement prediction mechanism that combines the speed of VADER with the precision of contextual models as means of detecting both direct threats and less clear, early-warning signs.

The daily aggregate head indicates a steady negative-sentiment rate of the mid-0.12s, zero defacement mentions initially, during the sampled days. This is an indication of defacement-related posts being sparse as compared to the total volume.

The study also determined the Cross-Correlation Function (CCF) depicted in Figure 7 . All across lags were below in practice this implies that one or either of the standardized series had zero variance on the overlapping business in each lag. Defacement mentions were more likely the reason since they were zero-valued on most days, thus the correlation calculation resulted in an undefinable series; the standardization collapsed. Due to this covariation dependency, sparse binary events (defacement mentions) can hardly be informative of linear correlation at a daily resolution unless there are sufficient positive days.

Based on the results of the correlation analysis above, the second objective of the study which was to investigate whether spikes in negative sentiment on the X platform could act as a predictive or coincident indicator to cyber defacement incidents was achieved. As shown in the provided time-series and cross-correlation charts, the analysis, nevertheless, showed that the relationship between these variables is weak, and not predictive. The time-series data indicated that there were the loose co-movement periods, including the end of 2024, when the positive mention in the negative sentiment and defacement mentions have coincided.

This might point toward some joint-cause, such as a big geopolitical event, or realization of a serious software vulnerability, which might have taken a toll on the mood as well as hacker activity. Nevertheless, one of the main issues with this relationship was that it was volatile; that is, there was a significant divergence in the spring of 2025 wherein an extreme negative sentiment did not result in an increase in defacement activity. The fact that this is inconsistent at once undermines the force of a direct causal connection.

The cross-correlation analysis in Figure 8 gives the most pivotal insight in the research objective. The figures indicate that the greatest association is at lag zero days but this relationship is weak. This supports the view that the only relationship that can be identified is contemporaneous; sentiment and defacement mention co-exist within the same time period. More importantly, the level of negative sentiment on earlier days does not have any meaningful relationship with later defacement incidents as there is no statistically significant positive correlation with the negative lags. This conclusively shows that general perceptions, which operate as proxies to negative sentiment in this measure, do not Granger-cause defacement with any significance and hence are not a recommendable leading indicator.

This result is consistent with a subdued trend in the evidence base regarding social media sentiment and cyber threat prediction. Although old studies tend to suggest a general correlation between negative social discourse and cybercrime, recent studies put emphasis on the issue of specificity. As an example, research on more narrow risks, such as those related to data breach, has shown that company-specific sentiment can be a more valuable measure than negative sentiment.

In a similar way, studies on hacktivism have demonstrated that geopolitical events may be a shared antecedent to online outrage as well as an attack, hence an abundant non-causal relationship with no predictive lag, just like seen in this data. That implies that the overall dependability of negativity on X might be too high and inclusion rate to pick up on particular motivations of the actors of defacement. What this data shows is that overall negative sentiments about X cannot be used reliably as a gauge of cyber defacement attacks. The poor contemporaneous correlation points to the idea that they are both most likely reactions to external, real-world catalysts, as opposed to one abusing the other.

Based on the analysis of the data at hand, some sentiment indicators were found that can be possibly used to correlate with the occurrence of a cyber defacement attacks and would give an indication of how the online communities tend to pre-behave before attacking.

A significant metric is negative sentiment spikes where a sharp rise in the negative sentiments in social media sites indicate an increase in the cyber defacement volume. Case in point, the negative sentiment level was higher on the days like 2024-08-01 and 2024-08-05 when there were more instances of defacement reported. The study showed that the surge in negative feeling on social media such as X can be used as the early signs of cyber threats, implying that the outbursts of negativity are potential symptoms of frustrations or plans to attack a particular entity. In order to enhance early detection, one could track the changes in negative perception towards particular events or scandals to define the possible risks.

The other indicator that is really significant is powerful negative sentiment whereby, the volume of posts with negative sentiments of below −0.5 are high. The presence of this type of sentiment intensity was found at a time when the cyber defacement incidents were increasing albeit weakly. A study by Zhang et al. [44] also emphasized the need to monitor strong negative sentiment, especially when responding to arising threats or vulnerabilities as the strong sentiment indicators would indicate high levels of risk online. By putting a focus on the high-attribution scores particularly when combined with an identified critical vulnerability this can assist in organizations to be able to predict more advantageously when a defacement or other cyberattack is likely to happen.

The existence of particular emotions, more especially anger, and frustration, was also found to be a critical antecedent to cyber defacement. The more posts in the posts on these feelings, the more incidences of defacement arose. The same conclusion is drawn by [45] , who have found that the anger and frustration are ranked in the list of most common hostile emotions, which accompany cyberattacks in the hacktivist community. Emotion detection systems Most systems are based on monitoring facial expression or tone of voice and as such are most useful at detecting anger or frustration. Emotion based detection systems can therefore be extremely valuable tools in early warning detection of potential threats so enabling a swift response by the cybersecurity team.

Also, sentiment deviation with respect to normal trends proved to be a good indicator. Disturbances to the baseline sentiment behavior of a given entity were identified to have significant correlation with the increased prevalence of cyber-attacks. This is consistent with the observations by [46] , who indicated that a sudden rise in the negative sentiment compared to normal behaviors could be used to presage a threat. The continuous tracking of the baseline sentiment progress and immediately identifying a significant departure may enable organizations to have an early warning against risks.

Sentiment in reaction to particular vulnerabilities or incidences was found to correlate with defacement incidences. As some defacement cases were preconditioned by online debates about vulnerabilities or controversies about organizations, sentiment monitoring in such scenarios could assist in predicting when the risk of a cyber-attack is increased. This observation is also congruent with another study by [47] , who earlier discovered that topics about vulnerabilities were usually rampant in negative sentiment time points prior to cyber incidences. Monitoring these changes around high-profile vulnerabilities can be a preemptive action of predicting threats before their onset.

A stacked ensemble approach was employed by training a logistic regression model on the probabilities generated by the base models. The logistic regression was formulated as follows:

$\text{logit}\left(p\left(y=1|x\right)\right)={\beta }_0+{\beta }_1\cdot {\text{NB}}_{-\text{prob}}+{\beta }_2\cdot {\text{ARIMA}}_{-\text{prob}}+{\beta }_3\cdot {\text{LSTM}}_{-\text{prob}}$

Meta-learner: Logistic Regression (max_iter = 1000). This allows the ensemble to up-weight whichever signal is most predictive at a given time. The dataset was divided into training and validation sets using a time-ordered 80/20 split. For the BiLSTM model, training incorporated early stopping based on validation loss with a patience of 2 epochs, a batch size of 128, and an initial training duration of 3 epochs, with the option to extend as necessary. The logistic regression model in the stacked ensemble was trained with a maximum of 1,000 iterations to ensure convergence.

The results for the ensemble model developed were discussed as follows where each individual model performance was analyzed and summarized in Table 6 below.

The Ensemble demonstrated stronger overall performance than Naive Bayes. It raised F1 from 0.7496 to 0.8055 and increased Accuracy from 0.8289 to 0.8568, representing a meaningful improvement in balanced utility at the default threshold (0.50). Recall increased from 0.6305 to 0.7300 (≈+15.8% relative), while precision declined slightly from 0.9243 to 0.8985 (−2.6 points). In concrete terms, false negatives decreased by 4070 (15,115 → 11,045), whereas false positives increased by 1261 (2114 → 3375). In security settings, this tradeoff substantially fewer misses for a modest increase in reviews was typically favorable.

Ranking capacity remained unchanged: ROC AUC and PR-AUC were identical for NB and the Ensemble (ROC AUC = 0.9116; PR-AUC = 0.8964). This indicated that NB already ranked examples effectively and that the Ensemble’s gains arose from improved probability mapping/calibration and threshold geometry, which translated similar rankings into fewer misses at the operating point. Calibration improved materially: the Brier score decreased from 0.1323 to 0.1151. The more reliable probabilities supported the use of probability thresholds (e.g., alert at ≥0.70) and facilitated integration with downstream risk models.

In order to improve the current state of early detection regarding cyber defacement threat, a number of sentiment-based indicators can be raised. A Negative sentiment rate is computed each day or hour based upon defacement-relevant traffic including the security, web, and gov domains to help find that there is a spurt in negative impact that is above-or-beyond historical trends that uses a rolling z-score. However, the valence shifts close to defacement lexicon, more specifically in that it only considers sentiment in posts with terms that pertain to defacement like beefed up website, web shell, deface, may also offer more targeted risk visualization.

This is better than examining inter-national negativity because an increase in negativity in this con-text has a stronger relation to possible defacement attacks. Emotion facets, i.e., anger, fear and contempt, lead to defacement actions in many hacktivist contexts, and monitoring such indicators may be used to provide rolling indices (with respect to time) indicating an increased threat level. In addition, threat-intent sentiment can be detected by a classifier that identifies intent statements, and which may be suggestive of an upcoming cyber-attack.

At the organizational level, sentiment cues must deal with brand/asset-focused sentiment. This is tracking bad feelings targeted at domains, assets, or key leaders of any company. Particular statements of such com-ponents as index, landing, or CMS used with negative tones may indicate the risk of targeted-defacement. The presence of hostility in a campaign narrative can be measured through topic model- or topic -clustering methods to identify the emergence of patterns of pillaging on vocabularies related to defacement.

When hostile discourses converge to form more coherent themes, the chances of attack grow. As well, geo/sector spillover is likely to be evident, whereby negative sentiment patterns in other adjacent sectors/regions tend to be common in copycat defacement instances. Baseline monitoring of peers would enable organizetions to detect the spread of sentiment across entities, or cross-entity contagion, where any improvement in sentiment in one entity affects the other, increasing the vulnerability to similar attacks.

Amplifiers that involve measurements of behavior, including velocity and acceleration, are early indication of an attack. Using the rate of change in negative sentiment can bring to attention abrupt spikes, which can be associated with threats. Term Frequency-Inverse Document Frequency (TF-IDF) used to detect novelties or burstiness of terminologies that such an attack is imminent through the detection of rare and emergent defacement terminologies. Likewise, the weighting of negativity by influencers is important because small and significant groups of antagonistic messages by high-profile accounts can serve as precursors of cyber incidents. Tracking a coordinated behavior like synchronized messages of aggression on freshly registered accounts or repeated templates can ensure a mobilizational of cyber attackers.

The study developed a stacked, multi-view ensemble that integrates complementary evidence streams lexical polarity, contextual semantics and temporal dynamics into a single calibrated risk score for cyber-defacement prediction. Three base learners captured distinct facets of the signal: a TF-IDF-based Multinomial Naïve Bayes model extracted sparse but discriminative lexical cues; a bidirectional LSTM modelled long-range dependencies and pragmatic features such as negation and sarcasm; and ARIMA prior estimated daily baseline propensities reflecting exogenous rhythms in online discourse.

Their probabilistic outputs were combined by a logistic meta-learner trained on time-ordered splits to prevent information leakage and to match deployment conditions. This architecture achieved superior performance over its constituents (Accuracy = 0.8568, F1 = 0.8055, ROC-AUC = 0.9116, PR-AUC = 0.8964, Brier = 0.1151), with particularly improved calibration and ranking ability, thereby enabling cost-aware, real-time triage of high-risk items. By unifying fast lexical scoring, contextual sequence encoding and lightweight temporal priors within a principled stacking framework, the model delivered both discriminative power and operational reliability, satisfying the study’s objective of producing a robust predictor of defacement-related risk.