The study began with what the supplied workbook could legitimately support. Its primary measure was follower-normalised like intensity, or FLI, calculated as likes divided by recorded followers and multiplied by 100. This metric was available for 9,968 TikTok records and 10,127 Instagram records. FLI is not a conversion rate, a reach estimate or direct evidence of algorithmic recommendation: it compares recorded likes with recorded account size, and follower counts may have been captured after publication. The workbook also lacked shares, saves, watch time, completion rate and recommendation exposure, while comment counts could not be independently identified, preventing the construction of broader interaction ratios.

A central audit finding concerned the column labelled “views.” Embedded headers in the TikTok sheet described the same column as “comments,” leaving its identity unresolved. Among records with numeric likes and a positive value in that field, likes exceeded the recorded value in 92.7% of TikTok cases and 83.3% of Instagram cases. Those comparisons diagnose the supplied data rather than proving that native platform view counts were wrong, but they rule out treating the field as a reliable exposure denominator. The researchers consequently limited it to diagnostic summaries and did not calculate like-to-view engagement ratios, even where a like count did not exceed the recorded field value.

The analysis used a seven-step protocol covering variable availability, platform validity, timestamp validity, denominator validity, ratio construction, hashtag classification and distributional shape. Rows without account identifiers, embedded headers and Facebook-labelled entries were removed, while account identifiers were standardised by lower-casing and trimming whitespace. The resulting sample represented 3,996 distinct recorded TikTok accounts and 4,691 Instagram accounts, although the workbook lacked verified post identifiers and documentation of the extraction tool, dates, search terms or original inclusion query. The data were therefore treated as dataset-defined records associated with tourism content rather than a probability sample or a controlled collection of verified posts.

Distributional diagnostics produced the study’s clearest platform contrast. The median FLI was 12.63 on TikTok and 1.77 on Instagram, and a Mann–Whitney U test indicated that TikTok records generally occupied higher ranks in the distribution, with a rank-biserial effect of 0.317. The mean told a different story: Instagram’s mean FLI was 922.53, compared with 201.57 for TikTok, because Instagram contained extreme outliers. A supplementary Welch t-test consequently pointed toward higher Instagram means. The result does not support a simple platform ranking: TikTok records displayed stronger typical intensity relative to followers, whereas a smaller number of Instagram posts achieved exceptional values. A higher mean therefore did not mean that the typical Instagram post performed better.

Two sensitivity checks preserved the direction of the median comparison. When FLI was aggregated to the median for each recorded account, TikTok’s account-level median remained higher than Instagram’s, at 18.11 versus 0.98, with a rank-biserial effect of 0.484. A separate analysis removed exact repeated records—900 additional TikTok rows and 311 additional Instagram rows matching earlier entries across eight fields—and again found higher median FLI on TikTok, at 12.50 versus 1.72. These checks strengthen the descriptive contrast, although they do not correct all dependence among posts from the same account, and the remaining record-level significance tests do not account for clustering. Without post identifiers, identical entries also cannot conclusively be distinguished from separate posts carrying identical recorded information.

Hashtag comparisons did not detect a reliable targeted-versus-generic advantage on either platform. Researchers extracted only tokens preceded by a hash symbol, converted them to lower case and classified records as targeted-only, generic-only, mixed or other/uncoded. Targeted-only records had higher descriptive median FLI on both TikTok, at 14.75 versus 12.57 for generic-only records, and Instagram, at 4.05 versus 2.74. Neither difference was statistically significant, with p values of 0.320 and 0.233, respectively. These findings do not establish equivalence or prove that hashtags are irrelevant; exact dictionary matching and uneven extraction coverage, especially for TikTok entries without a hash prefix, limited the scope of the classification.

Account size also related differently to engagement across the platforms. Recorded followers were strongly associated with raw TikTok likes, with a Spearman correlation of 0.660, but only weakly associated with Instagram likes, at 0.087. Descriptive ordinary least-squares models of log-transformed likes and followers yielded coefficients of 0.690 for TikTok and 0.110 for Instagram, with R-squared values of 0.433 and 0.007. These models account for associations rather than causal effects. They show that TikTok could display high typical FLI while raw likes remained strongly linked to audience size, undermining any claim that TikTok circulation operates entirely outside follower networks. The negative correlations between followers and FLI, at −0.330 for TikTok and −0.502 for Instagram, also require caution because followers form the metric’s denominator.

Temporal and textual analysis was restricted because the available records did not support it reliably. Recorded TikTok dates ranged from 2018 to 2025, plus one value parsed as the impossible year 2206, while Instagram included a date before the platform launched in 2010. Because the source of these anomalies was undocumented, the researchers did not conduct period-matched or longitudinal comparisons. Inspection also found apparent caption misalignment in parts of the TikTok sheet, so caption-based sentiment analysis was excluded rather than assigning text to records by assumption. The final results therefore concern platform-conditioned engagement traces, not feed position, recommendation exposure, tourist persuasion, destination effectiveness or causal effects of platform architecture and hashtag use.

The study’s broader message is methodological rather than a verdict on tourism campaigns. Auditing can identify missing variables, inconsistent fields, invalid denominators, ambiguous categories and skewed distributions, allowing researchers to determine where interpretation should stop. It cannot reveal how content was ranked, whether users encountered posts through recommendations, followers, search or external sharing, or which collection process produced a discrepancy. The UAE and Dubai case is also bounded: it lacks variables describing content format, creator type, account verification and institutional versus personal use, all of which could influence both audience size and engagement. The defensible lesson is that platform metrics must be reconstructed and tested before they are treated as comparable evidence of visibility or success.

Subject of Research: Auditing follower-normalised engagement metrics in TikTok and Instagram records associated with UAE and Dubai tourism content.

Article Title: Auditing platform metrics for communication research: a cross-platform case study of TikTok and Instagram engagement

Article References: Salim Humaid Kattarah Al Shamsi, M., & Ragnedda, M. (2026). Auditing platform metrics for communication research: a cross-platform case study of TikTok and Instagram engagement. Frontiers in Communication, 11, Article 1921355. https://doi.org/10.3389/fcomm.2026.1921355

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1921355

Keywords: social media metrics, TikTok, Instagram, tourism communication, data auditing, engagement analysis, platform studies, UAE tourism, Dubai tourism, methodological research, Auditing, platform

The analysis examined publication and citation trends, prominent authors, journals, institutions and countries, as well as international collaboration patterns and keyword co-occurrence networks. It also identified major thematic clusters and emerging research frontiers. The four dominant areas were blockchain adoption in accounting and auditing, its reported impact on accounting and auditing practices, blockchain-enabled sustainability and governance, and prospective integration with artificial intelligence, Industry 4.0 and data-driven auditing environments.

Beyond these clusters, the review identified digital governance, continuous auditing, cybersecurity, intelligent supply chains and technology-enabled accountability systems as emerging areas of attention. The authors describe their findings as a computational overview of blockchain-enabled accounting ecosystems, with implications for understanding interdisciplinary links and knowledge gaps. The analysis characterizes blockchain as infrastructure associated with transparency, traceability, automation and data integrity, while also reporting its use in financial reporting, assurance, governance and sustainability-related disclosure research.

The study is a literature-mapping review rather than an evaluation of blockchain implementations or an assessment of their effectiveness. Its corpus is limited to Scopus-indexed publications published between 2017 and 2024, so the findings describe the structure and trajectories of that database’s research literature rather than demonstrating that blockchain improves reporting accuracy, audit quality or regulatory outcomes. The available abstract does not report detailed quality-appraisal procedures, uncertainty intervals or sensitivity analyses, and those elements cannot be assessed from the summary alone.

Subject of Research: Bibliometric analysis of blockchain research in accounting and auditing

Article Title: Blockchain research in accounting and auditing through bibliometric and science mapping analysis

Article References: Tahat, Y., Benhayoun, I., Zejjari, I., AlMi’ani, M., & Hassanein, A. (2026). Blockchain research in accounting and auditing through bibliometric and science mapping analysis. Discover Sustainability. https://doi.org/10.1007/s43621-026-04852-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04852-9

Keywords: blockchain, accounting, auditing, bibliometrics, science mapping, scientometrics, Scopus, sustainability reporting, continuous auditing, artificial intelligence, Industry 4.0, research

The core idea behind SSAT is deceptively simple: occlusion-based analysis. Take an input image, cover one region of it, run the model again, and measure how much the prediction changes. If blanking out a patch causes the model’s confidence to collapse, the model was probably relying on whatever was in that patch. This perturbation primitive has existed since the early days of convolutional network visualization, and libraries such as Captum already provide the underlying feature-removal operations. What has been missing, the authors argue, is a standardized workflow that scales this experiment to entire datasets while guarding against the many ways such an analysis can mislead.

A large change in the model’s output, it turns out, does not by itself prove meaningful dependence. Occluding an equally large area at an arbitrary location may produce a comparable shift in confidence, and the direction of the change can even reverse depending on how the occluded region is filled in. SSAT therefore pairs every region-level intervention with matched random controls of the same area, repeats the perturbation under multiple fill strategies and random seeds, and grades the resulting evidence rather than reporting raw magnitudes alone. Each comparison between a sample and a target region, called an anchor, receives a reliability grade of HIGH, MODERATE, LOW, or UNRELIABLE based on four checks: whether the degradation has the same sign under every operator, whether it exceeds the control mean by more than two control standard deviations, whether at least two operators reproduce the dominant sign, and whether the 95 percent bootstrap interval of the region mean excludes zero.

The toolkit is organized as a five-stage pipeline driven by a single YAML configuration. An estimate stage performs a bounded preflight of configuration validation, workload and storage estimation, and sanity checks. A run stage combines a data source, a model adapter, a region generator, and a perturbation operator to perform clean and perturbed inference, writing full model outputs to a resumable Parquet dump with a manifest that records configuration hashes, checkpoint hashes, adapter identity, and code version. A metrics stage normalizes task responses, an analyze stage performs matched-control and stability analysis, and a report stage produces HTML, CSV, and JSON outputs including dataset-wide heatmaps and per-sample drill-downs. Because the raw observations are preserved before any metrics are derived, researchers can regrade thresholds, recompute intervals, or apply new aggregations to stored interventions without running any new inference.

To validate the workflow, the authors first turned to a synthetic dataset in which a shortcut patch was inserted at a known location, together with a negative-control model that does not exploit the shortcut. Under pre-registered questions, SSAT recovered the patch as the most sensitive region of the shortcut model and ranked it last in the negative-control model. The patch effect was 175.65 times the same-area sibling-region baseline, far above the 3.0 times threshold, and the conclusion reproduced across all five fill strategies. Independently of the region audit, removing the patch caused a 95.75 percentage-point accuracy-drop margin between the two test conditions, confirming that the shortcut dependence found by the spatial analysis corresponds to an actual generalization gap.

The second case study audited 10,000 samples from the ImageNet-1k validation set across four pretrained classifiers: MobileNetV2 at two width multipliers, ConvNeXt-T, and the Vision Transformer DeiT-S. Each model was audited under its official preprocessing and a crop-free variant, giving eight runs that shared the same 4 by 4 grid, the same three perturbation operators, three matched random controls per anchor, and the same aggregation protocol. The results showed a strikingly consistent spatial profile across architectures: in all eight runs, the four central cells of the grid held the top four ranks in mean margin degradation, followed by the eight edge cells and the four corner cells, with 16-cell profiles correlating at Spearman 0.89 to 0.95 between ConvNeXt-T, DeiT-S, and the MobileNetV2 models.

Yet the ImageNet study also exposed a genuine confound that had been distorting region-level comparisons. Under the official center-crop pipeline, corner cells projected onto the model input plane have a smaller effective area than center cells, a ratio of 1.78 for a square image and a median of 4.4 between the largest and smallest cell of an audited image. An area effect can therefore be conflated with a genuine difference in sensitivity across locations. The area-matched controls themselves failed this check for 99.3 percent of anchors under the official pipeline. Switching to crop-free preprocessing restored uniform cell area within 4 percent and matched every control within 5 percent, while preserving the dataset-level profile at Spearman 0.96 to 0.98. The finding motivated a preprocessing-aware geometry diagnostic that now warns when the effective area fraction of a region deviates from its intended fraction beyond a tolerance.

The third case study extended the same protocol to video, auditing TSM-ResNet50 on the NTU RGB+D 60 action recognition dataset using skeleton-guided body-part regions that vary from frame to frame. Aggregated over all body parts, the upper body showed the largest mean degradation, followed by the arm and torso, whereas the head showed the smallest. For the class-conditional view, a third-party annotator blind to SSAT’s results rated the relevance of five body-part groups to each of the 60 classes before any scores were computed. SSAT’s standardized class-level group scores ranked relevant groups above non-relevant ones with a mean class AUROC of 0.69, and a perfect AUROC of 1.0 in 25 of the 60 classes, including sitting down, kicking, and brushing teeth.

The authors also examined how sensitive the grading design itself is to its thresholds and control counts. Regrading stored runs showed that the z threshold moves anchors only between HIGH and LOW, leaving at least 93 percent of ImageNet grades unchanged across thresholds from 1 to 4. The HIGH share decreased smoothly as the control spread was estimated from more controls, from 14.5 percent at three controls to 6.8 percent at twenty, and 97.6 percent of grades agreed between ten and twenty controls. In terms of cost, the toolkit achieved about 3.6 milliseconds per model evaluation on an RTX 4090 host with 3.9 kilobytes of storage per evaluation, so each 3.2-million-evaluation run takes roughly four hours. A coarse-to-fine strategy, scanning a 2 by 2 grid and refining only the most sensitive cell, reaches 8 by 8 resolution with 31 percent of the evaluations of an exhaustive grid.

Compared with hand-written Captum-based reference implementations of the same audits, SSAT required roughly half the user-authored code for the synthetic setting, with one explicit design decision instead of eight, and moved to ImageNet with only two changed configuration lines rather than 419 changed lines of code. The authors are careful to note the limits of what the tool measures: sensitivity is a model’s response to a change in an input region, not a causal explanation or a statement about human semantic importance, and results depend on the region definition, perturbation operator, fill strategy, and preprocessing. Anchor-level grades agreed poorly across preprocessing pipelines and architectures, so they are model-specific, whereas dataset-level profiles remained similar. Future work includes a GPU pipeline, matched controls for frame-varying video regions, and task adapters beyond classification. For now, SSAT offers researchers something the field has lacked: a shared, verifiable protocol for asking where their models look, and an honest accounting of when the answer is trustworthy.

Subject of Research: SSAT: A reproducible toolkit for task-conditioned spatial sensitivity auditing of vision models

Article Title: SSAT: A reproducible toolkit for task-conditioned spatial sensitivity auditing of vision models

Article References: Lim, D., Jin, T., & Kang, C. (2026). SSAT: A reproducible toolkit for task-conditioned spatial sensitivity auditing of vision models. SoftwareX, 36, Article 103130. https://doi.org/10.1016/j.softx.2026.103130

Image Credits: AI Generated

DOI: 10.1016/j.softx.2026.103130

Keywords: SSAT, reproducible, toolkit, task-conditioned, spatial, sensitivity, auditing, vision, models, scientific research

The research team set out to examine the role of accountants in identifying, mitigating, and detecting fraud in the field of digital technology, while also identifying patterns, innovations, gaps, and potential future developments in the literature. To do this rigorously, they employed a Systematic Literature Review method structured by the CIMO-Logic framework, an analytical device that organizes evidence around Context, Interventions, Mechanisms, and Outcomes. This framework allowed the authors to move beyond simply cataloguing studies and instead trace how specific technological interventions produce measurable changes in fraud-detection capability under particular organizational conditions.

The scope of the review covered a six-year period from 2020 to 2025, a window chosen to capture the most recent wave of digital transformation in accounting research. From searches of ScienceDirect and Google Scholar, the team selected 32 articles that met their criteria. That relatively small but focused corpus reflects both the maturity and the immaturity of the field: there is now a substantial body of peer-reviewed work on digital fraud detection, yet the literature remains fragmented across disciplines, methodologies, and technology domains, which is precisely the kind of landscape a systematic review is designed to clarify.

The headline finding is unambiguous. Technologies such as artificial intelligence, big data analytics, computer-assisted audit tools and techniques, known in the profession as CAATTs, blockchain, and other specialized digital tools play a significant role in enhancing accountants’ capabilities to detect and prevent fraud. Each of these technologies attacks a different weakness of the traditional audit. AI systems, particularly machine learning models, can classify transactions as anomalous or legitimate at scales no human team could match, learning the statistical fingerprints of fraud from historical cases. Big data analytics extends the auditor’s field of vision from small samples to entire transaction populations, eliminating the sampling risk that once allowed fraudulent entries to slip through unnoticed.

Computer-assisted audit tools and techniques occupy an interesting position in this story because they represent the bridge generation between manual auditing and fully intelligent systems. CAATTs allow auditors to interrogate complete datasets, run repeatable analytical tests, and flag exceptions automatically, which means that even organizations not yet ready for deep learning pipelines can achieve continuous, full-population testing. The reviewed literature shows that these tools are not merely conveniences; they change the mechanism of detection itself, shifting the auditor’s role from retrospective verification of selected records toward proactive, ongoing surveillance of financial flows.

Blockchain technology contributes a different kind of assurance. Because distributed ledger systems create immutable, timestamped, and cryptographically linked records, they reduce the opportunity for transaction tampering at the source rather than detecting it after the fact. In the framework of the review, blockchain functions as a preventive mechanism embedded in the accounting infrastructure, complementing the detective power of AI and analytics. Together, these technologies form a layered defense: blockchain hardens the data, big data analytics widens the view, and machine learning sharpens the identification of anomalies within that view.

Beyond the technology itself, the authors found that digitalization is a key factor transforming the role of accountants in fraud mitigation. The reviewed studies consistently indicate that as organizations digitize, two organizational consequences follow. First, there is growing pressure to improve the digital competencies of accounting professionals, since tools are only as effective as the people who deploy and interpret them. Second, digitalization encourages the strengthening of internal controls within organizations, because automated systems expose control weaknesses that manual processes could conceal. The auditor of the literature review’s future is therefore not a replaced professional but a redefined one, combining accounting judgment with data science literacy.

The theoretical implications of the review are considerable. By synthesizing the literature on the application of digital technologies in accounting for fraud detection and prevention, the study provides empirical evidence of how technology can transform accounting mechanisms, and it highlights the importance of integrating digital technology with organizational behavior. This integration point matters because fraud is ultimately a human phenomenon, enabled by opportunity, rationalization, and pressure, and technological controls interact with those human factors rather than operating in isolation. A model that flags anomalies is only useful if organizational incentives support investigation of the flags, and a blockchain ledger only helps if governance ensures the integrity of what enters it.

The practical implications are equally direct. The study emphasizes that accountants and auditors need to adopt digital technologies in order to improve the effectiveness of auditing and financial oversight, including through investments in security systems and big data infrastructure to prevent both internal and external fraud. For firms, this reframes technology spending as fraud-risk management rather than optional modernization. For regulators and standard setters, the findings suggest that audit standards built around sampling and periodic testing may need continued evolution toward continuous, technology-mediated assurance. For educators, the message is that curricula must produce graduates fluent in both double-entry bookkeeping and data analytics.

The review also leaves open questions that define the next research frontier. With only 32 articles across six years meeting the inclusion criteria, the evidence base remains thin in areas such as the real-world performance of AI fraud-detection models across industries, the costs and failure modes of blockchain-based assurance, and the behavioral responses of fraudsters to increasingly automated detection. The authors note that identifying these gaps and potential future developments was a core aim of the study, and their mapping of trends and methodologies gives researchers a clearer starting point. What is already clear, however, is that the transformation is not a forecast but an ongoing shift: the audit file of the 2020s is increasingly a live data pipeline, and the profession’s oldest skill, skepticism, is being augmented by algorithms that never sleep.

Subject of Research: Digital technologies transforming auditing and fraud detection

Article Title: The transformation of traditional auditing toward digital fraud detection a systematic literature review

Article References: Andayani, W., Herawan, T., Sari, S. N., & Prestianawati, S. A. (2026). The transformation of traditional auditing toward digital fraud detection a systematic literature review. Discover Sustainability. https://doi.org/10.1007/s43621-026-04666-9

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04666-9

Keywords: auditing, fraud detection, artificial intelligence, big data analytics, blockchain, CAATTs, digitalization, forensic accounting, systematic literature review, internal controls, accounting, financial technology