Regular Expressions for Web Advertising Detection Based on an Automatic Sliding Algorithm

Riaño, Donovan; Piñon, Rodrigo; Molero–Castillo, Guillermo; Bárcenas, Everardo; Velázquez-Mena, Alejandro

doi:10.1134/s0361768820080162

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regular expression can seeks comprehension-affecting code smells. To evaluate the understandability of various regular expression language features used golden standart [23]. In the biomedical domain normalization is considered more difficult than concept recognition, e.g.…”

Section: Methodsmentioning

confidence: 99%

Scientific reference style using rule-based machine learning

Helen,

Pradana,

Afif

2023

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing.

show abstract

Section: Methodsmentioning

confidence: 99%

Scientific reference style using rule-based machine learning

Helen,

Pradana,

Afif

2023

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…In addition to commercially available ad blockers, some academic works have proposed ad detection algorithms [52,53]. For instance, Lashkari et al [54] developed CIC-AB, which is an algorithm that employs machine learning methodologies to identify advertisements and classify them as non-ads, normal ads, and malicious ads, thereby eliminating the need to regularly maintain a filter list (as with earlier rule-based approaches) [51].…”

Section: Web Ad Filtering and Blockingmentioning

confidence: 99%

Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users

Kodandaram,

Sunkara,

Jayarathna

et al. 2023

J. Imaging

View full text Add to dashboard Cite

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users’ experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out internal ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and ‘in-the-wild’ random websites yielded F1 scores of 0.86 and 0.88, respectively.

show abstract