Abstract
In this paper, a REST-based Web Service developed in previous work was integrated with a newly developed browser extension that works in modern browser (Firefox and Google Chrome) using Greasemonkey. It uses previous collected datasets which comprised of 17.000 postings and comments from 10 Indonesian actresses whom followers are more than 10 million on Instagram. The performance of the developed web services has been evaluated and the average response time is 1678.133ms using AWS platform located in Ohio (US East 2). The proposed work is working as expected and in accuracy test, it has reached 63.125% in overall, 72% for non-stemmed data and 70% for stemmed data using 1000 test data with a processing time needed for classification is under 2s. The new extension works in Firefox and Chrome and it can utilize the web services to classify spam comments in Instagram.
Highlights
II.Social media is no longer just a mean for sharing information along relatives and colleagues, but it has transformed into a bigger scope and touching every aspect of human life
There are a lot of spam comments in media social, such as YouTube, Facebook, Twitter, and Instagram
Some experiments were conducted using different algorithms and it was concluded that K-Nearest Neighbors gave the best results with 88.4% of accuracy [17], followed by Support Vector Machine with 78.5% [18], and
Summary
There are a lot of spam comments in media social, such as YouTube, Facebook, Twitter, and Instagram. Ali and Okiriza [12] published their work on detecting spam comments on Indonesia’s Instagram post using three different algorithms: Naïve Bayes, SVM, and XGBoost. They concluded that SVM and XGBoost got the best scores of 0.9601 and. The final data used are 17.000 postings From this datasets, some experiments were conducted using different algorithms and it was concluded that K-Nearest Neighbors (kNN) gave the best results with 88.4% of accuracy [17], followed by Support Vector Machine with 78.5% [18], and.
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