Analysis of Cyberbullying Incidence among Filipina Victims: A Pattern Recognition using Association Rule Extraction

Abstract

Cyberbullying is an intentional action of harassment along the complex domain of social media utilizing information technology online. This research experimented unsupervised associative approach on text mining technique to automatically find cyberbullying words, patterns and extract association rules from a collection of tweets based on the domain / frequent words. Furthermore, this research identifies the relationship between cyberbullying keywords with other cyberbullying words, thus generating knowledge discovery of different cyberbullying word patterns from unstructured tweets. The study revealed that the type of dominant frequent cyberbullying words are intelligence, personality, and insulting words that describe the behavior, appearance of the female victims and sex related words that humiliate female victims. The results of the study suggest that we can utilize unsupervised associative approached in text mining to extract important information from unstructured text. Further, applying association rules can be helpful in recognizing the relationship and meaning between keywords with other words, therefore generating knowledge discovery of different datasets from unstructured text.

Analyzing the relationship between information technology jobs advertised on-line and skills requirements using association rules

“…If the value of lift rule is equal to one, then it is independent. A lift value of one indicates that the pair of skill words appear almost as often together as expected [35].…”

“…The number of occurances of skill terms in an online web pages weighted with a greater significant is the way used to discover the dominant skills words and skill patterns. The TF-IDF score increase in accordance to the frequency of times a word (skills) appears on an online job posting websites, but is countered by the word's frequency in the dataset, which helps to account for the fact that some words are more prevalent than others [35].…”

“…The rule Wi⇒W jholds in the collection of skills Tc with confidence c if among those skill words that contain Wi, c % of them contain W j also. The confidence is calculated as shown in below: Support, which is the ratio of the number of instances when [w1 wj] appeared together in a single transaction to the total number of transactions, is used to quantify frequent item sets, whereas confidence is defined as the probability of finding [w1, wj] together [35].…”

“…FP-growth is one of the most utilized association rule mining algorithms. The FP-growth algorithm utilized an analytical process that finds/locate frequent patterns/associations of job words from the dataset without generating the candidates [35], [36].…”

State and Legal Policy for the Protection of Children in the Digital Environment

“…The digital environment may open new ways to perpetrate violence against children. Open access to the Internet, difficulties in tracking the abuser, fear of parents' reaction to cyberthreats [2][3] and, as a result, the concealment of cyberbullying [4][5] make the child easy prey. Crises, such as pandemics, may lead to an increased risk of online harm, considering that children spend more time on virtual platforms [6].…”