Detecting cyberbullying

Abstract

In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content.The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100.In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.

Integrated-system to minimizing cyber harassment in kingdom of Saudi Arabia (KSA)

“…According to the results of Wilcoxon signed rank test, it is found that there is a significant difference between males and females in terms of the frequency in using the foul words in their posts fig 2 illustrates Top ten frequently used foul words by female (circle) versus male (square). On the other hand the researchers in [12] pore in studying cyberbullying and cyber harassment. They used analyzing language to detect cyberbullying appeared in the texts at social networks or SMS.…”

Detecting Offensive Statements towards Foreigners in Social Media

“…The majority of the publications apply either lexicon [28,31] or machine learning approaches [14,29,30]. Lexicon approaches entirely rely on a lexicon containing offensive words typically used in hate speech.…”

“…However, their practical applicability is limited, especially in the context of online harassment detection as they achieve only reasonable to moderate classification performance [28]. As a consequence, they are often used to preselect potential offending messages to perform subsequent analyses [31].…”

“…These features are derived by experts from characteristics of hate speech messages and include, for example, the presence of offending words defined in a lexicon [14,30] and the presence of words typically referring to persons [30]. Compared to lexicon approaches, the classification performance is only slightly better [28,29,31]. Additionally, the collection of an adequate amount of training data is cumbersome due to the lack of annotated datasets [28,31].…”

“…Compared to lexicon approaches, the classification performance is only slightly better [28,29,31]. Additionally, the collection of an adequate amount of training data is cumbersome due to the lack of annotated datasets [28,31].…”