Social media, is often the go-to place where people discuss their opinions and share their feelings. As some platforms provide more anonymity than others, users have taken advantage of that privilege, by sitting behind the screen, the use of profanity has been able to create a toxic environment. Although not all profanities are used to offend people, it is undeniable that the anonymity has allowed social media users to express themselves more freely, increasing the likelihood of swearing. In this study, the use of profanity by different gender classes is compiled, and the findings showed that different genders often employ swear words from different hate categories, e.g. males tend to use more terms from the "disability" hate group. Classification models have been developed to predict the gender of tweet authors, and results showed that profanity could be used to uncover the gender of anonymous users. This shows the possibility that profiling of cyberbullies can be done from the aspect of gender based on profanity usage.

Abstract-This paper describes a concept to measure social capital. The concept needs dimensions and illustrative factors to explain the existence of social capital in social network sites. This paper describes the dimensions and factors to measure social capital. The objective of this paper is to demonstrate a way to describe social capital. The description of social capital supports the measurement and gives the scientific world the opportunity to identify and to attest social capital in social network sites. The value of the paper is the concept to measure social capital with a questionnaire and gives the opportunity to identify social capital in networks with many participants.Index Terms-Social capital, measurement, social network sites.

Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007-January 2017)'s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the "word" seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line.

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This study attempts to explore the different types of Hate Speech appearing in social media by identifying profane words used in hate speech. This study also compares the profane words used in different generations to assist in identifying the user's profile. Fivehundred (500) comments posted on YouTube on the abusive topics were collected. Profane words are classified into eight different types of hate speech. The finding shows 35% of profane words found in our sample are words related to sexual orientation. Comparison of the terms between 1970 and 2017 also show a high percentage of profane words are sexual orientation. Though the results are found based on only 500 comments collected from YouTube link in the current study, they are useful in establishing the list of profane words which will serve as the base for automatic hate speech identification in our future study. The originality of this research is the development of a training list of profane words for each category and comparison of the type of the words used in 1970 century with today's social media platform.