Full Project – Semantic analysis of offensive languages on social media platform using Facebook as a case study
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CHAPTER ONE
INTRODUCTION
- Background of the Study
Online social networking (OSN) websites have enjoyed great success in recent years and have become the new frontier in today’s social relationships providing great places for self-expression and exchange of ideas.
Social networking has provided opportunities for new relationships as well as strengthening existing relationships. Benefits of social networking platforms vary based on platform type, features and the company itself. OSN allows organizations to improve communication and productivity by disseminating information among different groups of employees in a more efficient manner, resulting in increased productivity. (Mahmud and Ahmed, 2008)
In the past, social networks were viewed as a distraction and offered no educational benefit. Blocking these social networks was a form of protection for students against wasting time, bullying, and invasions of privacy. In an educational setting, OSNs are seen by many instructors and educators as a frivolous, time-wasting distraction from schoolwork, and it is not uncommon to be banned in school computer labs. Cyberbullying has also become an issue of concern with social networks. According to the Children Go Online survey of 9-24-year-olds, it was found that a third have received bullying comments online. ( http://internetsafety101.org) To avoid this problem, many school districts/boards have blocked access to online social networks within the school environment. (J. Cheng, 2007)
Social networking services often include a lot of personal information posted publicly, and many believe that sharing personal information is a window into privacy theft. Schools have taken action to protect students from this. It is believed that this outpouring of identifiable information and the easy communication vehicle that social networking services opens the door to sexual predators, cyberbullying, and cyber-stalking (http://en.wikipedia.org/wiki/Social_networking_service). In contrast, however, 70% of social media using teens and 85% of adults believe that people are mostly kind to one another on social network sites.
(http://en.wikipedia.org/wiki/Social_networking_service) Research has suggested that there has been a shift in blocking the use of social networking services. In many cases, the opposite is occurring as the potential of online networking services is being realized. It has been suggested that if schools block them [Online Social Networks], they’re preventing students from learning the skills they need. Banning social networking is not only inappropriate but also borderline irresponsible when it comes to providing the best educational experiences for students. Schools have the option of educating safe media usage as well as incorporating digital media into the classroom experience, thus preparing students for the literacy they will encounter in the future. (M. Ferguson. et al, 2007)
Cyberbullying is a fast-growing trend that experts believe is more harmful than typical schoolyard bullying. Nearly all of us can be contacted 24/7 via the internet or our mobile phones. Victims can be reached anytime and at anyplace. For many children, home is no longer a refuge from the bullies. “Children can escape threats and abuse in the classroom, only to find text messages and emails from the same tormentors when they arrive home.” (Marneffe and Manning, 2008)
“There’s no safe place anymore and one can be bullied 24/7; even in the privacy of his/her own bedroom.” (Cyberbullying, Able Publishing Newsletter – Term 3, 2008).
Online social networking sites have become increasingly popular with children, especially young teens, as a place where they can meet other people, communicate, and exchange information. No type of bullying is harmless. In some cases, it can constitute criminal behavior. In extreme incidents, cyberbullying has led teenagers to suicide. Most victims, however, suffer shame, embarrassment, anger, depression, and withdrawal. (Cyberbullying, Able Publishing Newsletter – Term 3, 2008) Cyberbullying is often seen as anonymous, and the nature of the internet allows it to spread quickly to hundreds and thousands of people.
Cyberbullying has the same insidious effects as any kind of bullying, turning children away from school, friendships, and in tragic instances, life itself. Parents often tell their children to turn off mobile phones or stay off the computer. Many parents don’t understand that the internet and mobile phone act as a social lifeline for teenagers to their peer group. Victims often don’t tell their parents because they think their parents will only make the problem worse, or that they might even confiscate their mobile phone or take away their internet access, removing that social lifeline. While bullying is something that is often ‘under the radar’ of adults, cyberbullying is even more so. Teenagers are increasingly communicating in ways that are often unknown by adults and away from their supervision. They organize their social lives through these mediums. Their friendships are made and broken over these mediums. (Klein and Manning, 2003)
So the question remains “How can we avoid offensive languages in OSNs?” This research work aims at removing offensive languages in a user message. When offensive language is detected in a user message, a problem arises about how the offensive language should be removed, i.e. the offensive language filtering problem. To solve this problem, the manual filtering approach is known to produce the best filtering result. However, manual filtering is costly in time and labor thus cannot be widely applied.(http://en.wikipedia.org/wiki/Anti-spam_techniques) Here, we will analyze the offensive language in text messages posted in online communities, and propose a new automatic sentence-level filtering approach that is able to semantically remove the offensive language by utilizing the grammatical relations among words. Comparing with existing automatic filtering approaches, the proposed filtering approach provides filtering results much closer to manual filtering.
1.2 Problem Statement
The online community has encouraged the use of offensive languages which has spread into about 80% of all OSN and has been very harmful to the mental health of both children and youth. To the online community, the deluge of offensive language undermines the community’s reputation, drives users away, and even directly affects its growth. (Zhi Xu and Sencun Zhu, 2010)
People have realized the problems brought by offensive language in online communities and many efforts have been made on detecting the existence of offensive language within user messages. However, detection alone is not enough to eliminate the hazard caused by offensive language. When offensive contents are detected within a user message, a question arises naturally about how the detected offensive content should be removed from the message before it is been transmitted. (J. Sjoberg. et al, 1997)
Also, how do we remove or filter offensive languages and words form a message thoroughly and still keep inoffensive content untouched as much as possible. Also, will the readability of filtered content be guaranteed so as to make our filtering transparent to readers?
1.3 Aims and objectives:
This project work intends to develop and implement a sentence-level semantic filtering System, which will
- Utilize grammatical relations among words to stop cyberbullying by semantically remove offensive content in a sentence.
- Produce a minimal error when filtering offensive languages and words form a message and still keeps inoffensive content untouched as much as possible.
- Guarantee the readability of filtered content so as to make the filtering transparent to readers.
- Implement the designed model which is going to be a sophisticated NLP application, not an AI application, since learning is not going to be involved.
- To help reduce the chances of victimization in Online Social Networking Sites.
1.4 Research Methodology
The methodology adopted in carrying out this project includes the use of interviews to gather primary data from a number of leading filtering vendors in Nigeria. Both telephone and face-to-face interviews will be carried out with the relevant technology experts within selected organizations. Also, an existing database of offensive words and languages will be collected and use to simulate an offensive database engine. A semantic filtering model will be proposed and implemented using XYZ. Statistical/probabilistic analysis of recurring offensive tokens will be done using Bayesian method. The designed semantic filtering system will be tested as an online web application with a client application by engaging users to validate the efficiency of the designed system.
1.5 Scope and Limitation
In this research work, we made an assumption that all offensive opinions are expressed by offensive words and we have a comprehensive offensive lexicon containing all offensive words. Based on this assumption, we adopt a simple word matching approach to identify offensive words in the sentence to be filtered. This assumption is made because the focus of our work is about offensive language filtering instead of detection. Since our filtering approach depends on detection of offensive language, the filtering might fail if offensive language cannot be detected before the filtering process. To avoid filtering, the offender may try to evade the offensive language detection mechanisms. For detection, there are many literatures discussing detecting offensive language in sentence level or message level. For offensive lexicon generation, presents a study. We believe offensive language detection is a very challenging problem worthy of separate treatment.
1.6 Organization of the study
The thesis work is arranged in five chapters with the breakdown as follows:
The First Chapter is termed introduction and it includes the Online Social Networking System, research aim and objectives, research methodology and organization of dissertation.
Chapter Two deals with the literature review on grammatical relations, cyberbullying and the concept of semantic filtering system.
Chapter Three presents the Methodology and analysis of the input and output specification of the proposed system and the design of the system.
Chapter Four describes the system implementation and evaluation of the system design. This would consist of a brief description of each program module and its functions. It also justifies the choice of package and describes the software required to implement the system. It also shows the measures taking during the implementation.
Chapter Five summarizes the project work. It covers the conclusion and recommendation for the project.
1.7 Expected Contribution to Knowledge
This research work will add to knowledge in the following ways:
- Been able to filter offensive words and clauses in a sentence for online communities.
- Making filtered sentence to be readable and also making the existence of offensive words in the original sentence difficult to notice.
- The framework that offensive words are been developed.
1.7.1 Glossary of Terms
OSN: Online Social Networks
ISP: Internet service providers.
POStagging: Part of Speech Tagging
PTree: Parse Tree
TDset: Typed Dependency Set
TDgenerator: Typed Dependency Generator
NPL: Natural Language ProcessingTyped Dependency Generator
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Full Project – Semantic analysis of offensive languages on social media platform using Facebook as a case study