Performance analysis of a keyword search system

تعدين البيانات هي عميلة لاكتشاف انماط في مجموعة من البيانات وفقاً للكلمة الرئيسية. البحث عن الكلمة الرئيسية هي الطريق الاكثر فاعلية لاكتشاف المعلومات في الوثائق. ولكن في مكان ما، في بعض الأحيان فقط البحث عن الكلمة الرئيسية ليست كافية، مع البحث في تقييد تلك الكلمة الرئيسية أصبح ضرورة. كما هو الحال في إساءة استخدام وسائل الاعلام الاجتماعية من كلمة آخذت في الازدياد. عملت العديد من الأنظمة على الكشف عن كلمة غير ملائمة فقط؛ وليس على تقييد تلك الكلمة. حتى هنا في ورقة البحث الكلمة المقترحة في طريقة وسائل الاعلام الاجتماعية التي لا يجد فقط الكلمات غير المناسبة، ولكن أيضا تقييد تلك الكلمة من النشر على وسائل الإعلام.Data mining is the process of discovering patterns in a data set by keyword. Keyword search is the most effective way to discover information in documents. But somewhere, sometimes just searching for a keyword is not enough; with research restricting that keyword has become a necessity. Like in social media abuse of word is increasing. Many systems worked on only detecting an inappropriate word; not on restriction of that word. So here in this paper keyword search method is proposed for social media which not only finds the inappropriate words, but also restrict that word from publishing on the media

Detection of Cyberbullying in SMS Messaging

Cyberbullying is a type of bullying that uses technology such as cell phones to harass or malign another person. To detect acts of cyberbullying, we are developing an algorithm that will detect cyberbullying in SMS (text) messages. Over 80,000 text messages have been collected by software installed on cell phones carried by participants in our study. This paper describes the development of the algorithm to detect cyberbullying messages, using the cell phone data collected previously. The algorithm works by first separating the messages into conversations in an automated way. The algorithm then analyzes the conversations and scores the severity and frequency of the bullying words. A scoring threshold is used to predict whether or not a message or a conversation contains cyber bullying

An Intervening Ethical Governor for a Robot Mediator in Patient-Caregiver Relationships

© Springer International Publishing AG 2015DOI: 10.1007/978-3-319-46667-5_6Patients with Parkinson’s disease (PD) experience challenges when interacting with caregivers due to their declining control over their musculature. To remedy those challenges, a robot mediator can be used to assist in the relationship between PD patients and their caregivers. In this context, a variety of ethical issues can arise. To overcome one issue in particular, providing therapeutic robots with a robot architecture that can ensure patients’ and caregivers’ dignity is of potential value. In this paper, we describe an intervening ethical governor for a robot that enables it to ethically intervene, both to maintain effective patient–caregiver relationships and prevent the loss of dignity

Graph-based Features for Automatic Online Abuse Detection

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach

Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features

In recent years, online social networks have allowed worldwide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content-and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%

Detection of Abusive Language from Tweets in Social Networks

Detection of abusive language in user generated online con-tent has become an issue of increasing importance in recent years. Most current commercial methods make use of black-lists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted ex-samples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior