Improving a human-computer dialogue
Communications of the ACM
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Introduction to Expert Systems
Introduction to Expert Systems
High-performing feature selection for text classification
Proceedings of the eleventh international conference on Information and knowledge management
Slow Technology – Designing for Reflection
Personal and Ubiquitous Computing
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Open Mind Common Sense: Knowledge Acquisition from the General Public
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Classifying racist texts using a support vector machine
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Proceedings of the 4th decennial conference on Critical computing: between sense and sensibility
FearNot! demo: a virtual environment with synthetic characters to help bullying
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Digital Intuition: Applying Common Sense Using Dimensionality Reduction
IEEE Intelligent Systems
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Backchannel persistence and collaborative meaning-making
Proceedings of the 27th ACM international conference on Design of communication
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Detecting Wikipedia vandalism with active learning and statistical language models
Proceedings of the 4th workshop on Information credibility
Finding deceptive opinion spam by any stretch of the imagination
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Extracting social power relationships from natural language
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Cyberbullying Prevention and Response: Expert Perspectives
Cyberbullying Prevention and Response: Expert Perspectives
Adaptive game for reducing aggressive behavior
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Crowdsourced ethics with personalized story matching
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Script-based story matching for cyberbullying prevention
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Real-time emotion classification of Tweets
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Hi-index | 0.00 |
Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.