Sentiment Informed Cyberbullying Detection in Social Media

@inproceedings{Dani2017SentimentIC,
  title={Sentiment Informed Cyberbullying Detection in Social Media},
  author={Harsh Dani and Jundong Li and Huan Liu},
  booktitle={ECML/PKDD},
  year={2017}
}
Cyberbullying is a phenomenon which negatively affects the individuals, the victims suffer from various mental issues, ranging from depression, loneliness, anxiety to low self-esteem. In parallel with the pervasive use of social media, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include the use of standards and guidelines, human moderators, and blacklists based on the profane words. However, these mechanisms fall short in social media… Expand
Session-Based Cyberbullying Detection: Problems and Challenges
TLDR
The importance of studying session-based cyberbullying detection within a social media session is highlighted, core challenges are identified, and the resource serves as a resource to help direct future research efforts. Expand
XBully: Cyberbullying Detection within a Multi-Modal Context
TLDR
XBully is proposed, a novel cyberbullying detection framework that first reformulates multi-modal social media data as a heterogeneous network and then aims to learn node embedding representations upon it, which shows that the XBully framework is superior to the state-of-the-art cyberbullies detection models. Expand
Social Media Cyberbullying Detection using Machine Learning
TLDR
A supervised machine learning approach for detecting and preventing cyberbullying and shows that Neural Network performs better and achieves accuracy of 92.8% and NN outperforms other classifiers of similar work on the same dataset. Expand
Multi-modal cyberbullying detection on social networks
TLDR
This work proposes a multi-modal cyberbullying detection framework that takes into multi- modal information on social networks, and uses the hierarchical attention networks to capture the session feature in social networks and encode several media information. Expand
Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network
TLDR
Experiments on a real-world dataset from Instagram, the social media platform on which the highest percentage of users have reported experiencing cyberbullying, reveal that the proposed architecture outperforms the state-of-the-art method. Expand
Improving cyberbullying detection using Twitter users' psychological features and machine learning
TLDR
Suggestions and recommendations are described as to how the findings can be applied to mitigate cyberbullying. Expand
Cyberbullying detection in social media text based on character‐level convolutional neural network with shortcuts
TLDR
A Char‐CNNS (Character‐level Convolutional Neural Network with Shortcuts) model is proposed to identify whether the text in social media contains cyberbullying, using characters as the smallest unit of learning to overcome spelling errors and intentional obfuscation in real‐world corpora. Expand
Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach
TLDR
A contextaware and model-agnostic debiasing strategy that leverages a reinforcement learning technique, without requiring any extra resources or annotations apart from a pre-defined set of sensitive triggers commonly used for identifying cyberbullying instances is proposed. Expand
PI-Bully: Personalized Cyberbullying Detection with Peer Influence
TLDR
This paper proposes a personalized cyberbullying detection framework, PI-Bully, that draws on empirical findings from psychology highlighting unique characteristics of victims and bullies and peer influence from like-minded users as predictors of cyberbullies behaviors. Expand
Modeling Temporal Patterns of Cyberbullying Detection with Hierarchical Attention Networks
TLDR
This article investigates how temporal information within a social media session, which has an inherently hierarchical structure, can be leveraged to facilitate cyberbullying detection. Expand
...
1
2
3
4
...

References

SHOWING 1-10 OF 59 REFERENCES
Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying
TLDR
An “air traffic control”-like dashboard is proposed, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. Expand
Learning from Bullying Traces in Social Media
TLDR
Evidence is presented that social media, with appropriate natural language processing techniques, can be a valuable and abundant data source for the study of bullying in both worlds. Expand
Cyberbullying detection: a step toward a safer internet yard
TLDR
This work proposes that incorporation of the users' information, their characteristics, and post-harassing behaviour, for instance, posting a new status in another social network as a reaction to their bullying experience, will improve the accuracy of cyberbullying detection. Expand
Modeling the Detection of Textual Cyberbullying
TLDR
This work decomposes the overall detection problem into detection of sensitive topics, lending itself into text classification sub-problems and shows that the detection of textual cyberbullying can be tackled by building individual topic-sensitive classifiers. Expand
Fast learning for sentiment analysis on bullying
Bullying is a serious national health issue among adolescents. Social media offers a new opportunity to study bullying in both physical and cyber worlds. Sentiment analysis has the potential toExpand
Identification and characterization of cyberbullying dynamics in an online social network
TLDR
The role of user demographics and social network features in predicting how users will respond to a cyberbullying comment is investigated and the influencer/influenced relationship is characterized, the first effort modeling peer pressure and social dynamics with analytical models. Expand
Content-Driven Detection of Cyberbullying on the Instagram Social Network
TLDR
This work investigates use of posted images and captions for improved detection of bullying in response to shared content, and identifies the importance of these advanced features in assisting detection of cyberbullying in posted comments. Expand
Exploiting social relations for sentiment analysis in microblogging
TLDR
This work proposes a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification and presents a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process. Expand
Unsupervised Sentiment Analysis with Signed Social Networks
TLDR
This paper incorporates explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model SignedSenti for unsupervised sentiment analysis, and empirical experiments corroborate its effectiveness. Expand
Unsupervised sentiment analysis with emotional signals
TLDR
This work investigates whether the signals in social media can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation and incorporates the signals into an unsupervised learning framework for sentiment analysis. Expand
...
1
2
3
4
5
...