BullStop: A Mobile App for Cyberbullying Prevention

Abstract: Social media has become the new playground for bullies. Young people are now regularly exposed to a wide range of abuse online. In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected. BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store.

14/09/2020

behaviors, cases, classification, classifiers, communities, detection, factors, large_scale, learning, linguistic, linguistic aspects, networks, performance, representations

9:53

07/06/2020

Driving the Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks

Hemank Lamba, Shashank Srikanth, Dheeraj Reddy Pailla and
Shwetanshu Singh, Karandeep Singh Juneja, Ponnurangam Kumaraguru

Keywords Paper

behaviors, changes, classifiers, contexts, deep learning, factors, learning, networks, sites

10:37

06/12/2021

Kostantinos Papadamou, Antonis Papasavva, Savvas Zannettou and
Jeremy Blackburn, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, Michael Sirivianos

Naeemul Hassan, Amrit Poudel, Jason Hale and
Claire Hubacek, Khandaker Tasnim Huq, Shubhra Kanti Karmaker Santu, Syed Ishtiaque Ahmed

Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in the COVID-19 Era and Beyond

attention, bias, deep learning, detection, groups, identities, learning, sources, toxic, toxicity

9:59

07/06/2020

cases, common, deep learning, images, learning, rates, search, shared, sources, texts, traditional

9:43

07/06/2020

4chan, cases, changes, communities, elections, embeddings, events, groups, images, influences, large_scale, large_scale quantitative, measures, memes, political, politically incorrect, presidential election, rates, reddit, rest, spread, tools, traditional, trends, twitter, twitter reddit

9:06

07/06/2021

Yi Xu, Fan Bai, Yingxuan Shi and
Qiuyu Chen, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun

Keywords Paper

16:48

12/08/2020

The Tools and Tactics Used in Intimate Partner Surveillance: An Analysis of Online Infidelity Forums

Emily Tseng, Rosanna Bellini, Nora McDonald and
Matan Danos, Rachel Greenstadt, Damon McCoy, Nicola Dell, Thomas Ristenpart

Multidisciplinary Topics and Applications, Security and Privacy, Classification, Mining Graphs, Semi Structured Data, Complex Data

13:28

07/06/2021

Qualitative and quantitative studies of social media, Human computer interaction, social media tools, navigation and visualization, Subjectivity in textual data, sentiment analysis, polarity/opinion identification and extraction, linguistic analyses of soc

7:05

16/11/2020

Team Oulu at SemEval-2020 Task 12: Multilingual Identification of Offensive Language, Type and Target of Twitter Post Using Translated Datasets

Ulrik Lyngs, Kai Lukoff, Petr Slovak and
William Seymour, Helena Webb, Marina Jirotka, Jun Zhao, Max Van Kleek, Nigel Shadbolt

claims, classifiers, large_scale, measures, misinformation, tweets

9:56

14/06/2020

multimodal learning, multimodal categorization, cross-attention, stochastic shared embedding, event detection, social media, image-text fusion, ai for social goods, language and vision, emergency response

1:01

17/08/2020

accounts, detection, early detection, influences, links, political, political candidates, search, spread, tweets, twitter

3:01

30/11/2020

Zhixiu Guo, Zijin Lin, Pan Li, Kai Chen

Keywords Paper

11:51