E-Community Health and Toxicity - BC-452

Discipline(s) souhaitée: Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques
Entreprise: Two Hat
Durée du projet: 6 months to 1 year
Preferred start date: As soon as possible.
Langue exigée: English
Emplacement(s): Kelowna, BC, Canada; Canada
Nombre de postes: 1+

Au sujet de l’entreprise: 

Two Hat Research has developed the next generation of moderation tools for virtual worlds and social networking apps. Our Community Sift product lets clients define which kinds of chat messages are acceptable and which are high risk. We are seeking MSc and PhD students or Post-Docs to work on this research projects. We are particularly interested in pursuing multi-year collaborations. We are committed to eliminating bullying from the web. We work with some of the largest video game, social media, and messaging companies on the internet and are scheduled to process 4 billion chat messages per day. We are deeply interested in publishing papers to showcase strong Canadian research. The work that these researchers will be doing will have a real-world impact.

Veuillez décrire le projet.: 

Online communities abound today, arising on social networking sites, on the websites of real-world communities like schools or clubs, on web discussion forums, on the discussion boards of video games, and even on the comment pages of news sites and blogs. Some of these communities are “healthy” and foster polite discussion between respectful members, but others are “toxic” and devolve into virulent fights, trolling, cyber-bullying, fraud, or worse even, incitation to suicide, radicalization, or the sexual predation and grooming of minors. Detecting toxic messages and toxic users is a major challenge, in part because they are adversarial users who are actively trying to circumvent or fool detection software and filters. Two Hat has a software product, Community Sift, which assists community moderators in finding toxic messages in online conversations. They are looking for research partners to improve the health of online communities further advancing 5 keys areas outlined below.


  • To explore improvements to the conversation handling tools and toxicity rating metrics within the context of the Community Sift system
  • To research new methodologies for toxicity detection in online conversations
  • To develop innovative algorithms to aggregate the various pieces of information in evidence files into a coherent evaluation and prediction
  • To develop real-time implementations of these methodologies that can handle the massive data stream of online conversations
  • To study the nature of toxic behaviors, their impacts on users and on online communities, and the mechanisms to curb them

This is a great opportunity for a graduate student or Post-Doctoral Fellow to conduct cutting-edge research in the Rapidly-Growing Tech Hub of the Beautiful Okanagan Valley.

Expertise ou compétences exigées: 

  • Deep learning, machine learning, natural language processing (NLP).
  • Data cleaning, data analysis, feature selection.
  • Training binary and multiclass systems.
  • Exceptional written and oral skills (the intern will have an opportunity to work directly with many notable organisations while conducting research and experimenting).